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. 2024 Oct 31;13(21):3501. doi: 10.3390/foods13213501

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

Marietta Fodor 1,*, Anna Matkovits 1, Eszter Luca Benes 1, Zsuzsa Jókai 1
Editors: Santiago Ruiz-Moyano1, Elísabet Martín-Tornero1
PMCID: PMC11544831  PMID: 39517284

Abstract

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups—including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate—have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

Keywords: NIR, meat, meat product, milk, dairy product, honey, vegetable, fruit, tea, coffee, chocolate

1. Introduction

Preserving and monitoring food quality is an increasingly important part of a healthy diet. In addition, the issue of climate change is becoming more and more prominent. As a result of climate change, the stability of global food systems, food security, and diet quality are decreasing. Climate change affects, among other things, changes in soil fertility and yield, the composition of food, the bioavailability of nutrients, and resistance to pests [1]. Many chemicals are used to determine the most basic qualitative characteristics of our food—dry matter, protein, fat, carbohydrates, fibre, etc. The protein content is usually determined by conducting the Kjeldahl destruction process, which is a destruction process with concentrated sulfuric acid at a high temperature (380 °C) [2]. The fat content is determined by using a large amount of organic solvents (petroleum ether, hexane, chloroform, etc.) [3].

The residues of these techniques have a harmful effect on the environment. Although effective, these traditional analytical techniques require energy and are time-consuming.

To overcome these problems, a non-destructive and environmentally friendly chemical analytical method, near-infrared spectroscopy, offers the perfect solution. This is a secondary analytical technique which is based on mathematical relationships between the reference data and spectral results obtained by chemometric methods.

The technical advances in NIR instruments and the proliferation of chemometric computer software have made the technique one of the most used methods in the analytical toolbox. This is confirmed by the number of papers on the subject published over the past 20 years (Figure 1).

Figure 1.

Figure 1

Publications on the topic from 2005 to 2024 (based on Scopus).

In this review work, the focus is exclusively on NIR spectroscopy techniques (NIRS). Other imaging techniques, such as hyperspectral or mid-infrared spectroscopy, are not discussed in this paper.

The basic principles of NIRS and the explanation of different chemometric methods are only partially described in this manuscript, given the vast literature available on these two topics. For a more detailed overview, attention is drawn to some previous summary works [4,5,6].

A rapid analysis and, after knowing the results, a quick intervention—such as those which goes into technical processes—are crucial during food quality control.

Conventional analytical techniques are unable to accomplish this. A protracted sample preparation and a measurement phase are features of both traditional and instrumental techniques. Traditional methods necessitate the operation of quality control laboratories, which call for skilled workers.

On the other hand, the NIR method can be applied offline, online, at-line, and in-line. In addition to not requiring the use of chemicals or sample preparation, it also operates without the need for skilled labour, which is crucial. When NIR sensors are positioned correctly in the technological process, we may quickly learn about the sample’s usual characteristics.

The non-destructive technology uses a lot less energy than conventional analytical methods.

The NIR method is not an absolute method, as its measurement accuracy depends on the accuracy of the reference method used.

Nevertheless, it can be stated that this fast, non-destructive technique plays an increasing role in the quantitative determination of key parameters of foods. Chemometric methods, which are developing more and more, offer the possibility to identify the origin based on the spectra, to determine the maturity status, and to detect possible adulteration.

2. Basics of NIR Spectroscopy

The electromagnetic radiation range of 12,500–3800 cm−1 (800–2500 nm) is the near-infrared radiation (NIR) region.

The energy in this range is no longer high enough to excite electron transitions, so only rotational and vibrational transitions can be detected. However, its energy is too high to detect these stretching and deformation vibrations (normal vibrations) clearly, so combinations and overtones of these appear in the NIR spectrum (Figure 2).

Figure 2.

Figure 2

Excitation of vibration modes.

Infra-active molecules and molecular groups can be studied in this range, which change their dipole moment in response to electromagnetic radiation.

The recorded NIR spectrum consists of overtones and combination vibrations of molecules that contain CH, NH or OH groups (Figure 3).

Figure 3.

Figure 3

NIR band assignment [7].

Therefore, NIR spectroscopy is suitable for the analysis of organic substances in food, agriculture, feed, chemical, and pharmaceutical products.

Figure 4 provides an overview of the NIR technique, including its optics, detection methods, spectrum recording options, light source, and sample type.

Figure 4.

Figure 4

An overview of the NIR technique.

Focus should be placed on the spectra’s acquisition method (Figure 5).

Figure 5.

Figure 5

Measurement possibilities in NIR spectroscopy.

Solid samples can be examined using the diffuse reflection method (PbS detector). Since the photon penetrates only a few millimetres deep into the sample in this instance, the layer thickness of the sample has no effect on the spectrum image. Although, in this instance, the particle dispersion needs to be carefully considered. A detrimental scattering phenomenon may result from an excessively diverse particle dispersion.

The transmission technique can be applied to liquids (InGaS detector) or to colloidal samples (Si diode). The homogeneity of the samples is crucial when dealing with liquids. Otherwise, harmful scattering phenomena may occur. Depending on the sample, the ideal layer thickness (optical path length) can be between 0.5 and 2 mm.

When examining colloidal samples, signal loss may occur due to inadequate layer thickness. If the layer thickness is too big, the infrared photon is absorbed and does not pass through the sample, while if the layer thickness is too small, the signal of the sample is detected, and, accordingly, we obtain a spectrum that is too noisy.

In food analyses, colloidal samples with questionable homogeneity are common. To provide an “average″ image, the spectra are obtained in this instance while the samples are rotating.

An insufficient spectrum is a common issue that arises when the transmission process is recorded. The transflection treatment can be conducted to get rid of this. It combines diffuse reflection and transmission. When measuring “problematic″ colloids, it is preferred.

A special technique is the attenuated total reflectance (ATR) phenomenon, which is also known to be utilized in the NIR range but is typically used in the mid-infrared (MIR) range.

It may be appropriate to obtain a summary of the most up-to-date infrared detection possibilities from Saleem et al.’s [8] summary study.

Infrared detectors that are currently in use are based on traditional inorganic semiconductors like Si, Ge, and InGaAs.

The need for cutting-edge imaging technologies is growing in other industrial applications, including virtual reality, driverless cars, and healthcare. Consequently, processed semiconductor photodetectors have already surfaced, allowing for the creation of numerous excitations and a tunable spectrum response.

Current studies deal with solution-processed infrared detectors and imaging devices based on colloidal quantum dots, perovskites, organic compounds and 2D materials.

Mobile near-infrared sensing is becoming an increasingly important method in many research and industrial fields. Jiang et al. provides a detailed overview of mobile near-infrared sensing prototypes, data ignition techniques, machine learning methods, and relevant application areas [9].

3. NIR Data Evaluation, Chemometric Methods

Evaluating the NIR spectrum is challenging because combinations and overtones of the chemical and deformation vibrations of the infrared bonding groups appear in the spectra, so the peaks cannot be assigned to a specific compound.

The first step in the evaluation is to apply various data pre-processing techniques, such as “cleaning″ the spectra from various noises, separating overlapping peaks, etc.

A multiplicative scatter correction (MSC) is the most used scatter correction method that removes both additive and multiplicative effects in diffuse reflectance spectroscopy [10,11]. MSC is a model-based method in which all spectra are corrected by the average spectrum for the dataset. It works primarily in cases where spectral variations are due to scattering. A widely used variance correction method is standard normal variate (SNV) [11,12]. This method centres the spectral data, line by line (sample by sample), correcting for baseline shifts and then scales. This reduces variations due to differences in optical path length. Baseline deviations can also be corrected by straight line subtraction (SLS), where the algorithm fits a straight line to the spectrum and then subtracts these values from the original spectrum. Various other derivation or smoothing methods, such as the Savitzky–Golay algorithm [13], can also be used. Derivation methods are used both to improve the resolution and to correct the baseline for NIR spectra. By resolving overlapping absorption bands, the accuracy of the quantitative estimate can also be improved. For FT-NIR spectroscopy, the first derivative (FD) and the second derivative (SD) spectra are the most used ones, but it should be noted that the noise increases with the derivative. In addition to the individual data processing methods, a combination of them can improve the performance of mathematical models, e.g., FD + SNV, and SD + SNV.

Various chemometric techniques are used for qualitative or quantitative assessment, such as the principal component analysis (PCA), polar qualification system (PQS) [14], cluster analysis (CA), and partial least squares regression (PLSR).

The NIR spectroscopy is most used for the quantitative estimation of various constituents based on a calibration model built from reference data and spectral data. Different, essentially linear, regression methods can be used for this purpose, given that NIR spectroscopy measurements are usually based on the Lambert-Beer law, which assumes a linear relationship between absorbance and concentration. The most used linear algorithms are: PLSR, PCR (principal component regression), and MLR (multiple linear regression).

Since the number of explanatory variables (spectral data) is significantly larger than the sample size, traditional linear regression methods are not applicable, and PLSR has become most widespread [15].

The analysis of quality attributes (e.g., origin, type of product, identification of origin, adulteration, type of plant, etc.) is usually performed using classification methods, allowing the samples to be classified into classes. Non-linear models [16], such as artificial neural networks (ANNs), AdaBoost, local algorithm (LA) or support vector machines (SVMs), are commonly used to solve classification problems (Figure 6).

Figure 6.

Figure 6

Multivariate data analysis methods.

The classification model’s performance was assessed using standard metrics such as sensitivity, specificity, precision, and accuracy. These metrics were calculated from the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN), employing the Equations (1)–(4) [17,18]:

Sensitivity=TPTp+FN (1)
Specificity=TNTN+FP (2)
Precision=TPTp+FP (3)
Accuracy=TP+TNTP+TN+FP+FN (4)

Since each material has different spectral properties (fingerprint-like pattern), a separate model must be developed for each sample matrices. The data can be analyzed using many different methods, but the main steps of model building are the same (Figure 7): sample selection, spectral recording, reference data determination, data pre-processing, calibration, and model validation.

Figure 7.

Figure 7

Main steps of model building [19]. 🢣 calibration; 🢣 validation.

Among the multivariate regression procedures, parameters indicating the performance of the most commonly used PLS regression procedure are summarized in Table 1.

Table 1.

Characteristic qualifying parameters of PLS regression.

Parameters Calibration Validation Aim
Notation
Square of the determination coefficient R2 Q2 The value of Q2 is pertinent for the correlation rating, ideally as close to 1 as possible.
Mean squared error RMSEC RMSECV; RMSEP The goal is to attain the smallest value possible.
PLS principal component 3–10 3–10 The value is highly dependent on the number of samples. Generally, for approximately 100 samples, a cut-off range of 3–10 is advised. Below 3, the function tends to be underfitted, while above 10, it tends to be overfitted.
RPD—
Ratio of Performance to Deviation
(1 − R2)−0.5 (1 − Q2)−0.5 If greater than 3, the function is appropriate for quantitative assessment. The calculated value is not independent of Q2.
bias <0.1·RMSECV;
<0.1·RMSEP
The goal is to be at least an order of magnitude smaller than the average validation error.

The root mean squared error (RMSECV for cross-validation; RMSEP for test-validation) is calculated according to the following relation:

RMSECV or RMSEP=1Ni=1N(yiy^i)2 (5)

RMSECV or RMSEP: root mean square error of cross-validation or test validation (the unit of measurement is the same as that of the estimated parameter)

yi: measured (reference) value of the i-th component

y^i: estimated value of the i-th component

N: number of samples tested

The minimum–maximum number of main components of PLS is not regulated, it basically depends on the number of samples. In most cases, the minimum value is set at three and the maximum value is set at ten to avoid under- or over-fitting.

Ratio of Performance to Deviation (RPD) is calculated according to the following relation [20]:

RPD=SdSEP (6)

where Sd is the standard deviation of the samples

Sd=1N1i=1Nyiy¯2 (7)

y¯: the average of the measured (reference) values

SEP is defined as the standard error of prediction:

SEP=i=1Ny^iyibias2N1 (8)
bias=i=1Ny^iyiN (9)

NIRS is a fast and efficient analytical tool in the food industry. As an advanced chemometric tool, multipath analysis has great potential for solving a wide range of food problems and analyzing complex spectroscopic data. The development, advantages, and limitations of the multipath models used to analyze NIRS data and the various multipath models are summarized in Yu et al. [21].

4. Limitations of NIR Spectroscopy

The limits of NIRS include its low sensitivity due to low absorption coefficients, which causes the detection limit to be higher. NIRS is an indirect method that requires the development of a multivariate calibration model against a suitable reference method. Therefore, the accuracy of the NIR data depends on the precision of the reference measurements and shouldn’t be higher than that. However, the accuracy detection of reference data does not clearly mean that the parameter examined can be determined by NIR spectroscopy. The technique has a concentration limit. The parameter being examined, the matrix’s complexity, the reference’s sensitivity, and the NIR technology being employed all affect this limit. The detection limits for more complicated matrices (like food samples) are roughly 1000 mg/L (0.1%). For less complex matrices (e.g., milk, energy drink), this detection limit can also reach 50–100 mg/kg (ppm) [22].

In NIR spectra, the absorption bands come from combinations of overtones and/or normal vibration movements. They are wider and much less intense than basic absorption bands. Various data management procedures can reduce the signs caused by noise and separate overlapping peaks.

Temperature variations play a crucial role in developing predictive models with NIRS. They alter the location and intensity of the NIR spectral absorption bands, impacting the calibration models’ predictive accuracy. This issue can be addressed by employing local and global temperature compensation techniques. Local models tend to be vulnerable to temperature shifts, whereas a global model, which utilizes sample spectra across the full temperature spectrum, demonstrates robust predictive performance [23].

Measuring the moisture content of samples is a common task in food analysis. However, the moisture content in samples can pose challenges, particularly when assessing their protein and sugar content. For solid samples, methods like lyophilization or drying are suitable for addressing this issue. For liquid or colloidal samples, it is advisable to use a transflection spectrum rather than the conventional transmission spectrum [24].

NIR spectroscopy requires no or minimal sample preparation. This primarily means homogenization of fluid and colloid patterns. Diffuse reflection (DRIFTS, Diffuse Reflectance Infrared Fourier Transform Spectroscopy) is used to analyze powders and other solid matters. The collection optics in the DRIFTS accessory are designed to exclude spectral reflected radiation and collect the diffuse reflected light as much as possible [25].

About the challenges of nearly infrared spectroscopic measurements, Hong et al. published a detailed review [26].

5. Applications of NIRS for Quality Assurance

5.1. Bakery Products, Pastas, Biscuits, and Snacks

The application of near-infrared (NIR) technology is not yet common in the baking industry, unlike in the milling sector, where NIR technology is used to monitor raw materials, processes, and products [27].

5.1.1. Bakery Products

Previous articles have mainly focused on nutritional analyses of bakery products, so the results are mainly related to the determination of protein, fat, and sugar content.

Scientific literature primarily focuses on the nutritional analysis of baked goods made from various flours, such as wheat, rice, buckwheat, and corn. The analyses typically estimate the content of protein, fats, sugars, dietary fibre, ash, monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and sodium. The total carbohydrate and energy content can be derived from NIR data [28,29,30].

Reference data from gas chromatography-flame ionization detection (GC-FID) are used for the NIR method to determine the ethanol content in packaged whole-grain bread [31].

In the baking industry, controlling the fermentation state of bread is crucial. The inline application of technology based on PLS-DA evaluation of NIR spectra offers a way to monitor the fermentation state during the production process, allowing for the filtering of potential defects before baking [32].

Edible coatings, such as those with probiotic, antimicrobial, or antioxidant properties, can be utilized to prolong the shelf life of products. The drying of the coating is a critical phase in this process. The spectra obtained from monitoring the drying process provide a detailed description, enabling the clear differentiation of various coatings and drying durations [33].

Two-dimensional correlation spectroscopy (2D-COS) was utilized to explore the processes of deterioration. The key structural factors in bread rancidity include the crystallization of amylopectin within the starch and the loss of water content through evaporation and diffusion from the core to the crust. Two-dimensional-COS enabled the distinction of the detailed sequence of structural events over the investigated time intervals: crystallization of amylopectin, evaporation of weakly and strongly hydrogen-bonded water, and reorganization of starch’s OH functions [34].

NIR and the electronic nose provide an ideal solution for assessing the volatility and texture of the dough, thereby testing the quality of sourdough bread [35].

The adulteration of fats also presents a challenge in the baking industry. A 1:1 adulteration model was created using commercially available margarine and butter samples. The act of adulteration was confirmed by PCA of Raman and NIR spectra, proving successful not only in the fat examination but also in the analysis of baked goods produced with them [36].

Foreign food contaminants, such as metallic iron, polypropylene plastic, and hair fibres, were detected in bread samples using NIR and computer vision (CV). The evaluations achieved an accuracy of over 92% using a discriminant analysis paired with Savitzky–Golay smoothing [37].

Table 2 presents a summary of the data pre-processing and chemometric methods employed in the research.

5.1.2. Pastas, Biscuits, and Snacks

Although dry pasta is traditionally not considered to be a bakery product, it does fit neatly into any other food category, hence it is discussed here.

Following extrusion, the dough’s optimal moisture content was achieved through controlled drying, with the process monitored by NIR reflectance spectroscopy [38,39].

The NIR technique was also used to determine the nutritional value (energy, protein, fat, carbohydrate, sugar, and fibre) of dry pasta. A PLS regression was used in data processing to determine the correlation between reference and spectral data [40]. Nutritional analyses were performed by Cayuela-Sánchez et al. [41], and in addition to those already mentioned, the parameters studied were extended to determine of saturated fatty acids (SFA), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs). Spectra were recorded from both intact and ground samples, and reference data were also determined for both conditions.

For egg-based dry pasta, egg content is an important qualifying parameter, and its determination is therefore a key issue.

Traditional methods often recommend spectrophotometry, specifically the Lieberman-Burchard reaction. Chromatographic techniques like gas chromatography with flame ionization detection (GC-FID) or mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC) are also prevalent in food analysis. The Lieberman-Burchard reaction has a drawback: it measures sterol concentration without distinguishing cholesterol, which can be problematic for pasta with minimal egg content, such as two eggs, where the flour’s phytosterol content may significantly alter the results. Additionally, this method is a time-reaction, and its reproducibility is debatable. Chromatographic methods require extensive sample preparation, making them impractical for routine dry pasta testing. Addressing this issue, Fodor et al. [42] introduced a NIR method based on calculations. By considering the fat content of pasta ingredients like wheat and durum flour, and lyophilized eggs, they calculated reference values through a theoretical model and then achieved a successful correlation using a PLS regression. Bevilacqua et al. [43] utilized their samples with a known egg content and observed that the spectral profile was affected by the production process, especially the drying temperature and duration. They employed a multivariate data analysis technique (ASCA), which is based on the ANOVA concept, in conjunction with locally weighted PLS regression (LWR-PLS). This non-linear approach yielded a stronger correlation than the conventional PLS regression. Adulteration poses a problem in the case of pasta products as well. The most frequent form of fraud is the substitution of pure durum flour with a mix of durum and wheat flours. To detect this fraud, De Girolamo et al. [44,45,46] effectively used the FT-NIR method alongside various chemometric techniques, such as PLS-DA and LDA. The duration of heat treatment, and temperature of fresh unfilled egg pasta (tagliatelle, fettuccine, and tagliolini) were examined. The experiment demonstrated that an NIR analysis can be effectively used for the rapid monitoring of thermal processing parameters [47].

Xanthine (caffeine, theobromine, and theophylline) and polyphenols (catechins and epicatechins) are primarily responsible for the bitter taste of baked goods containing coffee, cocoa or chocolate. For the Fourier transform near-infrared (FT-NIR) spectroscopic method, the reference measurements were performed using liquid chromatography LC-ESI/mass spectrometry MS-MS method. This method can be directly applied to solid products and may extend to other flavour molecular markers like sugars, potentially for routine monitoring of standardized bitter taste quality in actual industrial production [48,49].

In assessing the physicochemical characteristics of fresh egg pasta made by extrusion and lamination, it became evident that these two techniques yield pasta with distinct properties, particularly in colour and starch gelatinization. Although, no notable difference was observed in water absorption during cooking. FT-NIR spectral classification procedures effectively differentiated between the two types of pasta [50].

The physicochemical attributes of fresh pasta, such as water activity, colour, water absorption index, and hardness, are crucial determinants of its stability, quality, and consumer appeal. FT-NIR analysis tracked the structural changes in dough stored under various temperatures and durations. These changes, linked to the interactions between water, starch, and proteins, were significantly influenced by storage temperature, impacting the dough’s physicochemical properties, like hardness [51].

In biscuit production, kneading and rolling are vital. The NIR technique, paired with the novel soft multiclass compatible classification method (PLS2-CM), effectively pinpointed defective products during these stages. During kneading, the method could distinguish well-kneaded dough from defective ones.

Although a reliable classification model for determining excess water was not achieved, the same doughs were modelled after fermentation and during rolling with complete sensitivity and precision (100%). This indicates that the physicochemical changes that occur during fermentation are critical in determining the absence of defects in kneaded biscuit doughs using NIR spectroscopy [52].

Foreign food contaminants, such as metallic iron, polypropylene plastic, and hair fibres, were detected in bread samples using NIR and computer vision (CV). The evaluations achieved an accuracy of over 92% using discriminant analysis paired with Savitzky–Golay smoothing [53]. The research results related to meat and meat products are summarized in Table 2.

The concept of snacks is rather complex, as it refers to sweet and salty snacks that are not eaten as a main meal. In the case of salty snacks, in addition to the fat and salt content of the macro components [54,55,56,57], an important issue is the determination of the acrylamide content [57,58], which is highly dangerous from a physiological point of view.

Several classification models have been developed for the technological process, the raw materials and the country of origin of the finished product [59].

Table 2.

NIR test results for bakery products, pastries, dough, biscuits, cake, snacks.

Sample Investigated Parameter Concentration Range Chemometrics Data References
Pre-Treatment, Regression R2 Root Mean Square Error
Bread Moisture, % 49.05–53.85 MLR 0.92 0.46 [30]
PCR 0.85 0.61
PLS 0.88 0.55
n.i. PLS top 0.963
bottom 0.937
2.49; 2.87
3.08; 3.15
[33]
Protein, % 5.3–11.7 SNV, DT, 1st der. PLS; MH > 3.5 0.99 0.14; 0.17 [28]
10.8–16.2 1st der. PLS; MH > 3.0 0.989 0.16 [29]
1.93–8.89 MLR 0.99 0.29 [30]
PCR 0.97 0.46
PLS 0.99 0.29
Fat, % 1.2–13.5 SNV, DT, 1st der. PLS; MH > 3.5 0.99 0.27; 0.33 [28]
1.2–31.1 SNV, PLS; MH > 3.0 0.99 0.79 [29]
Dietary fibre, % 2.8–9.4 SNV, DT, 1st der., PLS; MH > 3.5 0.89 0.60; 0.55 [28]
Sugar, % 2.1–8.5 0.96 0.43; 0.54
0.9–10.9 MSC, PLS; MH > 3.0 0.988 0.28 [29]
Ash, % 1.1–2.6 SNV, DT, 1st der., PLS; MH > 3.5 0.91 0.1; 0.15 [28]
SFA, % 0.1–3.0 0.90 0.15; 0.16
MUFA, % 0.2–2.9 0.91 0.23; 0.25
PUFA, % 0.22–6.1 0.92 0.22; 0.31
Total carbohydrate, % 28.7–51.8 0.98 1.1; 1.17
Energy; kJ/100 g 738–1421 0.99 21; 19
Total carbohydrate - Calculated from NIR predicted data n.i. 0.75; 0.91
Energy; kJ/100 g - n.i. 14; 14
Ethanol, % 0.0–3.45 MSC, DA, MLR classification 100% [31]
Fermentation point PLS-DA, ROC sensitivity 86–88% [32]
Staling n.i. EMSC, MCR-ALS explained variance 99.9994,
sum of squared residuals 0.75776
[34]
Contaminants SGS, PCA, DA accuracy: 92–95% [37]
Pastry Moisture 31.4–74.4 MSC, 1st der., PLS 0.956 2.4 [38]
7.37–31.42 PLS 0.994 3.32; 3.41 [39]
Egg content (pieces) 0.5–9.1 pieces MSC, 1st der., PLS 0.907 0.6; 0.7 [42]
n.i. ASCA, LWR-PLS n.i. 1.01; 1.25 [43]
Authentication durum wheat, mix wheat PCA, PC-LDA, SVMc, PLS-DA sensitivity 95%,
sensitivity 95%,
specificity and accuracy 94%
[44,45,46]
Thermal treatments 1.78–3.31 2nd der., PLS 0.781 0.183 [47]
Extrusion or lamination - PCA Accuracy 100% [50]
Storage time, days, temp., °C Time: 0–75 d
Temp. 0; 5; 10
PLS 0.968 (0 °C)
0.974 (5 °C)
0.968 (10 °C)
4.5 (0 °C))
4.1 (5 °C)
4.4 (10 °C)
[51]
Pastry doughs Kneading or rolling SNV, 1st der., PLS2-CM sensitivity and specificity 100% [52]
Biscuits, cake Protein, % 5.3–12.2 RS, OLS, PLS, DA, kNN, NB 0.941 0.385 [53]
Lipid, % 0.8–25.0 MSC, OLS, PLS, DA, kNN, NB, PLS-DA, PLS-kNN, PLS-NB 0.992 0.56
Fatty acid, % 0.2–17.0 RS, OLS, PLS, DA, kNN, NB 0.988 0.39
Carbohydrate, % 42.7–87.0 0.965 1.46
Fibre, % 0–21.6 0.906 0.72
Energy, kJ/100 g 1544–2135 0.986 25.1
Salt, % 0–2.8 SNV 0.9 0.182
Main cereals five kinds PLS-kNN classification 100% [36]
Cooke type 14 kinds PLS-kNN classification 100%
Adulteration-fat n.i. SVD, PCA classification 100%
Xanthines, mg/kg 1–1600 1st der., PLS 0.96 <10% [48,49]
Polyphenols, mg/kg 0–83 0.96 <10%
Bitter taste <4–8 PAA n.i. n.i. [48]
Snack Cereal base and sucrose coated, % [54]
Sucrose 1.23–25.73 SGS, DT, PLS 0.97 1.47
Glucose 1.04–5.06 SGS, DT, PLS 0.95 0.36
Fructose 1.53–3.86 SGS, DT, PLS 0.59 0.2
fat, % 2.2–45.1 SNV, PLS 0.98 1.1 [55]
carbohydrates, % 45.1–69.7 SNV, 1st der., PLS 0.92 1.9
sugar, % 1.7–8.6 g/100 1st der., PLS 0.93 0.47
protein, % 3.0–40.1 MSC, 1st der., PLS 0.98 0.93
salt, % 0.7–2.5 g/100 SNV, 1st der., PLS 0.91 0.16
energy, kJ/kg 1264.3- SNV, PLS 0.87 92.03
Classification Frying oil
Raw material
Production technology
Origin
PRPropMLP Accuracy
83%
98%
91%
90%
[59]
Chips—potato Fat, % 1.2–4 MSC, PLS 0.98 1.21 [56]
26.7–49.3 SNV, PLS 0.99 0.99 [57]
Moisture, % 18–45 MSC, PLS 0.99 0.82 [56]
Dry matter, % 82.9–98.6 SNV, PLS 0.97 0.84 [57]
Acrylamide, mg/kg 40–1770 SNV, PLS 0.83 266 [57]
μg/kg 56.7–789.7 CARS-PLS 0.71 61.1615 [58]

5.2. Meat and Meat Products

Meat is one of the most important foods because of its nutritional properties. It is mostly composed of water (~73%), amino acids (~23%), and fatty acids (~1.8%), and additionally it contains cholesterol, phospholipids, minerals, and vitamins [60,61].

As people’s standard of living continues to improve and the supply of meat on the market becomes more abundant, expectations for meat quality have also risen. Consumers are increasingly concerned not only with the nutritional value but also with the taste, texture, and appearance of meat, as well as factors like convenience, health, and safety [62]. The development of rapid, environmentally friendly, and non-invasive methods for predicting, certifying, and authenticating meat quality has become a priority in recent years.

In this context, near-infrared (NIR) techniques are most commonly used for meat analysis [63,64]. MSC and SVN are mostly used for data pre-processing, and it is equally important to test the derivatives, e.g., the 1st and the 2nd ones [65]. Furthermore, in data management, the first derivative is recommended for homogeneous products, while the second derivative is preferred for heterogeneous products, as it reduces scattering effects caused by variations in grain size [66].

As a result, the prompt identification of meat quality is essential not only in the laboratory but also in industrial settings, where it is used to monitor technological processes, reduce losses, and increase exports. A key example of this is the study conducted by Isaksson et al. [67], in which the quality parameters of minced meat—such as fat, water, and protein content—were determined under industrial conditions.

Building on this, in recent years, numerous studies have focused on the industrial testing of meat, with a growing emphasis on the utilization of specialized portable equipment to facilitate monitoring [60].

It is important to recognize that the actors in the food supply chain have different priorities and, therefore, different assessments of quality. Important factors include shape, flavour, freshness, and health safety. They pay attention to the appearance of meat, particularly colour and fat content, as these influence their perception of freshness and meat quality, although this may vary regionally.

Technological properties such as water retention, colour, and pH are important meat quality indicators that correlate with consumers’ sensory evaluation.

For example, a dry, dark, firm texture indicates DFD meat, while pale, soft, and flaky meat is referred to as PSE in the literature. The occurrence of these meat defects poses a significant issue for the meat industry due to their unappealing nature to customers and poor processing characteristics, such as lower yield and high spoilage potential, compared to normal meat [68].

5.2.1. Beef Meat

A model was built by Tejerina et al. [69] for beef samples to predict some of the DFD meat parameters, such as colour (L*, a*, and b*), which offers a good opportunity for internal quality control in slaughterhouses. Samuel et al. (2011) [70] found that the Vis-NIR range was superior to the NIR range, as the Vis-NIR region of the spectrum contained abundant information about muscle pigments [71].

The moisture, fat, and protein content of bovine meat was determined by Dias et al. [72] using NIRS.

In the case of beef, the quality of the meat was found to be largely dependent on intramuscular connective tissue (IMCT) components. The measurement of muscle and IMCT components were identified as important for quality determination and prediction. In a related study, Andueza et al. developed a NIRS method to predict IMCT components from fresh and lyophilized samples while investigating whether the accuracy of the model varies for meat from different body regions. The efficiency and reliability of the NIRS models were found to depend on the variability of reference values. Additionally, the meat was characterized by a high water content (75%), which can interfere with the absorption of other components in the NIR spectrum and, thus, affect the results of NIRS predictions [73].

Their study investigated Vis/NIRS models for FA prediction in fresh and lyophilized beef samples. No significant difference in performance between models for 16:0, 18:0, 18:1 n-9, 18:2 n-6, 20:4 n-6, 22:5 n-3, 22:6 n-3, saturated, monounsaturated FA, and total n-3 long chain PUFAs was found, but the standard error of total PUFAs, total n-3 PUFAs, total conjugated linoleic acid, 20:5 n-3, and 18:3 n-3, improved by an average of 21% in lyophilized samples [61]

Steer meat samples were examined, and a NIRS model was built to predict ether extract, among other parameters. An excellent result was obtained (R² = 0.92; RPD = 3.32), and it was found that ether extract and gross energy results are correlated, with better predictability of results achieved when MSC is applied to raw spectra. This improved predictability may be attributed to the difference in the refractive index of samples with varying ether extract contents [74].

5.2.2. Pork Meat

The ability of NIRS to predict pork meat quality characteristics of early post-mortem samples was investigated, but it was found that no correlation was achieved with the PLS method. Although, promising results were obtained in predicting IMF (intramuscular fat) content [75].

Balage et al. [76] used NIR spectroscopy to predict meat pH, colour, IMF, and shear force (WBSF) to build classification models that can categorize meat based on tenderness and juiciness. They found that their PLSR- and Vis/NIRS-based models were inaccurate for IMF and WBSF, respectively, and needed further improvement.

An NIRS method for fat characterization of live and slaughtered pigs was developed by Pérez-Marín et al. [77]. The spectra were collected in five analysis modes: live animal, carcass from slaughterhouse, subcutaneous fat sample, subcutaneous fat sample without skin, and transverse section. Calibrations were developed to predict the four main fatty acids (FA) (palmitic acid, stearic acid, oleic acid, and linoleic acid) in Iberian pig fat. The NIRS system that was developed allows for the analysis of live pigs and carcasses to predict fatty acid profiles without interrupting the processing system.

Savenije et al. [78] studied three different breeds of pigs, and the accuracy and robustness of the calibration on independent samples were validated. Drip loss, colour value, pH, and IMF were investigated in chops. It was found that the breed of pig did not influence the accuracy of the calibration, and IMF was determined with good accuracy.

The nutritional value of meat is related to its composition of AA, FA, minerals, and vitamins. Although, excessive consumption of meat, especially red meat, can lead to diseases such as hypertension. Most of these diseases are related to the FA composition of meat, so awareness of this would be of paramount importance from a consumer perspective. FA is determined by GC as a standard. Several studies on the determination/prediction of FA composition using NIRS technology have been reported in beef [79,80,81], pork [82,83,84,85,86], sheep [87,88], chicken [89], and rabbit [90]. When predicting small FAs, PUFAs are difficult from beef because the strong absorption effect of water in the IR range affects the detection of the component found in small amounts.

Cheng et al. used NIR-HSI (1000–2200 nm) in combination with chemometrics to predict the degree of lipid oxidation in pork (TBARS) during frozen storage. An interesting phenomenon they discovered was that good results in predicting TBARS value also showed that the chemical modification of pork during frozen storage was highly significantly correlated with the size and distribution of ice crystals [91].

To improve predictions, researchers are trying several models. For instance, Vasconcelos et al. [92] found that the SVMR-Poly predictive model cannot predict with high accuracy the aw, moisture, ash, fat, protein, pigments, collagen, WHC (water holding capacity), RT (raw texture), and CT (cooked texture) analyzed by NIR.

Besides this, the use of multi-techniques integrating NIRS, Computer Vision (CV), and Electronic Nose (EN) to significantly enhance the prediction performance has also been explored, particularly for TVB-N content in pork. The TVB-N content of meat serves as an important reference for evaluating its freshness alongside organoleptic qualifications and chemical parameters. In this study, NIRS, CV, and EN were combined to determine TVB-N, while BP-ANN was employed for the prediction model [93].

The ability to predict the protein, fat, and moisture content of meat samples by NIR spectroscopy was discussed in previous reviews. Visible/near-infrared (Vis/NIR) spectroscopy for online prediction of fresh pork meat quality characteristics (IMF, protein, and water content, pH, and shear force value) was tested. It was found that the 1st derivative for the quality parameters they investigated eliminated the negative effect of translation errors, independent of the wavelength of the reflectance spectra caused by varying slice thicknesses, and when combined with MSC, this derivative gave the best calibration results [94].

Barbin et al. took hyperspectral images of whole and minced meat, determined protein, moisture, and fat content using classical methods, and then combined the spectral information with PLS. The results showed that PLS regression models developed from wavelengths associated with characteristics from ground samples predicted protein, moisture, and fat with reasonable accuracy, with a coefficient of determination R2P > 0.88 [95].

5.2.3. Lamb Meat

Additionally, the potential for predicting the organoleptic properties of lamb meat using the Vis/NIR technique was investigated. Samples were scored by sensory judges on a taste panel, with 25 extreme cases—best and worst—being selected. It was shown that NIRS could effectively discriminate samples with extreme sensory properties. The range between 890 and 1000 nm was identified as particularly useful for this, as it was found to significantly correlate with the water and IMF content of the meat samples [96]. Protein, being a key functional and nutritional component of meat and meat products, has been the focus of numerous studies involving the development of predictive NIR models. However, comparisons of the reported errors in protein measurements are often challenging, as these errors are expressed either as a percentage of fresh or dry matter and are determined through cross-validation or separate validation sample sets. In certain instances, only calibration errors are reported, which further complicates the evaluation of the model’s predictive accuracy.

5.2.4. Poultry Meat

Marchi et al. examined whole chicken breasts 48 h after slaughter, aiming to explore the capability of NIR technology in estimating the physical and chromatic characteristics of chicken meat. This was achieved by directly applying a fibre-optic probe to the breast muscle. Their research revealed that the prediction of a CIE index was closely related to absorption at wavelengths between 1230 and 1400 nm. Furthermore, the prediction of the a* value, which is influenced by meat water content and myoglobin concentration, was effectively linked to the visible NIR regions [97].

Viljoen et al. developed a NIRS method for predicting the chemical composition of freeze-dried lamb meat [98]. For this purpose, samples were scanned at wavelengths ranging from 1100 to 2500 nm. It was found that the freeze-dried samples provided more accurate calibrations than previously published research results, likely due to the homogeneous nature of the samples and the absence of moisture. Although, it was emphasized that changes in temperature also affect the chemical composition of the samples. The model developed was deemed suitable for the determination of K, P, Na, Mg, Fe, and Zn minerals [99]. Additionally, Dixit et al. [100] developed a method to predict the IMF content of lyophilized ground lamb.

Research confirmed that NIR can be successfully used to estimate the chemical composition of fresh and lyophilized minced meat. In addition to chemical composition, they were also able to distinguish the AA (amino acid) profile depending on the genetic group. The most important amino acids used to distinguish the genetic groups were alanine, aspartic acid, and methionine [101].

5.2.5. Adulteration and Classification

The issue of meat authenticity concerns not only consumers but also producers and distributors. Meat adulteration can cause harm not only to human health but can also raise religious concerns, as in some countries pork is considered an unclean animal. To protect consumers and prevent unfair competition in the meat trade, fast and reliable methods must be applied to detect adulteration [102].

Kuswandi et al. [103] developed a method for detecting adulteration in beef meatballs with pork using NIR spectra coupled with chemometric techniques (PLS and LDA). A quantitative prediction of pork adulteration in beef meatballs can be achieved using the PLS model built on first derivative spectra. Meanwhile, a classification of clean and pork-adulterated beef meatballs can be performed using the LDA model.

Schmutzler et al. [104] developed a method for detecting adulteration in pork meat. In developing this method, adulterations between 10 and 51% were analyzed. Principal component analyses (PCA) were designed for each setting using pre-processing steps of the data, including wavelength selection, variance corrections and spectral data derivation. PCA scores were used as input data for classification and validation using support vector machines (SVM). Measurements were also performed directly through polymer packing of the samples and compared to measurements through quartz slides. Meat and fat adulteration were detected at contamination levels as low as 10% in both laboratory and industrial fibre optic set-ups, with measurements made through quartz and polymer packaging.

Consumers are placing more and more emphasis on quality-related attributes, such as animal breed, husbandry, feeding, etc. For this reason, there is a need for a method to ensure that foodstuffs are classified in this respect. Clear differences in location, feeding conditions, breed, and soil characteristics may contribute to variations in the organic composition (protein, fat, and carbohydrate) and structure of meat. This information is reflected in the NIR spectra measured at different locations. NIR spectroscopy was used to identify breed and age, in this case, to compare aspects such as colour, fat, protein, and moisture, as well as technological properties, e.g., cooking loss and purge loss [105], in another study, Iberian pig half carcasses were analyzed after slaughter according to three feeding methods using a microelectromechanical system (MEMS) spectrometer. The classification results for Iberian pigs fed with three different feeds were 93.9%, 96.4%, and 60.6% [106].

The classification of lambs from pastoral and agricultural regions was investigated. D-PLS and LDA analyses correctly classified 100% of samples from both pastoral and agricultural regions, with overall correct classification rates of 88.9% and 75% for the five different regional samples [107].

Researchers tried to classify meat according to its origin, and a NIRS method was developed to investigate the origin of chicken meat. The spectra were used to distinguish between fresh and thawed meat and the growing conditions of the chickens (rearing method and feeding) using the RSDE (random subspace discriminant ensemble) method, achieving a classification accuracy of over 95% [108].

In addition, studies have been carried out to classify post-harvest techniques, e.g., storage conditions [109,110].

The possibility for NIR-based discrimination of meats originating from the extensively-reared autochthonous breed of Mangalica and intensively-reared commercial genotypes (Landrace, Large White, Landrace × Large White crossbreed) was investigated. The classification is based on the considerable difference between the intramuscular fat content of Mangalica and intensively-reared meats (average of 19.1 DM% vs. 9.3 DM%, resp.) [111].

5.2.6. Meat Products

Processing plays a major role in NIR analyses of meat and meat products, as researchers have found that meat prepared by mincing is more homogeneous than meat tested whole. The energy absorbed is lower when examining minced meat, thus producing a higher reflectance that is easier to measure [112,113]. The ability of NIR techniques to discriminate pork chop roasting methods based on other methods (roasting and confit) and conditions (temperature and time) was demonstrated by González-Mohino et al. [114].

A NIRS model for the determination of hydroxyproline content in pork sausages and dry-cured beef using a remote reflectance fibre-optic probe was developed by González-Martín et al. [115]. The method allowed for the determination of hydroxyproline in the range of 0–0.74%.

The use of near-infrared spectroscopy (NIR) to predict the drying parameters (aw, moisture, and NaCl) of fermented sausage was evaluated by Collell et al. Both methods demonstrated high predictive accuracy, suitable for online monitoring [116].

The use of NIR spectroscopy combined with chemometric analyses to detect the treatment of dry fermented sausage with ionizing radiation was investigated by Varrá et al. [117]. The irradiation of food products, which can increase shelf life, is allowed up to a maximum dose of 10 kGy according to Directive 1999/3/EC. The study demonstrated the feasibility of simple and rapid detection of dry fermented sausages treated with irradiation doses of 0.5–3 kGy through chemometric analysis combined with NIR spectroscopy. OPLS-DA results showed 100% clear discrimination of the samples by irradiation treatment.

A near-infrared spectroscopy technique was developed to monitor the production process (curing) of an alternative salted ham. In this study, lean cuts of meat were salted on a tray, and the fatty cuts of meat were salted in a tub. During the curing process of lean hams, the accurate determination of moisture and protein parameters was enabled by the developed calibration models, with RPDs of 5.8 and 3.4, respectively, being achieved. For fatty ham, good predictive capacity was archived for protein, water activity, and proteolysis index parameters, with values ranging between 2.5 and 3, while moisture was well predicted with an RPD of 10.4 [118].

Meat products from meat depend on external factors such as rearing, feeding, sanitary and environmental conditions, transport, preslaughter conditions and post-slaughter storage. Internal factors such as genetics, age, slaughter weight, sex and physiological condition also affect quality [92].

Building on this understanding of the factors affecting meat quality, further research has focused on developing more precise models to assess key chemical parameters in meat.

A model for the determination of major chemical parameters of prad-based meat products was developed by Ritthiruangdej et al. [119]. Good results were achieved using a PLS regression calibration model with MSC pretreatment in predicting protein (RPD = 7.6), moisture (RPD = 9.8), and fat content (RPD = 9.5). Although, the determination of residual nitrite content proved to be challenging.

Texture problems can also arise in the production of dry-cured hams. A crust may form on the surface of the ham, reducing the possibility of drying out [120]. The resulting calibration models allow for the monitoring of the resting and drying process, which may be useful in avoiding crust formation [121].

A NIRS method was developed to predict the sodium content of dry-cured ham slices. As reference data, the sodium content of the sample was determined by ICP-AES. PLS regression was used to perform the calibration. The models gave acceptable results with cross-validation correlation coefficients (R2CV) ranging from 86.2 to 90.2%. The prediction capacity achieved in external validation was 3.63 with a standard prediction error of 0.12% Na [122].

The prediction of storage temperature and storage time was investigated. It was found that a handheld NIRS instrument combined with PLS-DA could be used as a suitable tool to discriminate the temperature at which sliced Duroc dry-cured ham was preserved (4 °C vs. 20 °C). In addition, reliable discriminatory models were obtained to predict the storage time of samples (under conventional refrigeration conditions or at room temperature) at 0, 3, and 5 months. These results have practical implications for self-monitoring and logistics [110].

In summary, these advancements in NIR spectroscopy, from predicting protein and moisture content to distinguishing cooking methods and monitoring sodium levels, demonstrate the versatility and growing precision of the technique in meat quality analysis. While challenges remain, such as improving the accuracy for certain parameters and accounting for sample preparation, the continued refinement of calibration models and processing approaches highlights the potential of NIRS as a reliable tool for the meat industry.

The research results related to meat and meat products are summarized in Table 3 and Table 4.

Table 3.

NIR test results for meat.

Sample Investigated Parameter Concentration Range Chemometrics Data References
Pre-Treatment, Regression R2 Root Mean Square Error
Beef—fresh Dry matter, % 21.5–26.8 1st der., SNV, DT, PLS 0.77 0.58 [123]
25.15 ± 1.22 1st der., 2nd der, MPLS 0.92 0.26 [79]
Moisture, % 59.6–72.9 MLR Grinder diameter (4 mm 8 mm)
0.75/0.81
[67]
40.53–80.72 SNV, DT, PLS 0.72 2.18 [72]
Lipid, % 6.2–21.7 MLR. Grinder diameter (4 mm 8 mm)
0.73/0.88
[67]
1.99 ± 1.20 1st der., 2nd der, MPLS 0.99 0.20 [79]
0.08–14.11 1st derivative, SNV, DT, PLS 0.93 1.00 [61]
0.43–46.03 SNV, DT, PLS 0.93 1.25 [72]
Ash, % 0.93–1.2 SNV, DT, PLS 0.66 0.03 [123]
1.00 ± 0.06 1st der., 2nd der, MPLS 0.86 0.09 [79]
Protein, % 18.1–20.7 MLR Grinder diameter (4 mm 8 mm)
0.23/0.27
[67]
18.3–22.6 2nd der., PLS 0.82 0.48 [123]
10.36–23.84 1st der., PLS 0.89 0.99 [72]
22.16 ± 0.47 1st der., 2nd der, MPLS 0.99 0.20 [79]
Collagen, mg/100 g 0.31–1.9 2nd der., PLS 0.18 0.30 [123]
18.43 ± 5.30 1st der., 2nd der, MPLS 0.74 8.52 [79]
Fatty acids 1st derivative, SNV, DT, PLS [61]
Total Fatty acids, % 0.58–16.3 0.90 1.16
C16:0 101–4051 0.86 355
C18:0 89–3086 0.93 202
C18:1 n-9 123–5339 0.90 402
C18:2 n-6 62.0–502 0.70 57.0
C18:3 n-3 6.35–128 0.51 19.8
C20:4 n-6 11.9–115 0.49 14.6
C20:5 n-3 0.00–39.0 0.10 7.61
C22:5 n-3 0.00–86.8 0.11 15.1
C22:6 n-3s 0.00–11.3 0.16 2.03
Beef—fresh Total SFAs 216–8116 1st derivative, SNV, DT, PLS 0.90 14.2 [61]
Total MUFAs 185–7019 0.90 45.6
Total cis-MUFAs 163–6526 0.90 560
Total trans-MUFAs 8.4–545.6 0.78 514
Total CLAs 1.9–114 0.67 490
Total n-3 PUFAs 10.4–264 0.28 52.8
Total n-3 LC PUFAs 0.00–149 0.06 24.9
Total PUFA 148–955 0.71 105
Individual Fatty acids, mg/100 g 1st derivative, SNV, DT, PLS 0.86 355
Total CLAs, mg/100 g 1.9–114 2nd der., MPLS 0.67 14.2
Total PUFAs, mg/100 g 148–955 2nd der., MPLS 0.71 105
Beef—freeze-dried Dry matter, % 25.15 ± 1.22 1st der., 2nd der, MPLS 0.96 0.35 [79]
Lipid, % 1.99 ± 1.20 1st der., 2nd der, MPLS 0.99 0.13
IMF, % 0.88–8.48 SNV, DT, 1st der. PLS 0.94 0.39 [124]
Ash, % 1.00 ± 0.06 1st der., 2nd der, MPLS 0.44 0.03 [79]
Protein, % 22.16 ± 0.47 1st der., 2nd der, MPLS 0.85 0.33
Collagen, mg/100 g 18.43 ± 5.30 1st der., 2nd der, MPLS 0.56 3.05
Tenderness 2.0–7.2 SNV, DT, PLSM 0.981 0.353 [66]
Myoglobin (mg/g of muscle) 2.55–5.08 RS, PLSM 0.914 0.260
WHC (%of liquid expelled) 21.17–29.17 RS, PLSM 0.892 1.338
Total CLAs, mg/100 g 1.9–114 2nd der., MPLS 0.76 11.3 [61]
Total PUFAs, mg/100 g 148–955 2nd der., MPLS 0.78 84.9
Hydroxyproline, % 0.1–3.3 SNV, PLSR 0.89 0.25 [125]
L* 23.85–50.77 SNV, DT, PLSR 0.765 2.51 [69]
a* 4.63–27.02 SNV, DT, PLSR 0.878 2.51
b* 3.27–21.14 SGS, 1st der., SNV, PLSR 0.767 1.44
Hue 21.74–58.06 SGS, 1st der., SNV, PLSR 0.924 4.06
Chroma 6.19–32.43″ SGS, 1st der., SNV, PLSR 0.867 2.43
Fatty acids, % 1st derivative, SNV, DT, PLS [61]
Total Fatty acid 0.58–16.3 0.88 319
C16:0 101–4051 0.92 212
C18:0 89–3086 0.91 370
C18:1 n-9 123–5339 0.67 58.6
C18:2 n-6 62.0–502 0.67 16.4
Beef—freeze-dried C18:3 n-3 6.35–128 0.55 14.0 [61]
C20:4 n-6 11.9–115 0.17 6.55
C20:5 n-3 0.00–39.0 0.32 13.1
C22:5 n-3 0.00–86.8 0.22 1.74
C22:6 n-3 0.00–11.3 0.90 570
Total SFAs 216–8116 0.90 473
Total MUFAs 185–7019 0.90 457
Total cis-MUFAs 163–6526 0.79 50.4
Total trans-MUFAs 8.4–545.6 0.76 11.3
Total CLAs 1.9–114 0.47 37.9
Total n-3 PUFAs 10.4–264 0.25 24.8
Total n-3 LC PUFAs 0.00–149 0.78 84.9
Total PUFA 148–955 0.88 319
Adulteration with turkey meat 0–10% SNV, PLS Classification: 80.3% [126]
15–20% Classification: 85%
30–40% Classification: 90%
50% Classification: 100%
100% Classification: 100%
Ether extract, % 0.47–6.10 2nd der., PLS 0.82 0.44 [123]
Adulteration, % 0–35
0–35
[127]
with pork RS, DA, PLS 0.9580 7.27 accuracy: 100%
with pork and duck MSC, SGS, DA, PLS 0.9569 9.27; accuracy: 9.27
with chicken 0–100% 1st der., PLS 0.99 3.5 [128]
with chicken and pork 0.93 4.7
Ox Protein, g/kg DM 588.7–851.0 MSC, 2nd der., PLS 0.874 20.33 [74]
Myoglobin, g/kg DM 17.7–37.0 MSC, 2nd der., PLS 0.440 3.45
Collagen, g/kg DM 5.7–21.3 2nd der., PLS 0.472 3.82
Ether extract, g/kg DM 92.2–359.8 MSC, 2nd der., PLS 0.924 16.22
Gross energy, MJ/kg DM 24.0–28.7 MSC, 2nd der., PLS 0.941 0.29
Dry matter, g/kg FM 271.0–339.1 RS, PLS 0.874 6.75 [74]
Ash, g/kg FM 31.7–57.7 RS, PLS 0.168 5.15
Hamburger meat Iron, mg/100 g 0.43–2.54 MC, 1st der., PLS 0.73 0.34 [129]
Calcium, mg/100 g 5.69–36.99 MC, MSC, 1st der., PLS 0.72 22.59
Potassium, mg/100 g 208.48–391.15 MC, MSC, 1st der., PLS 0.93 68.01
Sodium, mg/100 g 49.44–978.65 MC, MSC, 1st der., PLS 0.96 2.78
Pork Fat, % 2.58–3.15 MSC, 1st der., PLSR 0.767 0.087 [94]
Protein, % 19.15–23.01 MSC, 1st der., PLSR 0.757 0.405 [94]
22.2 ± 0.7 1st and 2nd der., PLS 0.57 0.49 [130]
Water, % 65.32–73.62 MSC, 1st der., PLSR 0.794 0.776 [94]
73.7 ± 1.5 1st and 2nd der., PLS 0.71 0.94 [130]
pH 5.06–5.98 MSC, 1st der., PLSR 0.824 0.104 [94]
pH ultimate 5.12–6.27 MSC, 1st der., PLS 0.70; 0.75 0.11; 0.11 [76]
Shear force, N 11.17–28.89 MSC, 1st der., PLSR 0.278 0.360 [94]
IMF, % 0.51–2.75 2nd der., MLR 0.35 0.36 [75]
0.1–4.3 2nd der., MPLS 0.70–0.86 0.26–0.36 [78]
Intact 32.4–51.1 PCA, 1st and 2nd der. SGS, PLS 0.33 4.0 [112]
IMF, g/kg
Moisture, %
Homogenized 694.3–713.0 PCA, 1st and 2nd der. SGS, PLS 0.66 3.1
IMF, g/kg
Moisture, %
IMF (g/kg) 0.22–7.12 n.i. 0.22; 0.33 1.09; 1.03 [76]
IMF, % 3.2 ± 1.8 1st and 2nd der., PLS 0.84 0.73 [130]
L* 38.6–63.35 PLS 0.84; 0.77 1.80; 2.02 [76]
a* (-) 1.78–4.67 MSC, PLS 0.75; 0.84 0.61; 0.61
b* 6.59–15.82 MSC, PLS 0.74; 0.81 1.14; 1.07
WBSF, N 25.87–62.03 MSC, 1st der., PLS 0.30; 0.25 4.98; 5.51
Tenderness PLS Accuracy 72%
Juiciness PLS Accuracy 73%
Fatty acids, % Normalization, 1st der., PLS [83]
SFA 34.5–45.9 0.98 0.36
MUFA 40.5–53.6 0.88 0.77
PUFA 7.0–20.9 0.96 0.54
16:0 20.3–26.2 0.88 0.39
18:0 10.7–17.6 0.94 0.32
Pork 18:1 37.1–49.1 0.92 0.59 [83]
18:2n-6 5.8–17.7 0.86 0.84
18:3n-3 0.01–4.02 0.76 0.33
LC-PUFA 0.78–2 0.88 0.09
TFA 0.3–2.3 0.83 0.12
in vivo [131]
C16:0 17.8–25.5 SNV, DT, 1st der., PLS 0.74 1.24
C18:0 6.9–12.5 SNV, DT, 1st der., PLS 0.72 0.67
C18:1 46.7–59.1 SNV, DT, 1st der., PLS 0.77 1.42
C18:2 6.5–10.2 SNV, DT, 2nd der., PLS 0.60 0.36
carcass
C16:0 17.8–25.5 SNV, DT, 1st der., PLS 0.87 0.82
C18:0 6.9–12.5 SNV, DT, 1st der., PLS 0.46 0.94
C18:1 46.7–59.1 SNV, DT, 1st der., PLS 0.80 1.48
C18:2 6.5–10.2 SNV, DT, 2nd der., PLS 0.31 0.55
Minced MSC, 2nd der., PLS [132]
L* 35.90–53.58 0.75 1.03
Myoglobin, mg/g 1.04–2.64 0.74 0.11
Hardness, N 2.68–20.31 0.74 0.99
Cohesiveness 0.17–0.39 0.79 0.02
Springiness, mm 0.52–2.15 0.79 0.08
Chewiness, N × mm 1.20–8.83 0.78 0.50
Intact
L* 35.90–53.58 MSC, 1st der., PLS 0.68 1.36
Myoglobin, mg/g 1.04–2.64 MSC, 2nd der., PLS 0.67 0.18
Hardness, N 2.68–20.31 MSC, 2nd der., PLS 0.80 1.00
Cohesiveness 0.17–0.39 MSC, 2nd der., PLS 0.61 0.03
Springiness, mm 0.52–2.15 MSC, 2nd der., PLS 0.60 0.17
Chewiness, N × mm 1.20–8.83 MSC, 2nd der., PLS 0.69 0.97
TBARS (malondialdehyde/kg) 0.16–0.68 MSC, HSI-PLS 0.932 0.0305 [91]
Lamb Moisture, % 72.0–78.6 SNV, DT, MSC, PCA, 2nd der., PLS 0.67 0.69 [96]
Fatty acid, mg/100 g C14:0 10.2–154.84 1st and 2nd der., GA-PLS 0.70 11.98 [88]
C16:0 170.52–1055 0.70 87.01
C16:1 7.8–56.7 0.63 5.43
C17:0 9.7–56.9″ 0.60 4.69
C17:1 4.4–23.1 0.55 2.32
C18:0 173.6–761.2 0.53 73.09
C18:1 c9 269.4–1503.4 0.69 128.31
C18:1 c11 8.42–30.7 0.73 2.01
C18:2 n-6 45.2–107.9 0.62 5.88
C18:2 c9 t11 5.70–81.0 0.68 7.10
C18:1 t11 20.5–197.09 0.61 21.10
C18:3 n-3 27.91–79.13 0.53 6.11
C20:4 14.39–30.92 0.40 2.30
C20:5 15.19–31.51 0.50 2.41
C22:5 16.23–26.89 0.47 1.57
C22:6 3.38–10.54 0.32 1.69
SFA 393.13–2065 0.60 192.21
MUFA 289.3–1678.5 0.60 168.72
PUFA 191–533.9 0.67 27.86
IMF, % 0.3–4.6 SNV, DT, MSC, PCA, 2nd der., PLS 0.84 0.41 [96]
3.49–18.54 1st and 2nd der., GA-PLS 0.69 1.6 [88]
1.2–6.79 MSC, PCA, PLS 0.79 0.38 [100]
Protein, % 53.49–84.33 2nd der., PLS 1.00 0.92 [98]
Fat, % 7.30–51.80 2nd der., PLS 1.00 0.43
Dry matter, % 90.55–95.92 2nd der., PLS 0.96 0.38
Ash, % 2.27–4.67 2nd der., PLS 0.97 0.15
K, mg/kg freeze-dried meat 8300–11,500 2nd der., PLS 0.86 600.00
P, mg/kg freeze-dried meat 5400–10,400 1st der., PLS 0.88 900.00
Na, mg/kg freeze-dried meat 960–1629 normalized, PLS 0.89 77.89
Mg, mg/kg freeze-dried meat 500–700 1st der., PLS 0.92 40.00
Fe, mg/kg freeze-dried meat 26.20–47.90 normalized, PLS 0.88 3.15
Zn, mg/kg freeze-dried meat 51.50–72.30 normalized, PLS 0.86 3.59
Mutton Rebound - 2nd der., SPA, PLS 0.94 0.05 [62]
Volatile basic nitrogen - MSC-UVE, PLS 0.74 1.81
Rabbit Fatty acid, % C14:0 1.66–3.12 1st and 2nd der., MSC, MPLS 0.21 0.26 [133]
C16:0 22.85–34.76 0.83 1.21
C16:1 cis n-7 0.91–6.83 0.77 0.64
C18:0 5.03–9.74 0.50 0.63
C18:1 n-9 18.52–30.18 0.84 1.26
C18:1 n-7 0.96–1.73 0.33 0.15
C18:2 n-6 14.99–41.19 0.91 2.08
C18:3 n-3 1.82–4.72 0.59 0.47
C20:1 0.19–0.53 0.08 0.07
C20:2 n-6 0.23–0.63 0.23 0.08
C20:3 n-6 0.15–0.47 0.54 0.04
C20:4 n-6 0.65–3.17 0.63 0.31
SFA 30.26–46.03 0.85 1.43
MUFA 20.81–37.21 0.83 1.81
PUFA 20.11–46.78 0.93 2.03
SFA 162–858 SNV, DT, 1st der., 2nd der., MPLS 0.96 32.2 [90]
MUFA 92–778 0.98 24.2
PUFA 143–568 0.83 37.2
n-6 PUFA 110–493 0.87 27.8
n-3 PUFA 23.6–82.2 0.50 7.87
Protein, % 18.1–26.3 SNV, DT, 1st der., 2nd der., MPLS 0.77 0.41
IMF, % 0.75–3.25 SNV, DT, 1st der., 2nd der., MPLS 0.98 0.07
Chicken Dry matter, % 20.45–26.43 RS, PLS 0.72 0.69 [134]
Moisture, % 73.57–79.55 RS, PLS 0.72 0.69
Protein, % 48.47–66.74 MSC, 2nd der., MPLS 0.86 2.012 [135]
13.89–19.4 RS, PLS 0.73 0.65 [134]
Fat, % 15.15–34.66 MSC, 2nd der., MPLS 0.93 1.723 [135]
Ash, % 7.67–11.08 MSC, 2nd der., MPLS 0.71 0.795
1.68–3.08 RS, PLS 0.74 0.19 [134]
Chicken L* 38.14–49.99 PLS 0.69 1.73 [97]
47.3–66.4 1st der., MPLS 0.74 2.3 [136]
46.08–63.91 RS, PLS 0.71 3.30 [71]
58.28–74.59 RS, PLS 0.84 1.40 [134]
pH 5.51–6.15 PLS 0.71 0.09 [97]
5.64–6.33 RS, PLS 0.58 0.24 [71]
6.35–6.7 RS, PLS 0.78 0.03 [134]
pHu 5.3–6.4 2nd der., MPLS 0.36 0.2 [136]
DFD n.i. Accuracy 77.78% [71]
Normal or PSE n.i. Accuracy 82.35% or 75.00%
a* −3.29–0.04 PLS 0.88 0.29 [97]
5.1–13.3 1st der., VN, MPLS 0.51 1.2 [136]
0.6–1.21 RS, PLS 0.72 0.08 [134]
b* −4.86–16.33 PLS 0.93 1.16 [97]
3.6–12.1 MPLS 0.55 1.3 [136]
14–21.95 RS, PLS 0.77 1.00 [134]
Ether extract, % 3.55–4.98 RS, PLS 0.83 0.18
Thawing loss, % 1.16–12.42 PLS 0.70 1.00 [97]
Cooking loss, % 13.36–29.18 PLS 0.76 1.88
Shear force, N 8.14–29.06 PLS 0.41 3.18
Drip loss, % 0.7–7.0 1st der., MPLS 0.73 0.8 [136]
Hen Protein, % 83.0–93.5 SNV-DT, 1st der., MPLS 0.91 0.74 [137]
Lipid, % 1.9–11.8 DT, 1st der., MPLS 0.99 0.24
Dry matter, % 91.8–94.8 DT, 1st der., MPLS 0.96 0.19
Ash, % 4.0–7.5 SNV, DT, 1st der., MPLS 0.05 0.65
Poultry hydroxyproline, % 0.4–1.5 SNV, PLS 0.82 0.11 [125]
Yak Classification 400–780 nm Grazing or Feedlot Yaks original, PLS-DA 0.870 0.521 [138]
SNV, PLS-DA 0.967 0.347
1st der., SNV, PLS-DA 0.829 0.590
2nd der., SNV, PLS-DA 0.795 0.724
780–2500 nm original, PLS-DA 0.844 0.738
SNV, PLS-DA 0.705 0.724
1st der., SNV, PLS-DA 0.975 0.478
2nd der., SNV, PLS-DA 0.958 0.429
Yak 400–2500 nm original, PLS-DA 0.861 0.548 [138]
SNV, PLS-DA 0.893 0.465
1st der., SNV, PLS-DA 0.904 0.481
2nd der., SNV, PLS-DA 0.989 0.449
Alpaca Classification Pork 0–50% SGS, SNV, MC, PLS 0.90 6.34 [139]
Chicken SGS, 1st der., MC, PLS 0.87 6.69
Beef SGS, 1st der., MC, PLS 0.88 5.11
Ostrich (freeze dried) Crude protein, % 85.45–93.93 2nd der., PLS 0.97 0.64 [140]
Fat, % 1.41–8.33 2nd der., PLS 0.99 0.18
Dry matter, % 94.53–99.37 2nd der., PLS 0.85 0.75
Ash, % 4.31–5.50 normalization, PLS 0.71 0.23
Meat-type classification Horse vs. beef vs. chicken vs. mutton vs. turkey vs. Pork (meat pieces) 2nd der., SNV, PCA, SVM-c Prediction Accuracy 38.1% [64]
Horse vs. beef vs. chicken vs. mutton vs. turkey vs. Pork (minced meat) Prediction Accuracy 42.9%
Horse vs. beef (meat pieces) Prediction Accuracy 62.5%
Horse vs. beef (minced meat) Prediction Accuracy 100.0%
Horse vs. chicken (meat pieces) Prediction Accuracy 87.5%
Horse vs. chicken (minced meat) Prediction Accuracy 75.0%
Horse vs. mutton (meat pieces) Prediction Accuracy 87.5%
Horse vs. mutton (minced meat) Prediction Accuracy 87.5%
Horse vs. turkey (meat pieces) Prediction Accuracy 100.0%
Horse vs. turkey (minced meat) Prediction Accuracy 75.0%
Horse vs. pork (meat pieces) Prediction Accuracy 75.0%
Horse vs. pork (minced meat) Prediction Accuracy 75.0%
Adulteration in Meat Chicken 0–100% 2nd der., SNV, PCA, PLS 0.85 13.83; RPD: 3.05
Mutton 0.94 7.52; RPD: 5.68
Pork 0.88 11.95; RPD: 2.19
All adulterated 5–50% 2nd der., PLS
2nd der., PLS
RS, PLS
2nd der., PLS
SNV, PLS
RS, PLS
2nd der., PLS, SNV, PLS
RS, PLS
0.5348 0.1914 [141]
Lamb-pork 0.9381 0.0706
Lamb-chicken 0.9693 0.0490
Lamb-duck 0.9218 0.0782
Beef-pork 0.9207 0.0791
Beef-chicken 0.9542 0.0599
Beef-duck 0.9016 0.0872
Pork-chicken 0.9119 0.0842
Pork-duck 0.8932 0.1018
Table 4.

NIR test results for meat products.

Sample Investigated Parameter Concentration Range Chemometrics Data References
Pre-Treatment, Regression R2 Root Mean Square Error
Sausages control samples vs. treated dry fermented 2nd der., SNV, OPLS-DA Classification rate: 100% [117]
0 vs. 0.5 vs. 1 vs. 2 vs. 3
0 kGy Classification rate: 46.7%,
0.5 kGy Classification rate: 41.7%
1 kGy Classification rate: 100%
2 kGy Classification rate: 91.7%
3 kGy Classification rate: 100%
Intact, % SNV, DT, MSC, MPLS [142]
Fat 15.3–43.2 0.98 1.47
Moisture 29.5–41.9 0.93 0.97
Protein 20.1–36.1 0.97 1.08
Homogenized, %
Fat 15.3–43.2 0.99 0.71
Moisture 29.5–41.9 0.98 0.41
Protein 21.1–36.1 0.97 0.95
Minced, % [143]
Fat 8–31.7 PCR 0.97 1.38
Moisture 50.2–68.4 0.98 1.01
Protein 13.6–20.5 0.93 0.83
Homogenized, %
Fat 8–31.7 MSC, SNV, DT, MPLS 0.99 0.94
Moisture 50.2–68.4 0.98 0.77
Protein 13.6–20.5 0.93 0.87
Cured pork sausage, cured beef
Hydroxyproline, %
0.13–0.74 SNV-D, MSC, 1st der., MPLS 0.80 0.05 [115]
On-contact probe [116]
Moisture, % 16.98–65.82 1st der., MSC, PLS 0.997 0.675
aw 0.765–0.982 1st der., VN, PLS 0.988 0.006
NaCl, % 1.13–3.80 1st der., VN, PLS 0.974 0.117
Remote probe
Moisture, % 16.98–65.82 1st der., MSC, PLS 0.998 0.622
aw 0.765–0.982 1st der., MSC, PLS 0.985 0.007
NaCl, % 1.13–3.80 1st der., MSC, PLS 0.974 0.116
Sausages Emulsion-type Moisture, % 41.19–69.98 MSC, PLS 0.99 0.86 [119]
Fat, % 9.08–45.39 0.99 1.27
Protein, % 10.30–18.30 0.99 0.36
Residual nitrite, ppm 0.00–74.32 0.92 12.02
Remote Q410/A Moisture, % 16.77–66.14 min-max norm., PLS 0.990 1.56 [116]
aw 0.754–0.982 VN, PLS 0.984 0.01
NaCl, % 1.07–3.86 SLS, PLS 0.910 0.22
On-contact IN 268-2
Moisture, % 16.77–66.14 1st der., VN, PLS 0.983 1.86
aw 0.754–0.982 1st der., VN, PLS 0.948 0.01
NaCl, % 1.07–3.86 1st der., SLS, PLS 0.804 0.33
Dry-cured [144]
C12:0 0.06–0.10 SNV, DT, 2nd der., MPLS 0.03 0.01
C14:0 1.22 1.78 SNV, DT, 1st der., MPLS 0.63 0.07
C16:0 22.83–28.00 SNV, DT, 1st der., MPLS 0.84 0.58
C16:1 2.25–3.71 SNV, DT, 2nd der., MPLS 0.41 0.26
C17:0 0.13–0.35 SNV, DT, 2nd der., MPLS 0.04 0.04
C17:1 0.15–0.33 SNV, DT, 1st der., MPLS 0.03 0.04
C18:0 10.57–14.83 SNV, DT, 2nd der., MPLS 0.78 0.55
C18:1 42.97–52.59 SNV, DT, 2nd der., MPLS 0.58 1.51
C18:2 4.54–10.34 SNV, DT, 2nd der., MPLS 0.56 0.86
C18:3 0.37–1.14 SNV, DT, 2nd der., MPLS 0.56 0.16
C20:0 0.16–0.28 SNV, DT, 2nd der., MPLS 0.02 0.02
C20:1 0.39–1.09 SNV, DT, 1st der., MPLS 0.07 0.17
SFA 35.65–44.79 SNV, DT, 2nd der., MPLS 0.86 0.98
MUFA 46.85–56.82 SNV, DT, 2nd der., MPLS 0.53 1.47
PUFA 4.92–11.23 SNV, DT, 2nd der., MPLS 0.61 0.88
Ham Remote Moisture, % 19.92–66.11 normalization, PLS 0.929 3.51 [121]
aw 0.823–0.929 RS, PLS 0.618 0.01
NaCl, % 0.67–14.02 VN, 1st der., PLS 0.910 1.13
On-contact
Moisture, % 19.92–66.11 normalization, PLS 0.899 4.17
aw 0.823–0.929 VN, PLS 0.451 0.02
NaCl, % 0.67–14.02 normalization, PLS 0.861 1.40

5.3. Milk and Dairy Products

Milk is one of the most important sources of nutrients widely consumed around the world, either in its natural form or through dairy products. Therefore, in the dairy industry, quality and safety control is essential to ensure that products meet legal requirements and customer needs.

Milk is a nutrient-rich complex liquid, 87% of which is water, so it also acts as a solvent for various nutrients. The remaining 13% contains nutrients that are essential for human health, such as lactose, which makes up about 4–5% of milk, is critical for supplying energy, and contributes to the distinctive taste of dairy products. Proteins make up about 3% of the composition of milk and can be divided into two classes: caseins and whey proteins. Caseins make up 80% of milk proteins, are insoluble, and form complexes called micelles, which can trap calcium and phosphorus. Whey proteins, which make up about 20% of milk proteins, are soluble, and are known for their high levels of branched-chain amino acids, which support muscle maintenance and immune function. Milk contains between 3% and 4% fat, 98% of which is made up of triglycerides, with more than 400 different fatty acids. This fat fraction is predominantly composed of 70% saturated fatty acids, including significant amounts of palmitic, myristic, and stearic acid, and 30% unsaturated fatty acids, mainly oleic acid. Milk also contains a small proportion of polyunsaturated fatty acids such as linoleic acid and alpha-linolenic acid. Milk fat includes bioactive compounds as well, such as conjugated linoleic acid, known for its cardiovascular support and anti-cancer effects. Although the micronutrient composition of milk is significantly influenced by the cow’s diet and the conditions of dairy technology, in general, it has a mineral content of about 0.8%, the main constituents of which are calcium and phosphorus, essential for bone and tooth structure and metabolic processes. In addition, milk provides significant amounts of magnesium and zinc selenium, supporting a range of physiological functions. Of the vitamins, both fat-soluble vitamins (A, D, E) and water-soluble B complex vitamins are found in milk, in total 0.1% [145].

One of the most prominent applications of near-infrared spectroscopy is in the milk and dairy industry, dating back to the late 1970s. This chapter reviews publications on the use of NIR in the dairy industry from 2004 to 2024. Most of the publications in this period deal with the quality assessment of milk and dairy products. In these cases, an estimation model is built to quantify the major compositional parameters, including protein [146,147,148,149,150,151,152,153,154,155,156], fat [146,147,148,149,150,152,153,154,155,156,157,158,159,160,161], lactose [146,147,148,149,152,153,154,155,162,163,164,165], moisture [166,167,168] and other quality attributes, like fatty acids [149,169,170,171,172,173,174], titratable acidity [163,175], pH [147,163,168], somatic cell count [146,149,155,160,176], vitamins [162,170], minerals [177,178,179], freezing point [147,155], density [147] in the final product or during dairy technological steps for monitoring and quality control purposes. In addition, there are several studies on the use of NIR in the detection of adulteration of dairy products, the classification of the products tested, and the quantification of the adulterant. Some publications report on NIR methods used to identify the animal species or geographical origin of dairy products. The key publications on the application of NIR in the dairy industry are summarized in Table 5.

There are some comprehensive reviews on the application of NIR in the dairy industry, providing valuable information for the quantification of major and minor components of milk and dairy products. The potential of non-destructive techniques for the determination of the quality of dairy products was presented by Karoui et al. [180]. Wang et al. [181] summarized the research developments of NIR in the field of liquid foods. A recent review discussed the use of multivariate chemometric modelling of NIR, MIR, fluorescence and Raman spectral data and the use of data fusion strategies for milk analyses [182].

Most of the publications in the period 2004–2024 focused on analyzing different milks, as shown in the Table 4.

Melfsen et al. [149] published their results about robustness of NIR calibration models for the prediction of milk fat, protein, and lactose. Different calibration models (fully random internal calibration, internal calibration, external calibration, and a combination of internal and external datasets) and different validations (internal and external) were used to estimate fat, protein, and lactose content. Excellent calibration results were obtained in the case of the fully random internal calibration sets; RPD values of around 10, 5 and 3 for the prediction of fat, protein, and lactose, respectively, were achieved. An application of internal calibration showed much poorer prediction results, especially for the prediction of protein and lactose. They also found that the prediction accuracy improved when a validation was conducted on the spectra of the external dataset. The effect of temperature on the accuracy of FT-NIR measurements was investigated by Dvorák et al. [183]. The samples were measured in a reflectance mode at 18, 20, 22, 24, and 40 °C. The results underlined that temperatures do not generally affect dry matter and lactose content in milk; responses to changes in temperature are probably caused by changes in the composition of fats and proteins. Therefore, milk should be measured at the same temperature as the calibrated instruments. Benedictis et al. [184] demonstrated an approach for optimizing near-infrared spectra with experiment designs. The investigated factors are layer thickness, number of scans, and temperature during measurement. The response variables were absorption intensity, signal-to-noise ratio, and reproducibility of the spectra. Optimized factorial combinations have been found to be 0.5 mm layer thickness, 64 scans, and 25 °C ambient temperature, for liquid milk measurements. Pu et al. [185] published a review article about advances in portable and handheld NIRs, focusing on recent developments and their latest applications in the field of dairy, including chemical composition, on-site quality detection, and safety assurances in milk, cheese, and dairy powders. Guerra et al. [155] reviewed the application of a short-wave pocket-sized near-infrared spectrophotometer to predict fat, protein, casein, lactose, urea, freezing point, SCC, and fat to protein ratio in cow milk. A total of 331 individual milk samples were collected for chemical determination and spectral collection by using two pocket-sized NIR spectrophotometers working in the range of 740 to 1070 nm, and modified partial least squares regression models were developed. The results revealed that short-wave pocket-size NIR spectrophotometers have the potential to predict milk fat, protein, casein, and fat-to-ratio while the poor models obtained for lactose, SCC, MUN, and freezing point could be related to a lack of information in this short-wave NIR region. Portable NIR was used by Yang et al. [153] to determine fat, protein, lactose, and total solids in milk using PLSR models. The effect of several spectral pre-processing methods on prediction performance were evaluated, and the results indicated that Savitzky–Golay smoothing (SGS) and SGS combined with standard normal variate proved the best spectral pretreatment method for raw milk and for homogenized milk, respectively.

The characterization of milk with NIR is not limited to estimating the quantity of the main constituents. Allende-Prieto et al. [186] used the NIR to detect bacteria in milk. The combination of PCA and PLS-DA was used to distinguish the contaminated and the uncontaminated samples. The results suggested that NIR technology can be used to accurately classify contaminated and uncontaminated milk samples, regardless of the type of bacteria causing contamination, even at low concentrations. However, the spectral analysis was not capable of distinguishing between the four studied contaminating bacteria. Tsenkova et al. [176] summarized their results about disease diagnosis and pathogen identification in milk samples. They have developed spectroscopic models for the simultaneous measurement of somatic cell count and electrical conductivity, as well as for identification of the main mastitis-causing bacterial pathogens in milk. These results highlight the potential of NIR spectroscopy as a powerful technology for in vivo health monitoring, disease diagnostics at the molecular level, and bacterial identification.

A good example of the use of the NIR technique for monitoring specific processes in dairy technologies is found in the work of Grassi et al. [187] about monitoring milk renneting during cheese manufacturing. A multivariate curve resolution optimized by alternating least squares (MCR-ALS) was used for data analysis and development of multivariate statistical process control (MSPC) charts. The models described the coagulation processes (explained variance ≥99.93%; lack of fit <0.63%; and standard deviation of the residuals <0.0067) well. Lactic acid fermentation process monitoring was investigated by the same research team Grassi et al. [188]. Some rheological and conventional quality parameters (microbial counts, pH, titratable acidity, lactose, galactose, and lactic acid concentrations) were used as reference values to assess the findings with FT-NIR spectroscopy. The results showed that near-infrared spectroscopy is a useful tool for real-time assessment of curd development during fermentation. Lyndgaard et al. [189] published a paper which focuses on the extraction of real-time, meaningful information from NIR reflectance measurements of coagulating milk.

In addition to milk, there have also been a few publications on the study of cheese. A comprehensive review regarding the application of NIR for predicting the chemical composition of cheese was written by Bittante et al., by providing the results of 71 papers. In addition to estimating the quantification of the main components, NIR was widely used in cheese to monitor technological processes and determine specific properties. Cheese ripeness was predicted based on the ratio of water-soluble nitrogen to total nitrogen as an index of cheese maturity by Currò et al. [190]. The prediction of sensory attributes of cheese via NIR was studied by González-Martín et al. [191]. Nicolau et al. published an application of NIR for the estimation of clotting and cutting times in sheep cheese manufacture. [192].

Comparatively few publications have been published on NIR analysis of other dairy products such as yoghurts and butters. Butter is mainly measured for fat and fatty acids [159,173], while yoghurts are measured for fat [156,160], protein [156], sugar [162] and pH [160] using NIR.

NIR is also widely used in the dairy industry to detect adulteration. According to a 2013 European Parliament report, milk was one of the four foodstuffs considered to be the most common target of economically-motivated adulteration. Milk and dairy products are foods with high nutritional value, largely consumed by the general population and play an important role in the diets of certain consumer groups, notably children and pregnant women. Due to their high demand and value, fraud in the dairy industry has become a widespread problem [193].

More review articles cover this topic, giving a good overview [180,193,194,195,196].

There have been reports of several types of in the dairy industry. Most of them can be detected by NIR, including dilution with water [197,198,199,200,201], addition of whey rennet [197,199,202], substitution of milk fat or protein [203,204,205], addition of fillers [202,203,206,207], substitution of milk from one species with a lower valued one [183,199,208,209,210,211], and addition of nitrogen-rich adulterants like melamine [206,207,212,213,214,215,216,217] or urea [198,199,206,207,218] to increase protein content.

The practise of mislabelling, either in terms of geographical origin or animal species origin, is also considered adulteration. Classification models based on NIR can distinguish dairy products by geographical origin [219,220,221] and animal species by origin [222], with high accuracy.

In conclusion, one of the most widespread uses of NIR is the qualification of milk and dairy products, monitoring of dairy technological processes, and detection of adulteration, with many present results and several future improvement opportunities.

The detailed data are summarized in Table 5 and Table 6.

Table 5.

NIR test results for milk.

Sample Investigated Parameter Concentration Range Chemometrics Data References
Pre-Treatment, Regression R2 Root Mean Square Error
Milk Sugar—lactose, % 2.06–5.06 RS, PLS 0.83 0.26 [146]
3.30–4.29 1st der., PLS 0.90 0.11 [147]
2.92–5.22 transmittance, 1st der., OSC, RiPLS 0.883 0.115 [148]
3.9–5.2 normalization, PLS 0.92 0.06 [149]
3.09–4.70 ANN 0.8822 0.238 [163]
3.98–5.1 MSC, 1st der., OSC, Rt-RiPLS 0.689 0.077 [152]
Log base, 1st der., OSC, PH-RiPLS 0.644 0.092
raw 3.97–4.89 SGS, PLS 0.78 0.11 [153]
homogenized SGS, SNV, PLS 0.71 0.12
4.14–5.25 RS, PLS 0.13 0.87 (RPD) [155]
Sugar, % SNV, MC, PDS, PLS [165]
sucrose 14.20–43.69 0.7973 5.04
lactose 0.000–38.99 0.9411 4.22
Lactose free, % 0–1 1st der., SNV, PLS 0.79 0.1984 [164]
Carbohydrate, g/100 mL 2.5–13.5 SGS, 2nd der., PLS 0.883 0.639 [154]
Fat, % 1.01–7.39 RS, PLS 0.95 0.25 [146]
5.66–11.06 1st der., PLS 0.73 0.66 [147]
2.72–7.94 transmittance, SNV, GA-PLS 0.997 0.043 [148]
0.7–12.3 normalization, PLS 0.998 0.09 [149]
Gerber
Röse-Gottlieb
0.13–7.25 PLS 0.98 0.232 [158]
0.992 0.148
0.1–3.7 SGS, 2nd der., PLS 0.969 0.216 [154]
0–3.9 1st der., MSC, PLS 0.98 0.002 [157]
1.54–6.25 DT, 2nd der., OSC, PH-RiPLS 0.989 0.078 [152]
DT, 2nd der., Rt-FiPLS 0.989 0.083
raw
homogenized
2.09–5.76 SGS, PLS 0.97 0.18 [153]
SGS, SNV, PLS 0.99 0.11
1.03–5.02 MC, SNV, SGS, SSDL 0.95 0.22 [161]
1.86–5.96 DT, PLS 0.93 3.73 (RPD) [155]
Milk Fatty acids, mg/mL SGS, SNV, SVM [174]
C4:0 0.08–0.325 0.87 0.03
C6:0 0.004–0.21 0.83 0.02
C14:0 0.019–1.208 0.82 0.11
C16:0 0.044–3.381 0.74 0.35
C18:1C9 0.048–1.75 0.84 0.12
SFA 0.128–6.553 0.83 0.59
MUFA 0.056–2.128 0.87 0.15
SCFA 0.011–0.505 0.88 0.04
BCFA 0.004–0.141 0.83 0.01
PUFA. mg/g: C18:2 0.63–59.88 2nd der., MSC, MPLS 0.58 8.40 [169]
C22:6 0.05–0.16 0.75 0.01
ω6 0.63–60.09 0.58 8.41
ω6/ω3 3.51–12.34 0.76 0.94
Total fatty acid, % SNV, DT, MSC, 1st der., MPLS [170]
SFA 36.74–78.06 0.96 2.03
MUFA 17.73–50.65 0.81 4.13
PUFA 2.02–14.08 0.80 0.95
trans FA 0.35–29.05 0.84 2.95
SFA 59.7–89.5 1st der., PLS 0.72 1.86 [171]
MUFA 9.30–38.2 2nd der., SNV, PLS 0.83 2.12
PUFA 1.21–7.20 2nd der., SNV, PLS 0.55 1.97
SCFA 2.97–9.87 2nd der., SNV, PLS 0.87 2.25
MCFA 40.61–71.77 2nd der., SNV, PLS 0.43 1.79
Acidity, °T 16.0–24.8 ANN 0.9709 0.380 [163]
pH 6.50–7.01 1st der., PLS 0.42 0.105 [147]
Protein, % 2.77–4.38 RS, PLS 0.72 0.15 [146]
5.30–7.00 1st der., PLS 0.84 0.21 [147]
2.65–5.01 reflectance, 1st der., OSC, GA-PLS 0.959 0.099 [148]
2.4–4.0 normalization, PLS 0.98 0.05 [149]
6.45–6.95 ANN 0.9645 0.0202 [163]
2.61–4.77 SNV, PLS 0.77 1.84 (RPD) [155]
Milk raw 2.88–3.59 MSC, PLSR-UVE-PLS 0.92 0.06 [151]
homogenized 0.96 0.04
2.63–4.34 SNV, 1st der., OSC, PH-FiPLS 0.947 0.08 [152]
DT, 2nd der., OSC, Rt-RiPLS 0.894 0.11
raw 2.94–4.33 SGS, PLS 0.85 0.16 [153]
homogenized SGS, SNV, PLS 0.90 0.13
1.3–7 SGS, 2nd der., PLS 0.883 0.290 [154]
Casein, % 2.03–3.70 DT, PLS 0.70 1.80 (RPD) [155]
Urea, mg/100 mL 10.41–15.73 RS, PLS 0.53 1.5 [146]
13.6–33.2 1st der., OSC, GA, PLS, RiPLS n.i RPD < 1.2 [148]
12.1–38.0 normalization, PLS 0.82 1.932 [149]
5.10–31.70 SNV, DT, PLS 0.43 1.18 (RPD) [155]
Freezing pont, °C −0.66–−0.47 1st der., PLS 0.90 0.02 [147]
−0.503–−0.548. SNV, PLS 0.22 0.64 (RPD) [155]
SCC, cell/μL 7.00–2837 RS, PLS 0.03 0.22 (RPD)
Log SCC, log cells/mL 3.78–5.84 RS, PLS 0.68 0.28 [146]
3.5–6.0 normalization, PLS 0.85 0.18 [149]
Fat:protein ratio 0.82–3.43 DT 0.71 1.74 [155]
Total solid content, % 9.42–15.12 [153]
raw SGS, PLS 0.96 0.28
homogenized SGS, SNV, PLS 0.98 0.21
Carotenoids, μg/mL; cis9-β-carotene, β-cryptoxanthin 0.11–1.04 SNV, MSC, DT, 1st der., 2nd der., MPLS >0.50 0.01 [170]
Vitamin A, μg retinol/mL 0.03–1.33 SNV, DT, MSC, MPLS 0.34 0.15
Density kg/m3 1029.66–1039.94 1st der., PLS 0.88 1.07 [147]
Fat free dry matter, % 9.53–12.45 1st der., PLS 0.90 0.29
Ash, % 0.87–1.14 1st der., PLS 0.89 0.03
Contamination 4–9 log cfu/mL MC, SNV, PLS [223]
E. coli 0.936 0.284
P. aeruginosa 0.597 0.0202
E. coli + P. aeruginosa 0.8822 0.584
Milk Progesterone (real-time), ng/mL 0.10–12.61 2nd der., PLS 0.93 1.06 [224]
3.92–21.37 0.89 1.22
0.03–10.78 0.93 0.92
0.01–4.86 0.91 0.43
Classification adulterated MSC, 2nd der., DPLS Accuracy: 100% [197]
lactose (no or yes) PLS-DA Sensitivity: 90% or 100%
Specificity: 100% or 90%
[154]
E. coli, P. aeruginisa MC, SNV, PLS-DA correct prediction 99% [223]
Salmonella sp. 2nd der., PLS-DA Sensitivity: 100%
Specificity: 100%
[225]
geographical origin SGS, FUDT, kNN accuracy 98.67% [220]
geographical origin SGS, SNV, kNN, FD-LDA accuracy: 97.33% [221]
water EPO, RSDE accuracy: 98%; reliability: 98% [200]
water RS, DTC or RFC, or kNN accuracy: 100% [201]
melamine RS, PLS-DA Sensitivity and specificity 100% [214]
melamine OPLS-DA R2X: 0.996, R2Y: 0.964, Q2: 0.933 [217]
Adulteration with water, % 1–97 MSC, PLS 0.997 2.159 [197]
0–70 MC, SNV, SGS, SSDL 0.80 0.12 [161]
1–30 RS, BRT 0.95 0.58 [200]
0–40% RS, kNN, SVML 0.999 0.353 [201]
Adulteration with melamine, % 0.001–0.29 OCPLS Sensitivity. specificity, accuracy
90%.; 88%; 89%
[215]
1–20 SNV, PLS 0.98–2.99 matrix dependent [217]
Adulteration with whey, % 2.15–48.40 MSC, PLS 0.999 0.244 [197]
0.01–0.29 1st der., UVE-PLS 0.97 0.015 [214]
Flavoured milk drink Moisture, % 77.13–80.83 2nd der., PLS and ANN 0.982; 0.989 0.778; 0.744 [168]
Water activity 0.963–0.982 2nd der., PLS and ANN 0.996; 0.984 0.764; 0.725
Total soluble solids, % 19.16–22.86 2nd der., PLS and ANN 0.687; 0.946 0.727; 0.754
pH 6.35–6.66 2nd der., PLS and ANN 0.955; 0.955 0.723; 0.711
Colour, BI 15.915–19.630 2nd der., PLS and ANN 0.988; 0.978 0.703; 0.713
Milk brands Classification MSC, EELM accuracy: 100% [226]
SGS, PCA, LDA, iNLDA, FiNLDA, KNN accuracy: 74.7% (LDA), 88% (iNLDA), 94.76% (FiNLDA) [227]
Human milk Moisture, % 83.18–94.26 2nd der., PLS 0.90 0.5149 [167]
Fat, g/100 mL 1.56–6.37 2nd der., PLS 0.70 0.4274
0.51–5.30 RS, PLS 0.841 0.51 [228]
Ash 0.09–0.40 2nd der., PLS 0.64 0.0507 [167]
Protein, g/100 mL 0.45–5.04 2nd der., PLS 0.70 0.3581
0.27–2.50 RS, PLS 0.512 0.21 [228]
Carbohydrates, g/100 mL 2.73–10.63 2nd der., PLS 0.70 0.6063 [167]
2.34–8.80 RS, PLS 0.741 1.35 [228]
Total solid content, g/100 mL 3.27–14.60 RS, PLS 0.686 2.42
Energy, kcal/100 mL 33.80–87.04 2nd der., PLS 0.83 3.7848 [167]
15.60–86.00 SNV, 1st der., PLS 0.830 9.60 [228]
Classification—lactation phases Colostrum
Transition
Mature
MSC, PLS-DA Sensitivity, Specificity
87.5%, 90.3%
56.3%, 71.9%
93.8%, 93.8%
[167]
Bovine colostrum adulterated milk MSC, PLS-DA Sensitivity, Specificity, Accuracy
84.62%, 100%, 94.74%
[210]
Infant formula Moisture, % 2–13 PLS 0.99 0.62 [229]
Storage time, months 0–3–6–12 PLS 0.97 n.i. [230]
FAST index 1.88–21.54 RS, PLS 0.78 n.i.
Soluble protein, % 0.77–5.29 RS, PLS 0.86 n.i.
Fat, % 24.94–28.65 SNV, PLS 0.74 n.i.
SFF, % 0.02–2.60 PLS 0.88 n.i.
Classification PLS-DA accuracy 100%
Adulteration with melamine, μg/g 17.3–2000 1st der., MC-OSC, ANN, SVR, LS-SVM n.i. 6.1 [212]
Milk powder Carbohydrates, % 50.73–60.28 SNV, LS-SVM 0.982 0.384 [231]
Fat, % 15.93–21.80 RS, LS-SVM 0.981 0.247
Protein, % 14.82–18.14 SNV, LS-SVM 0.984 0.148
18.0–32.6 SNV, MRMR-PLS 0.99 0.37 [232]
Moisture, % 4–10 MC, 2nd der., PLS 0.9822 0.1730 [166]
Mineral content, Ca-mg/100 g 243.1–722.8 SGS, SNV, UVE-SPA-LS-SVM 0.85 0.18 [178]
Classification brands MRMR-PLS-DA accuracy: 100% [232]
Milk powder Adulteration, % 14.6–2000 1st der., MC-OSC, Poly-PLS n.i. 0.28 [212]
melamine, μg/g
corn starch
wheat flour
0–30 MSC, 1st der., PLS
2nd der., MSC, PLS
0.74
0.82
9.70
8.38
[202]
Goat milk Lactose, % 2.06–5.06 RS, PLS 0.935 0.050 [175]
Fat, % 2.27–5.61 RS, PLS 0.924 0.154 [175]
0.9–4.2 13MM-LBC, iSPA-PLS 0.96 0.20 [209]
Protein, % 2.33–3.41 RS, PLS 0.888 0.111 [175]
2.95–5.03 13MM-BO-LBC, PLS 0.96 0.047 [209]
Total solid content, % 10.30–13.76 RS, PLS 0.899 0.334 [175]
Fat free solids, % 7.19–8.81 RS, PLS 0.812 0.191
Freezing point, °C −0.599–−0.527 RS, PLS 0.833 0.005
Titratable acidity, °SH 4.60–8.20 RS, PLS 0.878 0.469
pH 5.69–6.92 RS, PLS 0.703 0.076
Adulteration, 1.0154–100 SPA, PLS 0.9955 3.66 [209]
cow milk, %
water, urea, bovine, whey or cow milk 0–20% 1st der., MC, SNC, PLS-DA for authentication and adulteration sensitivity and specificity 100% [199]
Classification adulterated PLS-DA accuracy: 100% [209]
Goat milk powder Adulteration, % [207]
urea 0.5–10 area normalization, PLS 0.992 0.321
melamine 0.01–10 Area normalization, PLS 1.000 0.042
starch 1–30 smoothing, PLS 1.000 0.139
Goat dairy products adulteration with cow milk, % 10; 15; 20% MC, 2nd der., PLS-DA with iPLS Sensitivity, specificity
100% for both sample groups
[211]
yoghurt
cheese
cheese 0–50 PLS 0.783 2.454 [183]
Camel milk Adulteration with cow milk, % 0–20 1st der., PLS 0.92 1.32 [208]
Classification pure or adulterated PLS-DA 0.97 0.08
Plant milk Sugar, % (glucose) 0.5–7.6 1st der, MNSN, iPLS 0.84 0.98

Table 6.

NIR test results for dairy products.

Sample Investigated Parameter Concentration Range Chemometrics Data References
Pre-Treatment, Regression R2 Root Mean Square Error
Cheese Fat, % SNV, MPLS
MSC, MPLS
[191]
summer 25.55–50.97 0.936 1.68
winter 19.97–61.29 0.871 3.23
lyophilized 19.1–55.6 MSC, 1st der., PLS 0.99 1.0 [150]
Total fatty acid, % SNV, PLS; SVM [172]
total FA 47.57–472.44 0.86; 0.59 28.87; 24.40
SFA 33.22–325.04 0.84; 0.88 21.66; 18.32
MUFA 10.02–114.1 0.75; 0.83 9.11; 7.47
PUFA 0.00–10.15 0.0; 0.1 2.78; 2.32
SCFA 2.54–26.95 0.80; 0.89 1.94; 1.36
MCFA 7.14–55.51 0.22; 0.55 5.34; 4.88
BUFA 0.9–5.29 0.78; 0.79 0.61; 0.55
Protein, %—lyophilized 24.7–60.7 SLS, SGS, 1st der., PLS 0.972 1.4 [150]
Minerals, % PLS [179]
Ca 0.229–0.510 0.75 0.02
K 0.023–0.167 0.37 0.17
Mg 0.009–0.020 0.82 0.00
Na 0.024–0.290 0.89 0.02
P 0.187–0.370 0.82 0.01
Classification geographical origin normalization, FDA accuracy: [219]
Austrian: 100%
Finnish: 66.7%
German: 76.9%
French: 83.3%
Swiss: 94.7%
summer or winter DPLS Accuracy 97 and 96% [191]
Species of origin SNV, SG, PCA Accuracy 76% [222]
Butter cheese Classification PLS-DA Accuracy 94.44% [233]
Adulteration with soybean oil, % 5–100 RS, PLS 0.941 7.202
Yoghurt Fat, % 0.12–14.69 PLS 0.978 0.968 [160]
2.6–4.4 2nd der., SNV, MC, PLS 0.990 0.25 [156]
Sugar, % 10.75–13.25 MSC, SGS, PC-ANN 0.91 0.41 [162]
Yoghurt Lactose free, % 0–1 1st der., SNV, PLS 0.98 0.0609 [164]
Protein, % 3.2–3.5 2nd der., SNV, MC, PLS 0.80 0.16 [156]
Total solid content, % 10.32–22.48 PLS 0.989 0.46 [160]
Titratable acidity, °SH 11.88–58.91 PLS 0.979 2.47
pH 4.00–4.24 PLS 0.788 0.038
3.97–4.27 MSC, SGS, PLS 0.90 0.04 [162]
Adulteration, % SNV, OCPLS Sensitivity: 90%
Specificity: 91.9%
[203]
edible gelatine 1–8
industrial gelatine 0.5–5
soy protein 0.5–5
Dry matter, % 1st der., iPLS [159]
reflectance 39.7–80.7 0.9730 2.224
transmittance 19.5–59.7 0.9488 2.2399
Fat, % 1st der., iPLS
reflectance 19.3–61.3 0.9772 1.9955
transmittance 17.7–57.5 0.9245 2.8545
Fatty acid—trans, % 0.24–0.62 PLS 0.98 0.46 [173]

5.4. Vegetable

The concept of a vegetable plant cannot be precisely defined. Generally, it refers to horticulturally-derived food with high biological value, rich in vitamins, mineral salts, and aromatic substances.

Detailed research results in vegetables are summarized in Table 7, Table 8, Table 9, Table 10, Table 11 and Table 12.

5.4.1. Nightshades (Solanaceae)

The tomato (Solanum lycopersicum) is among the most extensively studied vegetables within the nightshade family.

NIR tests on tomatoes primarily focus on measuring the water-soluble dry matter (SSC) and titratable acid (TA) content of the fruit, as well as the SSC/TA value, which correlates with taste [234,235,236,237,238,239,240,241].

In addition to examining the SSC value, estimation models have been developed for the quick and non-destructive determination of glucose and fructose content in tomato samples, as well as the titratable acidity and the concentration of ascorbic acid and citric acid [242,243].

In addition to the quality attributes, evaluations of the texture and shelf life of extremely fragile tomatoes are also important [236,244].

To identify the most advantageous varieties, different classification models have been developed [236,239,242]. The PCA procedure was used for the classification, and the prediction models were performed using the PLS or wave number selection PLS regression method. The NIR technique combined with chemometric methods has been utilized to monitor quality alterations during storage. Classification tests have been performed on data from surface and liquid biopsies [245].

Quality characterization of tomatoes based on sensory attributes is time-consuming and very expensive. For this reason, it is not included in routine phenotyping. The root square error of prediction (RSEP) values for sensory properties (flavour and aroma intensity, texture, juiciness, and flouriness) were low. This can be explained by the fact that only 55 samples were tested for sensory properties. Despite the poor result, it is suggested that the estimation function can be improved for a larger sample population [243].

The NIR technique was successfully applied to the monitoring of lycopene concentration in addition to changes in quality attributes during ripening and storage [246,247,248]. In addition to the lycopene, titratable acidity (TA), and total phenolic content (TPC) of four dehydrated tomato varieties, a successful method was developed for determining the total sugar content and antioxidant capacity using near-infrared (NIR) spectroscopy. data obtained from the FRAP (Ferric Reducing Ability of Plasma), DPPH [2,2-di(4-tert-octylphenyl)-1-picrylhydrazyl], and ABTS (2,2′-Azinobis-(3-ethylbenzo-thiazoline-6-sulfonic acid) methods served as reference for antioxidant capacity determinations [249].

Duckena et al. [250] carried out comprehensive research on the NIR estimation of quality attributes in 80 different tomato cultivars. Besides the commonly tested dry matter content (DM) and taste index (SSC/TA), the method development was also expanded to include the estimation of lycopene, beta-carotene, total polyphenol, and flavonoid concentrations.

Li et al. [251] proposed a novel prediction method utilizing segmentation of Vis-NIR spectral graph features to assess the activity of tomato polyphenol oxidase (PPO). The experimental outcomes indicated that this algorithm enhances the modelling effect, simplifies the modelling process, and increases the efficiency of the model [251].

Although, the use of various plant protection insecticide and the determination of their residues pose significant challenges in horticultural products. Typically, residues are measured using capillary gas chromatography (GC) and/or high-performance or ultraperformance liquid chromatography (HPLC or UPLC) coupled with mass spectrometry (MS) methods [252], following complex sample preparation. The NIR technique has been successfully applied to monitor lycopene.

Nazarloo 2021 et al. [253] conducted experiments to determine if the Vis/NIRS technique is suitable as a pesticide residue prediction method. Samples with different pesticide residual (Pre-Harvest Interval -PHI) concentrations of 2 per 1000 L were infected. The tests were performed at different times after spraying (without spraying, 2 h after spraying, after 2 days, after a week, after two weeks). At the same intervals, the tests were also carried out by washing the samples after spraying. GC-MS measurements were also used to verify residue concentrations. Using different variable selection and data management procedures, it was established that the most favourable correlation was given by the ANN model combined with the successive prediction algorithm (SPA) (Table 7).

De Brito et al. [254] compiled a comprehensive summary manuscript presenting the determination of various tomato attributes using the NIR technique for the period 2010–2022.

Table 7.

Overview of NIR Results for Tomato (Solanum lycopersicum).

Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Treatment, Regression R2 Root Mean Square Error
Soluble solids content—SSC; °Brix 4.10–5.60 MN, MSC, PCA, PLS 0.80 0.210 [234]
≈5.0–8.6 EPO, PLS 0.9072 0.302 [235]
3.0–5.9 PCA, PLS 0.97 0.22 [236]
n.i. SGS, MSC, CARS, PLS 0.828 0.17 [237]
n.i. Smooth, PCA, BPN 0.8328 0.5711 (MAD) [238]
4.20–6.80 MSC, ELM 0.75 0.27 [239]
3.0–6.7 MSC, PLSR 0.72 0.58 [240]
3.2–6.8 OSC, PCA, PLSR 0.66 0.3227 [241]
2.92–11.22 MC, Smooth, 2nd der., PLS 0.89 0.52 [242]
4.20–11.60 2nd der., PLS 0.97 0.24 [243]
3.87–3.99 PLS 0.93 0.366 [244]
3.4–6.3 PLS 0.86–0.91 0.07–0.4 [246]
4.4–6.1 SNV, 1st der., CARS; RF-PLS 0.812 0.211 [247]
Dry matter 4.55–13.15 PLSR 0.83 0.98 [250]
Textural property MSC, PLS [234]
Puncture test, N 1.96–6.08 0.902 0.35
1515–1612 (Wp) PLS, var. selection 0.92 579 [244]
59.47–62.41 (Fint) 0.91 14.2
Firmness/hardness, N 11–23 PCA, PLS 0.71 0.7 [236]
Lycopene; mg/kg ≈50–118 EPO, PLS 0.8238 7.14 [235]
79.4–287.5 MSC, PLSR 0.68 15.07 [240]
3.69–50.05 PLS 0.73–0.84 0.91–0.92 [246]
‘Provence’ 26.43–264.77 SNV, LARS-PLSR 0.95 7.34 [248]
‘Jingcai No. 8′ 7.65–119.36 0.96 13.44
mg/kg DW 240–415 RBF-NN 0.939 16.1 [249]
0–83.8 PLSR 0.85 9.5 [250]
β-carotene 0.4–117.3 PLSR 0.85 10.1
Glucose, g/100 g 4.68–39.12 Norm, 2nd der., PLS 0.87 2.91 [242]
0.85–3.95 2nd der., PLS 0.98 0.09 [243]
Fructose, g/100 g 8.65–39.12 Norm, 2nd der., PLS 0.87 2.83 [242]
1.06–3.82 2nd der., PLS 0.98 0.08 [243]
Ascorbic acid, mg/100 g 3.77–77.91 Norm, 2nd der., PLS 0.82 4.09 [242]
Citric acid, g/100 g 0.11–1.10 Norm, 2nd der., PLS 0.87 0.07
Titratable acid—TA, % 0.1–1.7 PCA, PLS 0.89 0.20 [236]
4.58–7.12 PLS 0.91 0.646 [244]
0.5204–0.6320 PLS 0.74–0.77 0.0084–0.013 [246]
4.19–6.15 PLS 0.88 0.18 [249]
Total sugar, % 21.3–43.4 PLS 0.972 1.22
Dry matter 5.17–11.55 1st der., PLS 0.98 0.26 [243]
Taste (SSD/TA) 0.58–0.85 PLS 0.71 0.038 [244]
2.8–22 PCA, PLS 0.94 1.5 [236]
0.86–1.52 PLSR 0.77 0.1 [250]
Classification Maturity of three species MSC, PCA Correctness 96.85% [234]
Maturity of five species Classification success [239]
1st der., PCA 94.62%
SNV, PCA 76.92%
MSC, PCA 62.69%
MSC, PCA 78.85%
MSC, PCA 89.962%
Storage condition surface SVM AC = 92%; SENS = 86%; SPEC = 98% [245]
liquid biopsies AC = 94%; SENS = 74%; SPEC = 95%
Total polyphenol concentration (TPC), mg GAE/100 g 16.77–60.91 PLSR 0.5 6.33 [250]
g GAE/100 g DW 1.03–1.94 PLSR 0.954 0.08 [249]
Antioxidant activity
μmol trolox/100 g DW
FRAP 57.9–118 RBF-NN 0.936 3.89 [249]
DPPH 30.9–54.8 0.939 2.82
ABTS 47.7–108 0.968 3.44
Flavonoid, mg QE/100 g 1.09–11.02 PLSR 0.8 1.31 [250]
Polyphenol oxidase (PPO) act., U/mL 8.0–45.0 ASR, MLR 0.97 1.99 [251]
Pesticide residues, mg/kg n.d.–34.0 PCA, SPA-ANN 0.982 0.166 [253]

5.4.2. Brassicas (Brassicaceae)

Brassicaceae family includes a variety of cabbages such as Chinese cabbage, cauliflower, and kale, along with the traditional white and red cabbage.

The primally goal of NIR examinations are to determine the quality attributes of fresh products and those occurring during storage, such as moisture, SSC, ascorbic acid content, colour, firmness, and freshness. Following various data processing, the best estimation models were developed using PLS or SVR regression [255,256,257].

The protein content of lyophilized broccoli, Brussels sprouts, curly kale, white cabbage, red cabbage, cauliflower, and white kohlrabi was studied by Szigedy et al. [258].

Determining the nitrogen content of the samples is crucial in addition to the quality attributes, as it allows for the monitoring of proper nutrient management and necessary interventions. This ensures the production of an adequate yield and a high-quality product [259].

Successful classification models have been developed based on NIR spectra for determining freshness through colour and for differentiating various Brassica species [233,256].

In the case of red cabbage samples, a high concentration of bioactive components is typical. These include polyphenols (TPC) and anthocyanins (TAC), as well as the antioxidant capacity associated with these compounds.

Antioxidant capacity can be measured using various methods, including ORAC (oxygen radical absorbance capacity), TEAC (Trolox equivalent antioxidant capacity) and DPPH (α, α-diphenyl-β-picrylhydrazyl). Caramês et al. [260] and de Olivera et al. [261] carried out a successful model development using near- and mid-infrared technology. This is also very important because the determination of antioxidant capacity using different methods expresses the antioxidant capacity based on different properties, so the results obtained by different methods are not comparable.

The purple Chinese kale has an extremely high concentration of anthocyanidins, which have notable physiological effects. UHPLC-UV measurements confirmed that cyanidins are present at the highest concentration among the anthocyanidins when compared to other varieties.

Classical methods for anthocyanidin determination are time-consuming both in terms of sample preparation and chromatographic determination. Successful NIR method development has proved to be highly effective not only in quality control but also in vegetable cultivation [262].

Glucosinolates, which are secondary metabolites found in nearly all plants of the Brassicales order, make the determination of their concentration in brassicas an important matter. A spectral reflectance technique was developed which is used to quantify the functional components and can be characterized by appropriate chemometric qualification, which replace the chemical-intensive and lengthy classical methods [263,264,265].

An estimation function was developed for the quantitative measurement of the residues the pesticides such as profenofos [266], avermectin, dichlorvos, and chlorothalonil [267] using kale, cabbage, and cauliflower as samples. Different chemometric data as well as processing and prediction procedures were compared (Table 8).

Table 8.

Overview of NIR Results for Brassica (Brassicaceae).

Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Treatment, Regression R2 Root Mean Square Error
Moisture, % 93.35–95.82 2nd der., PLS 0.74 0.25 [255]
Soluble solids content—SSC; °Brix 3.45–5.53 2nd der., PLS 0.64 0.22
Protein 12.9–32.5 MSC + 1st der., PLS 0.988 0.76 [258]
Ascorbic acid, mg/g 3.8–10.8 2nd der., PLS 0.38 1.3 [256]
29–68 MSC, PLS 0.95 3.19 [255]
Weight loss rate 0.5–18 SNV, PLS 0.96 1.432 [256]
Surface colour—L*, mg/100 g 64–74 MSC, SVR 0.82 2.013
Firmness 13–26 autoscale, SVR 0.60 2.453
Freshness A; weight loss rate < 30%, L* > 71 SVC Accuracy A; 93.3% [256]
B; 30% ≤ weight loss rate < 50%, 68 < L* ≤ 71 B; 86.6%
C; weight loss rate ≥ 51%, L* ≤ 68 C; 86.6%
mean 73,777 °C·min SWSR, PLSR 0.753 22,651 [257]
Classification Three species SGS, PLS-DA SEN 100%, SPEC 95.7%, AC 93.6% [233]
SGS, iPLS-DA SEN 100%, SPEC 97%, AC 94.9%
N content; g/kg 15.4–48.4 SMLR 0.726–0.846 3.71–4.4 [259]
PLS 3.84–4.31
TAC; mg/g 3.04–7.41 1st der., MSC, MC, PLS 0.85 0.47 [260]
TPC; GAEq/g 3.87–6.97 1st der., MSC, MC, PLS 0.78 0.41
mg GAE/L 101.32–595.72 PLS-OPS,
PLS-GA
0.99 10.74 [261]
Antioxidant capacity
μmol trolox/g ORAC 434.11–1741.18 1st der., MSC, MC, PLS 0.87 116.34 [260]
μmol trolox/g TEAC 3.79–6.46 0.85 0.29
μm trolox/100 g DPPH 91.01–209.85 0.80 11.47
μmol trolox/mL DPPH 0.85–4.79 PLS-OPS, 0.99 0.22 [261]
μmol trolox/mL ABTS 0.70–5.75 PLS-GA 0.99 0.12
Cyanidin; μg/g 93.5–12,802.4 DT + 1st der., PLS 0.941 684.969 [262]
0.02–217.56 RS, PLS 0.56 60.37 [268]
Malvidin; μg/g 0.07–11.82 RS, PLS 0.91 1.04
Pellargonidin; μg/g 0.02–0.25 RS, PLS 0.74 0.03
Glycosinolates (total); 7.46–46.50 μg/cm2 Exp(Ref), SMLR 0.39 8.067 [264]
Total aliphatic glucosinolates 0–220.94 μmol/g n.i. 0.9 15.11 [265]
Total indolic glucosinolates, μmol/g 0–30.83 n.i. 0.97 2.35
Glucoraphanin, μg/cm2 1.22–16.02 Ref2; 1/Ref, SMLR 0.946 1.12 [264]
4-methoxyglucobrassicin, μg/cm2 1.63–7.57 Ref; Exp(Ref), SMLR 0.892 0.646
μmol/g 0–23.58 SNV-DT, MPLS 0.96 1.82 [265]
μmol/g 0.02–2.58 SNV-DT, SMLR 0.84 0.24 [268]
Neoglucobrassicin, μg/cm2 0.28–4.96 1/R; Exp(R), SMLR 0.893 0.386 [264]
μmol/g 0.03–1.56 Ln(Ref), SMLR 0.87 0.11 [268]
Sinigrin, μmol/g 0.03–1.56 Ln(Ref), SMLR 0.86 1.32
μmol/g 0–132.44 SNV-DT, MPLS 0.99 6.39 [265]
Gluconapin, μmol/g 0.13–1.69 Ref/Exp(Ref)/1/Ref /Ln(Ref), SMLR 0.89 0.12 [268]
μmol/g 0–171.47 SNV-DT, MPLS 0.95 9.06 [265]
Glucobrassicin, μmol/g 0.05–16.77 1/Ref or Ln(Ref), SMLR 0.92 0.88 [268]
Glucoalyssin, μmol/g 0–2.87 SNV-DT, MPLS 0.92 0.34 [265]
Glucoiberin, μmol/g 0–13.18 SNV-DT, MPLS 0.98 2.4
Pesticide residues; mg/kg
Profenofos in Chinese kale, 0.60–106.28 SNV + 1st der., PLS 0.97 5.25 [266]
Profenofos in cabbage 0.53–105.36 1st der., PLS 0.88 11.00
Avermectin 0.25–2.0 RS, RC; LV-SVM AC 98.33%; PRE 98.46% [267]
Dichlorvos 0.25–2.0 RS, PLS-DA AC 98.33%; PRE 95.26%
Chlorothalonil 0.25–2.0 RS, CARS, PLS-DA AC 93.33%; PRE 93.57%
Chlorpyrifos 0.011–2.184 MN, PLS-DA, AC 100%; PRE 99% [269]
RS or MN/SNV-DT/MSC, SVM, AC 100%; PRE 100%
RS, PC-ANN AC 100%; PRE 100%
Bacterial contamination
for thestomacher solution, og CFU/g 2.85–7.08 PLS 0.95 0.46 [270]
for the washing solution, og CFU/g PLS 0.92 0.44

5.4.3. Leaf Vegetables

Spinach (Spinacia)

Green colour, texture, and dry matter content are important indicators in assessing the freshness and quality of spinach. Modified partial least squares regression models based on NIR spectra of whole spinach leaves were developed to assess these characteristics, including colour (a* and b* values), texture (measuring maximum breaking strength, toughness, stiffness, and displacement), and dry matter content. The calibration model of the dry matter content was suitable for the quantitative evaluation, the texture parameter models were suitable for screening, while in the case of the colour-related parameters, the models allowed a rough screening of the test samples. This method can be a useful tool for on-site analysis, aiding in the optimization of fertilization and irrigation, as well as assessing quality at the time of harvest [271].

NIR models have been developed for on-site quality assessment in the field, during harvest and storage, and for an online analysis of the processing chain. The models were used to predict crop texture, dry matter, soluble solids content, ascorbic acid content, and safety parameters, such as nitrate content. The further development of these methods has allowed real-time monitoring of the spinach plant growth process. The PLS-DA method was employed to ascertain if a pattern of spinach-usage (fresh, quick-frozen) could be detected based on spectra and nitrate content. [77,272,273,274].

The use of the non-linear regression method (LOCAL) for the determination of nitrate concentration led to a model with more favourable statistical properties [275].

The microbiological spoilage (Pseudomonas) of baby spinach through various non-destructive approaches, such as the NIR technique, have been investigated. The data were analyzed using PLS and SVR algorithms. The findings suggest that with the appropriate sensor and algorithm, this method could be universally applied to all food products [276] (Table 9).

Lettuce (Lactuca)

A non-destructive measurement method based on Vis-NIR spectra has been developed for the determination of chlorophyll, carotenoid, and anthocyanin in three different varieties of lettuce (Lactuca sativa L.): crystal—green crinkled leaves, Regina 2000 plain green leaves, and Mimosa—slightly red, crinkled leaves [277].

Boros et al. [278] investigated the nitrate content of five varieties of lettuce applied FT-NIR technik (batavia, butterhead, lollo, and oak leaf; (both red and green coloured) during autumn and spring harvesting, as well as under open field and greenhouse cultivation conditions.

Wu et al. employed a variable selection and GA-LDA to develop a classification model that effectively differentiates between organic and non-organic vegetables using the Vis-NIR spectrum data from the stems and leaves of leafy vegetables (water spinach, amaranth, lettuce, and pakchoi) [279].

A method was developed for the non-destructive and accurate qualitative detection of pesticide residues in vegetables, specifically tested on lettuce leaf samples for fen valerate and chlorpyrifos residues. Following data preparation and variable selection, a classification was performed based on the transmission spectra [280].

Biological contaminants in fresh-cut lettuce, like worms, have been detected using multispectral imaging algorithms combined with Vis-NIR and NIR techniques. Following variable selection, the worm detection algorithms for both Vis-NIR and NIR imaging demonstrated high prediction accuracy [281].

When examining lettuce samples, the identification of not only bacterial infections (mainly Escherichia coli) but also fungal infections (Aspergillus niger, Fusarium oxysporum and Alternaria alternata) is of particular importance. Different chemometric classification methods, including SIMCA, SNV, PLS-DA, PCA, and HCA, were used to analyze and distinguish between safe and unsafe samples in the different microbial loads on the spectra [282]. Fungal infections mainly occur in lettuce grown in aquaponic systems, where chemical control of fungal pests is not possible, as it can be fatal to fish. The tested pathogens had a statistically significant effect on the water content of lettuce leaves and the water band index (WBI). The distinct spectral changes induced by each pathogen might potentially provide a way to not only detect infection but also identify the type of pathogen involved. Plant senescence reflectance index and WBI were significantly different for plants infected by A. niger and A. alternata, and could serve as key indicators for these specific pathogens. Among Vis-NIR reflectance spectra and vegetative indices, WBI proved to be the most reliable in distinguishing between infected and healthy plants [283] (Table 9).

5.4.4. Root Vegetables

Artificial Neural Networks (ANNs) have been utilized to forecast the content of completely dissolved solids, polyphenols, and antioxidant capacity in root vegetables, such as celery, fennel, carrots, yellow carrots, purple carrots, and parsley. These assessments were conducted on samples that were fresh, conventionally dried at 50 °C and 70 °C, as well as freeze-dried. Extractions were carried out using two distinct solvents [284].

A non-destructive method has been developed to determine the reducing sugar and protein content of sweet potatoes. A stepwise regression, combined with the regression coefficient (SRRC) method, was used to select optimal wavelengths for optimizing full-band PLS models [285,286].

Near-infrared reflectance spectroscopy combined with chemometric is suitable for analyzing and differentiating between powdered, pure, and adulterated samples of purple and white sweet potatoes. In addition to detecting falsification, the total anthocyanin content and antioxidant activity of the samples were evaluated, and the established estimation functions demonstrated a high residual prediction deviation (RPD) (Table 10) [287].

5.4.5. Pumpkins (Cucurbitaceae)

A NIR method was developed to determine the β-carotene content in pumpkin flesh, peel, and seed samples, with acetone as the extraction solvent. The highest concentration of β-carotene was found in the peel, followed by the flesh. The β-carotene content in pumpkin seeds could not be detected using the NIR technique [288].

The applicability of Vis-NIR spectroscopy and colour spectroscopy has been investigated to determine the total carotenoid and flavonoid content of three different cucumber varieties. The study examined how varying concentrations of ethephon (0, 150, and 300 ppm) influenced spectral characteristics and pigment prediction accuracy. It was demonstrated that non-destructive measurement techniques, utilizing a colour spectrophotometer and Vis/NIR spectroscopy, yield reliable predictions of total carotenoid and flavonoid content [289].

Classification models were developed using the Vis-NIR spectra of zucchini, bitter gourd, squash, cantaloupe, chayote, and cucumber to distinguish between these products. A comparison of various classification algorithms revealed that only one of the zucchini sample was incorrectly classified [290] (Table 11).

Table 9.

Overview of NIR Results for Leaf vegetables.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Processing, Regression R2 Root Mean Square Error
Spinach Dry matter, % 7.35–18.83 SNV, DT, 2nd der., MPLS 0.70 1.58 [271]
4.10–19.12 SNV, DT, 1st der., 2nd der., MPLS 0.66 1.22 [272]
6.12–20.34 SNV, DT, 1st der., 2nd der., MPLS 0.68 1.27 [274]
Texture
Maximum puncture force, N 0.37–4.51 SNV, DT, 2nd der., MPLS 0.62 0.83 [271]
1.03–4.57 SNV, DT, 1st der., 2nd der., MPLS 0.3 0.41 [272]
Tughness, mJ 0.38–8.73 SNV, DT, 2nd der., MPLS 0.63 1.50 [271]
Stiffness, N/mm 009–1.03 SNV, DT, 2nd der., MPLS 0.65 0.20
Displacement, mm 0.57–6.05 SNV, DT, 2nd der., MPLS 0.50 1.2
Colour
a* −17.32–(−10.78) SNV, DT, 2nd der., MPLS 0.31 1.09 [271]
b* 13.77–23.02 SNV, DT, 1st der., MPLS 0.13 2.22
Soluble solids content—SSC; °Brix 5.6–14.25 SNV, DT, 2nd der., MPLS 0.86 0.59 [77]
4.10–11.45 SNV, DT, 2nd der., MPLS 0.80 0.67 [272]
5.8–14.4 SNV, DT, 1st der. 2nd der., PCA, PLS 0.62 1.0 [274]
5.2–15.2 SNV, DT, 1st der., 2nd der., MPLS 0.68 1.0 [273]
Ascorbic acid, mg/100 g 157–454 SNV, DT, 1st der., MPLS 0.25 55.19 [77]
Nitrate content, mg/kg 109–5177 SNV, DT, 2nd der., MPLS 0.41 834.27
41–3526 SNV, DT, 1st der., 2nd der., PLS 0.59 725 [274]
67–3844 SNV, DT, 1st der., MPLS 0.51 567.79 [272]
70–3875 SNV, DT, 1st der., 2nd der., MPLS 0.62 688 [273]
41–3845 SNV, DT, MPLS 0.45 920 [275]
623–3845 SNV, DT, LOCAL 0.60 758
Microbiological spoilage, log CFU/g 6.8–9.0 random data partitioning, SVR 0.4 0.6 [276]
Lettuce Pigments 5.0–8.5 DT, PLS 0.8 0.495 [277]
Chlorophyll, mg/kg
Total carotenoid, mg/kg 0.9–1.8 0.76 0.105
Anthocyanins, mg/kg 0.1–4.0 0.89 0.592
Nitrate content, mg/kg fresh w. 1200–2750 SNV, MSC, PLS 0.90 99.4 [278]
Classification variety types LDA
red and green leaved variants of lollo and oak leaf variety types LDA AC 100%; PRE 100%
organic and no-organic SG, LDA AC 96.4% (leaf); 96.9% (stem) [279]
SS/RF/ANOVA, GA-LDA AC 92.1/84.9/80.5%
Potassium, mg/100 g 165–480 1st der., CARS, PLS 0.83 39.7 [291]
Green leaves (mixed samples) 1st der., RBF-NN 0.86 38.06
Petioles (mixed samples) CARS, PLS 0.71 31.20
RBF-NN 0.88 27.63
Pesticide residues n.i. SGS, SNV, CARS-IRIV-SGS, SNV, GSA-SVM AC = 98.33% [280]
Biological investigations n.i. ANOVA, HSI AC 97% (Vis-NIR), 100% (NIR) [281]
Worms
Escherichia coli SNV + 2nd der., PLS-DA 0.958 0.257 [282]
0.1, 0.2, 0.3 mL SVM AC = 100%
Table 10.

Overview of NIR Results for Root Vegetable and Sweet Potatos.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Treatment, Regression R2 Root Mean Square Error
Root vegetable Fresh Hidden/Output activation function, ANN, MLP [284]
Total dissolved solids, mg/L 10–690 Tanh/Exponential, ANN, MLP 0.9101 0.0165
Polyphenol content, TPC 3.5–800 Exponential/Logistic, ANN, MLP 0.7864 0.0141
Antioxidant capacity, DPPH mmol Trolox/g 0.04–0.33 Tanh/Identity, ANN, MLP 0.7356 0.0234
Dried 50 °C Hidden/Output activation function, ANN, MLP
Total dissolved solids, mg/L 5–460 Tanh/Exponential, ANN, MLP 0.7625 0.0262
Polyphenol content, TPC 1–29 Exponential/Logistic, ANN, MLP 0.8090 0.0363
Antioxidant capacity, DPPH mmol Trolox/g 0.02–0.19 Tanh/Identity, ANN, MLP 0.8409 0.0017
Dried 70 °C Hidden/Output activation function, ANN, MLP
Total dissolved solids, mg/L 30–500 Tanh/Exponential, ANN, MLP 0.8141 0.0167
Polyphenol content, TPC 1–13.5 Exponential/Logistic, ANN, MLP 0.7772 0.0128
Antioxidant capacity, DPPH mmol Trolox/g 0.05–0.15 Tanh/Identity, ANN, MLP 0.8452 0.0029
Lyophilized Hidden/Output activation function, ANN, MLP
Total dissolved solids, mg/L 60–620 Tanh/Exponential, ANN, MLP 0.8201 0.0117
Polyphenol content, TPC 1–30 Exponential/Logistic, ANN, MLP 0.8457 0.0188
Antioxidant capacity, DPPH mmol Trolox/g 0.01–0.27 Tanh/Identity, ANN, MLP 0.8246 0.0143
Sweet potatoes Reducing sugar content, % 0.35–3.31 SRRC-KM-PLS 0.952 0.264 [285]
Protein content, % 2.53–6.87 2nd der., PLSR 0.96 0.29 [286]
Total anthocyanins 0.449–0.563 (PSP)
0.027–0.084 (WSP)
RBF-PLS 0.985 0.031 [287]
Total antioxidant activity, RBF-PLS
DPPH, μmol trolox/100 g DW 570.0–585.0 (PSP) 0.975 1.602
554.6–562.0 (WSP)
ABTS, μmol trolox/100 g DW 2.593–3.108 (PSP) 0.974 0.148
0.713–1.195 (WSP)
Fe2+ chelate., mg EDTA/g DW 3.736–3.891 (PSP) 0.991 0.02
3.371–3.446 (WSP)
Classification SPA, kNN, RR 100%, PR 94.9%
SPA, LDA RR 100%, PR 97.4%
kNN, GA-PLS RR 100%, PR 97.4%
LDA, GA-PLS RR 100%, PR 100%
Table 11.

Overview of NIR Results for Pumpkins (Cucurbitaceae).

Investigated Parameter Concentration Range Chemometrics Data Ref.
Regression R2 Root Mean Square Error
β-Carotene—pumpkin, µg/g n.i. AC 92.0–96.0% [111]
Flesh 289–313
Peel 376–451
Seed n.i.
Total Carotenoid Content, mg/100 g—cucumber 3.86–410.68 +150 ppm ethephon 0.91 51.27 [289]
Total Flavonoid Content, mg/100 g—cucumber 26.12–349.84 +150 ppm ethephon 0.87 41.67
Classification zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber SNV, kNN, Bayes, DT, SVM accuracy rate 99% [290]
Table 12.

Overview of NIR Results for Legumes, Soybean.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Regression R2 Root Mean Square Error
Legumes Gross energy, kcal/g 4.149–4.511 1st der., PLS 0.966 0.0248 [292]
Fatty acids, % 0–63.18 MSC, SNV, MPLS 0.59–0.93 0.08–3.55
Mineral content, mg/100 g [293]
Mg (ground) 65.77–164.74 DT + 2nd der., MPLS 0.82 63.29
Ca (whole/ground) 23.87–123.74 2nd der./2nd der., MPLS 0.98/0.73 145.09/128.4
Fe (whole/ground) 6.98–38.07 MSC + 1st der/DT + 2nd der., MPLS 0.67/0.66 15.48/14.68
Soybean Moisture, % 8.16–18.10 SNV, PLS 0.80 1.55 [294]
Ash, % 4.32–6.14 0.63 0.38
Lipid, % 12.55–26.96 0.71 1.20
Protein, % 31.52–43.48 0.81 1.61
Carbohydrate, % 13.34–27.50 0.50 3.71
Dietary fibre, % 10.6–19.2 2nd der., PLS 0.80 0.86 [295]
Total fatty acid, mg/g 40.25–365.03 SNV, DTT, MPLS 0.94 8.76 [296]
Tocopherol, μg/g 39.57–860.81 raw, MPLS 0.83 35.28
Saponin, Abs/g 0.34–2.89 DT, MPLS 0.66 0.33
Total flavonoid, Abs/g 0.15–42.30 SNV, DTT, MPLS 0.91 1.27
Total isoflavone, μg/g 246.79–2511.65 SNV-DT, MPLS 1 121.58
Anthocyanins 0.01–1.97 SNV-DT, MPLS 0.8 0.13

5.4.6. Legumes (Fabaceae)

Using FT-NIR reflectance spectroscopy, the gross energy content of several legumes (beans—Phaseolus vulgaris L, peas—Pisum sativum L., lentils—Lens culinaris L and soybeans—Glycine max L) was studied. An adiabatic bomb calorimeter was used to determine the reference data [292].

The plant known as lentils (Lens culinaris Medicus) contains a high amount of minerals, including calcium, iron, and magnesium, and a low amount of fat, comprising mostly polyunsaturated fatty acids. Samples of whole and powered brown, green, black, and red lentils were analyzed for their fatty acid composition, fatty acid profile, and mineral content (Mg, Ca, Fe) to develop the NIRS approach. The results show that the fatty acid and mineral content of lentils may be accurately predicted using NIR spectroscopy [293].

Although they are categorized as legumes by nature, soybeans are distinguished from other “traditional″ legumes by their own special qualities. Soybeans have also been the focus of a great deal of research. Researchers have developed correlations with chemometric features suitable for quantitative evaluations through the development of several methodologies. The non-destructive measurement of soybean physiological processes, moisture, ash, carbs, lipids, proteins, dietary fibre, water-soluble proteins, fatty acids, anthocyanins, proanthocyanidins, isoflavones, tocopherol, and saponins may all be carried out using these correlations [294,295,296,297,298]. There are several alternative classification models that can be used to identify cultivars, group beans according to the temperature and length of storage, and distinguish between intact and damaged beans as well as Roundup Ready and regular beans [299,300,301] (Table 12).

5.5. Fruit

In the last two decades, the development of fast and non-destructive techniques for fruit quality analysis received considerable emphasis. The most investigated properties include soluble solids content (SSC), titratable acidity (TA), pH and bioactive compounds, as well as freshness, maturity, texture and spoilage, including external and internal effects, for example, the presence of the pathogen. These tests apply to both fresh and stored fruit. Nicolai et al. [302] were among the first to summarize NIR methods for fruit analysis during this period. Recently, several comprehensive reviews [303,304,305,306,307,308] were published on this topic. These reviews provide detailed NIR results for a variety of fruits, including apples, peaches, plums, mangoes, tangerines, kiwis, watermelons, pineapples, and more. The versatility of NIR techniques is proven by the fact that it is not only suitable for determining the previously listed internal properties. It offers a fast and non-destructive method for determining the vitamin C, polyphenol, total carotene, α-, β-, γ-carotene, lutein content of fruits, as well as for testing fruit freshness, ripeness and possible damage. Table 6 offer a detailed summary and comprehensive overview of the NIR methods used for the analysis of fruit samples. Detailed data on fruits are summarized in Table 13, Table 14, Table 15, Table 16, Table 17 and Table 18.

5.5.1. Pome Fruits (Maloideae): Apples (Malus) and Pears (Pyrus)

Apples are among the most consumed fruits globally, and the challenges posed by climate change and human environmental impact underscore the importance of sustaining quality apple production.

The evaluation of apple samples commonly includes measuring the water-soluble solids content (SSC), total acid content (TA), and the SSC/TA ratio to assess ripeness. For pear samples, a hardness test is also conducted. In addition to these intrinsic properties, Grabska et al. [309] summarized the various techniques and approaches used in Vis/NIR testing over the past five years, including authenticity, provenance, identification, counterfeiting, and quality control.

The models were developed using various variable selection procedures (synergy interval—si, genetic algorithm—GA, random frog—RF, Competitive Adaptive Reweighted Sampling—CARS, Successive Projection Algorithm—SPA) and regression methods (back-propagation artificial neural networks -BP-ANN, PLSR, PCR, MLR). Orthogonal signal correction (OSC) and various derivation steps were used as data processing [307,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324].

When recording spectra from an entire fruit, it is crucial to consider the impact of the spectrum recording’s location and orientation on the model’s accuracy. Compensation models using PLS and LS-SVM were developed to determine the SSC for each measurement position separately (local models) and for the combined dataset of all positions (global position model). Similar methodologies were applied to pear samples, where, besides SSC, firmness was also assessed. For this purpose, models were constructed using PLS, SVM, and Ridge Regression techniques [243,313,318].

The models have been created by including as many varieties of pears as possible. Convolutional Neural Network (CNN), PLS, and SVR approaches were used to create single-culture models and multi-species universal models. Multivariate universal models were built using the full spectra and important variables extracted by gradient-weighted class activation mapping (GradCAM) [248,325,326].

A notable application of NIR spectroscopy is in estimating α-farnesene and conjugated trienols (CTols; CT258 and CT281) levels. The synthesis and degradation of a-farnesene, e.g., to conjugated trienols (CTols) in apple skin, is closely related to surface scald, a physiological disorder that affects apples during and after storage. Using a PLS regression, a positive correlation was found for α-farnesene and CTols. A global model, independent of CTols type and year, was developed [327].

Data transfer between different spectrometers is an important technical issue, since this way the methods can be made device-independent. The transferability of calibration methods for the most important quality parameters (SSC, TA, pulp density, starch-iodine index, etc.) were investigated using a table-top (XDS) and hand-held ultra-compact spectrometer (MicroNIR) [328]. Others have created a model transfer platform with an internal quality terminal and an interactive cloud data system by developing an autoencoder (AE) neural network model [329].

Classification models were developed for apple samples based on SSC and TA [330] and using colour data to classify the maturity status (unripe, semi-ripe, ripe, or overripe). A hybrid artificial neural network simulated annealing algorithm (ANN-SA) was employed for the classification [331].

The ability to determine the quality of multiple species with a common calibration would be advantageous in certain situations. Based on the similar physical and chemical properties of apples; pears; peaches and apples; as well as pears and persimmons, universal models were developed for fruits’ SSC measurements. The effective wavebands of the three species were selected using moving window partial least squares (MWPLS) regression, there were identified using SPA and MLR model was developed [332,333].

During cultivation, harvesting, and storage, fruits are exposed to mechanical damage, microbial infections, and other types of damage that reduce fruit quality, increase the risk of fungal infections, and greatly affect food safety. Therefore, the timely identification of damaged fruits is essential. The classifications of apple samples of different varieties and freshness were investigated using different pattern recognition techniques (principal component analysis—PCA, partial least squares discriminant analysis—PLS-DA). Using variable importance in projection (VIP) variable selection to discriminate between fresh and stored apples, the model for both cultivar and freshness discrimination showed good classification performance [334,335].

He et al. [336] and Pandiselvam et al. [334] published a comprehensive summary of work on the detection of fruit damage using non-destructive techniques.

Bitter pit (BP), sunburn, as well as internal meat and seed browning processes are physiological disorders that develop mainly after harvesting and during storage. The NIR technique, combined with multivariate analysis (PLSR and PLS-DA and iPLS-DA), offers the possibility to predict the occurrence and severity of BP in apples, sun damage symptoms, and seed browning processes [314,317,337,338]. Discrimination models were created by combining different wavelength selection algorithms (CARS, CARS-SPA, MC-UVE and MC-UVE-SPA) and classification (SVM, ELM, kNN and LDA-kNN) methods to detect and predict apple fungal diseases [138]. Others have developed an LS-SVM model based on the transfer component analysis (TCA) method for this problem [339].

Models were developed to predict damage to pear samples caused by insect pests, enabling online, real-time detection [340]. (Table 13).

5.5.2. Stone Fruits (Prunoideae, Anacardioideae)

Stone fruits studies (cherries (Prunus avium L.), sour cherries (Prunus cerasus L.), peaches (Prunus persica L./Batsch), apricots (Prunus armeniaca L.), plums (Prunus domestica L.), and mangoes (Mangifera indica) primarily focus on the quantitative determination of dry matter, soluble solid content (SSC), titratable acidity (TA), pH, phenolic compounds, pectin, and parameters of flesh firmness and colour.

These basic qualifying parameters also enable the inference of fruit ripeness status, which is crucial for both harvesting and storage. During model development, various data processing procedures (Norris-Williams Smoothing (NWS), Savitzky–Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC); and Variable Sorting for Normalization (VSN), SNV, 1st der., 2nd der. and their combination), variable selection methods (Monte Carlo Uninformative Variable Elimination (MCUVE), SPA, CARS, regression coefficients (RC)) and linear and non-linear regression procedures (PLS), kernel partial least squares (KPLS) PCR, Sparse Partial Least Squares Regression (SPLSR), Sparse Partial Robust M Regression (SPRMR), BP-ANN, latent variables analysis (LVA) and independent component analysis (ICA) Feedforward Neural Network (FNN), Linear Deep Belief Network (LDBN, etc.), were applied [319,341,342,343,344,345,346,347,348,349,350,351,352,353].

The Kakadu plum (Terminalia Ferdinandiana) is an endemic plant in Australia that contains high concentrations of vitamin C, ellagic acid and other bioactive compounds. Due to its special content values also investigated the applicability of NIR spectroscopy to predict the vitamin C content of fruit [354,355].

For some fruits (e.g., mango), linear regression (PLS) was used, while for other fruits (e.g., peaches), non-linear models (LS-SVM) proved to be better [319,356].

In the case of stone fruits, they also tried to develop a universal model for determining SSC. In the case of peaches and nectarines, the model development was successful, but the model was no longer ideal for estimating plum samples [357].

Various types of hand-held devices were also developed to directly apply checks on the fruit plantations [351,358,359].

Storability, optimal storage conditions, packaging choices, and quality variable monitoring during storage, are also crucial for stone fruits [360,361,362,363].

Various classification models (PCA, PLS-DA, KNN, LS-SVM, SVM, LDA, QDA, MDA, CNN, etc.) were developed to distinguish fruits of different maturity states online before harvesting or throughout the processing chain [344,364,365,366,367,368].

These models facilitate variety identification [369] and geographical origin determination [370], as well as the detection of potential counterfeiting (e.g., pumpkin for apricot, or pumpkin for peach) [371] and the assessment of physical damage like bruising [334,336,372].

Maturity, harvest, and post-harvest technologies fundamentally determine the relatively short shelf life of plums which is often threatened by Monilinia spp. NIRS combined with an electronic tongue is suitable for the detection of M. fructigena fungal infection of plums and for the quantitative determination of this fungal contamination [373] (Table 14).

Table 13.

Overview of NIR Results for Pome Fruits (Maloideae).

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Processing, Regression R2 Root Mean Square Error
Apple Soluble solid content—SSC, % 11.0–14.0 GA 0.911 0.251 [310]
7.63–18.60 SNV, MSC, CARS-PLS 0.971 0.429 [313]
8.00–13.60 SGS, 2nd der., CARS-SPA-PLS 0.850 0.443 [318]
11.0–17.0 SGS, 1st der., SPA, MNLR 0.953 0.754 [323]
7.8–24.1 SNV, LS-SVM 0.73 0.7 [328]
9.13–15.66 CARS/PLS 0.9402 0.5079 [329]
Complex model with pear 11.1–15.2 2nd der., PLS 0.88 0.43 [332]
Complex model with pear and peach 10.20–15.60 SPA-MWPLS 0.96 0.46 [333]
MLR 0.96 0.46
Titratable acidity—TA, % 0.9–28.4 SNV, LS-SVM 0.68 0.89 [328]
Firmness, kg/cm2 1.5–12.7 SNV, LS-SVM 0.74 0.99
Starch-Iodine Index 2–10 SNV, LS-SVM 0.73 0.84
Visual ripeness index, VRPI n.i. LS-SVR 0.925 0.168 [324]
RPI n.i. PLS 0.777 0.191
IQI n.i. PLS 0.951 0.291
Streif index n.i. PLS 0.768 0.082
α-farnese, μmol/m2 15–1816 NCL, PLS 0.81–0.92 139 [327]
CTols CT258 14–502 1st der. BCAP, PLS 0.90; 0.94 59–60
CT281 1–450 0.91; 0.78
Maturity estimation SSC, TA, firmness, anthocyanin SGS, SNV, MSC, SLS, 1st der. 2nd der.
PLS, PCR, SMLR, GA-PLS
0.22–0.97 n.i. [319]
Internal flesh browning 93 good, 203 defect PLS 0.83 0.63 [314]
Sunscald 161 shaded and sun-exposed
100 mild sun damaged
MSC, 2nd der., PLS, iPLS-DA 0.454
0.594-
0.211
0.317
[317]
Classification Internal flesh browning LDA accuracy >95% [314]
Damage Bruise, Mouldy core
Sunburn
Internal browning
PLS-DA, SPA-PLS, SELFS, iPLS-DA
LDA
accuracy > 90%; 92%
R2cv = 0.59
accuracy 90%
[336]
Maturation level—colour ANN/SA accuracy 100% [331]
Variety; Freshness; Variety, freshness PCA, VIP, PLS-DA misclassification 0%; 5.8%; 2.0–3.9% [335]
Bitter bit (BP) 269 BP
719 non BP
PLS-DA accuracy 60–80% [337]
Origin TCA, LS-SVM accuracy 90.91% [339]
Fungal infection SNV, CARS, SPA, KNN, LDA, LS/SVM, RF accuracy 98.75% [138]
Pear Soluble solid content—SSC, % 8.6–13.8 PLS 0.912 0.662 [311]
8.6–11.3 SGS, SNV, 1st der., var.sel. PLS 0.58 0.65 [312]
10.8–14.6 SGS, PLS 0.92 0.41 [315]
8.6–13.6- aver. spectra, FWs PLS 0.8611 0.6314 [316]
9.8–16.8 SGS, MSC, siPLS 0.9657 0.2265 [320]
13.4–16.9 PCA, Si-GA-PLS, 0.9406 0.165 [321]
7.20–19.5 SpectraNet–32 0.58 1.08 [322]
8.2–16.5 SNV, 2nd der., SVM 0.71 0.7 [338]
11.3–18.5 OSC-PLS 0.85 0.46 [374]
11.3–18.5 OSC-MLR 0.86 0.46
6 cultivars 10.2–25.0 Grad-CAM, SVR, CNN n.i. 0.33–1.64 [326]
Complex model with apple 9.2–13.8 2nd der., PLS 0.88 0.43 [332]
Complex model with apple and peach 10.90–16.90 SPA-MWPLS, MLR 0.96
0.96
0.46
0.46
[333]
Dry matter 11.4–21.8 SGS, SNV, 1st der., var.sel. PLS 0.65 1.06 [312]
Firmness 4.2–11.3 PLS 0.854 1.232 [311]
28.4–127.1 PCA, Si-GA-PLS, 0.9119 5.5003 [321]
5.0–71.0 SNV, SVM 0.68 7.66 [338]
15.00–35.86 PLS 0.58–0.845 2.65–3.98 [325]
1.9–71.2 OSC-PLS 0.68 8.18 [374]
1.9–71.2 OSC-MLR 0.56 9.28
Maturity estimation SSC, firmness, lignin cont. SGS, SNV, MSC, OSC, 1st der., 2nd der., siPLS, UVE, MS-UVE-SPA, PLS, MLR, LSSVM, NIPALS 0.61–0.96 n.i. [319]
Classification internal browning PLS-DA sensitivity 76% [338]
Insect-affect SGS
CBAM-CNN
accuracy 92.71% [340]
Table 14.

Overview of NIR Results for Stone Fruits (Prunoideae, Anacardioideae).

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Processing, Regression R2 Root Mean Square Error
Peach Soluble solid content—SSC, % 7.8–14.5 ICA- LS-SVM 0.9537 0.4155 [341]
11.20–17.0 SNV, PLS 0.849 0.44 [345]
≈7–23 2nd der., PLS 0.754–0.951 0.566–0.695 [348]
7.5–13.4 CARS-LDBN 0.9346 0.4409 [353]
7.30–14.43 PCA, BP-ANN 0.90 0.691 [356]
13.0–29.7 2nd der., PLS 0.726–0.89 0.612–0.792 [358]
6.3–17.6 MSC, SNV, PLS 0.45 1.04 [365]
7.0–16.0 raw spectra, CARS, RC, PLS 0.7747 0.6915 [366]
PCR 0.7237 0.7576
static 10.1–15.2 SPRMR 0.987 0.161 [368]
online 0.967 0.244
Titratable acidity—TA, % 0.53–1.02 PLS 0.4267 0.101 [345]
pH 4.12–4.88 ICA- LS-SVM 0.9638 0.0497 [341]
3.69–4.23 PLS 0.521 0.084 [345]
Dry matter, % ≈7–25 2nd der., PLS 0.786–0.945 0.542–0.734 [348]
17.67–31.62 2nd der., PLS 0.67–0.725 0.687–0.911 [358]
Phenols, mg/100 g SNV, PLS [345]
Flesh 16.29–49.71 0.368 1.62
Skin 43.81–159.80 0.681 15.7
Pectin, μg/g n.i. KPLS 0.628 0.069 [349]
Flesh colour, °hue 68–91 SGS, PLS, MLR 0.92 1.35 [364]
Firmness (flesh), N ≈1–10.5 2nd der., PLS 0.039–0.656 0.848–1.368 [348]
8.93–34.10 LOGSIG, MSC, BP-ANN 0.453 3.844 [356]
4.9–111.7 SGS + 1st der. PLS 0.40 13.2 [365]
Complex model with pear and peach 6.30–12.00 SPA-MWPLS
MLR
0.96
0.96
0.46
0.46
[333]
Maturity estimation SSC, pH, TA, firmness SGS, VN, SNV, MSC, DT, 2nd der.
PLS, PB ANN, SVM, LS-SVM
0.73–0.98 n.i. [319]
Nectarine Soluble solid content—SSC, % ≈9–23 2nd der., PLS 0.919–0.938 0.589–0.614 [348]
−17 raw spectra, PLS
PCR
0.8473
0.8249
0.77390.7228 [366]
Dry matter, % ≈9–22 2nd der., PLS 0.928–0.984 0.65–0.7 [348]
Firmness (flesh) ≈1–11 2nd der., PLS 0–0.496 1.032–1.537
Apricot Soluble solid content—SSC, % ≈4.5–20 2nd der., PLS 0.759 1.983 [348]
Dry matter, % ≈9–20 2nd der., PLS 0.811 1.168
Firmness (flesh) ≈1.8–10 2nd der., PLS 0.438 1.379
Cherry Soluble solid content—SSC, % 8.7–30.3 PLS
LS-SVM
0.97
0.98
1.15
1.27
[347]
8.7–22.4 SNV + 1st der., PLS 0.897 0.99 [350]
Titratable acidity—TA, % 0.39–3.04 1st der., PLS 0.938 0.19
Total anthocyanin, % 0–164.1 SNV + 1st der., PLS 0.902 16.9
Cyanidin, mg/100 g 3.52–80.44 MSC, PLS 0.83 20.58 [343]
Maturity index (SSC/TA) 3.74–36.14 1st der. 0.939 1.59 [350]
Dry matter, % 14.70–36.01 SNV + 1st der., PLS 0.939 1.46
Classification maturity degree QDA accuracy 98.44%
bruise degree LS-SVM accuracy 97.3% [372]
Plum Soluble solid content—SSC, % 12.43–16.99 PCA, PLS 0.9456 0.456 [363]
7.90–19.40 SNV + 1st der., PLS 0.965 0.61 [352]
≈18–24 2nd der., PLS 0.931 0.377 [348]
powder
pure
4.7–6.8
5.3–6.8
2nd der., PLS 0.70
0.72
0.20
0.58
[354]
Titratable acidity, % 0.07–0.25 0.7702 0.0183 [363]
0.50–1.70 SNV + 1st der., PLS 0.949 0.07 [352]
pH 3.42–4.32 0.8299 0.1010 [363]
Firmness, N 2.15–5.89 0.825 0.532
≈1.5–5.5 2nd der., PLS 0.336 0.459 [348]
Maturity index, MI = SSC/TA 83.52–117.6 0.7663 15.6 [363]
5.20–38.80 SNV + 1st der., PLS 0.951 1.50 [352]
Colour (L*) 29.75–53.83 0.867 3.02 [363]
Dry matter, % 16.32–28.61 SNV + 1st der., PLS 0.882 0.65 [352]
≈18–23 0.881 0.498 [348]
Moister, % 2nd der., PLS [354]
powder 81.4–86.0 0.71 0.59
pure 81.2–86.0 0.86 0.68
Vitamin C, mg/100 g 227.4–28,954 2nd der., PLS 0.91 4773 [355]
Plum Classification accuracy 100% [352]
Mature/immature TA MDA, QDA
SSC LDA, MDA, QDA
MI LDA, MDA, QLDA
Cultivars LDA, MDA, QDA
Monilia fructigena injury; intact PCA/LDA accuracy 91.67% (24 °C); 85.71% (24 °C) [373]
Mango Soluble solid content—SSC, % 6.90–21.30 SNV, PLS 0.81 1.07 [351]
3.8–21.0 SNV + 1st der., PLS 0.87 1.39 [359]
19.36 ± 1.31 PLS 0.88 0.90 [342]
7.7–26.3 SNV, 1st der, PLS 0.9 1.2 [344]
Titratable acidity—TA, % 0.09–4.60 raw spectra, PLS 0.82 0.36 [351]
0.07–3.03 MSC, 2nd der., PLS 0.74 0.38 [344]
pH 2.73–6.94 SNV, PLS 0.80 0.45 [351]
Firmness, N 0.80–56.30 SGS + 1st der., PLS <0.8 -
Dry matter—DM, % 9.68–18.69 SNV, PLS <0.80 -
11.3–22.1 SNV + 1st der., PLS 0.84 0.88 [359]
15–25 2nd der., MLR 0.92 1.48 [367]
Firmness, N 4.94–37.10 MSC, 2nd der., PLS 0.72 4.22 [344]
Textura [346]
Average firmness, N/mm 1.19–4.4 raw spectra, PLS 0.70 0.56
Toughness, N/mm 20.39–65.69 SLS, PLS 0.53 1.03
Rupture force of peel, N 8.47–22.12 raw spectra, PLS 0.75 2.37
Rupture distance, mm 4.02–8.75 raw spectra, PLS 0.26 1.25
Penetration force in the pulp, N 0.53–3.32 SLS, PLS 0.71 1.98
Penetration energy in the pulp, N/mm 2.12–13.24 SLS, PLS 0.71 0.50
Maturity estimation SSC, DM, TA, firmness SGS, SNVMSC, EMSC, 1st der., 2nd der., PLS, MLR, SVM, ANN, PCR 0.50–0.97 n.i. [319]
Ripening index 0.8–6.8 MSC, 2nd der., PLS 0.8 0.8 [344]
Classification Maturity based on dry material KNN/SVM accuracy 88.2% [367]
Ripening status based on SSC DA correctly classified: over ripe 81.1%
correctly classified: ripe 80%
correctly classified: half ripe 59.6%
correctly classified: unripe 87.5%
[344]

5.5.3. Soft Fruits

The term “berry fruits″ does not correspond to a classical botanical classification. Based on the shape of the fruits, we classify the strawberries (Fragaria x ananasa), currants (Ribes rubrum L, R. nigrum L.), blackberries (Rubus caesius L.), raspberries (Rubus ideus L.), blueberries (Vaccinium ocycoccos L.), and kiwifruit (Actinidia chinensis) into one group.

Strawberries are the most grown berry in the world. Its characteristics are the SSC value and the TA, from which the ripeness can also be inferred; the bright red colour, the characteristic texture, and, finally, its compounds with bioactive, antioxidant properties (vitamin C, anthocyanin and phenolic acid). Given that it is a very fragile fruit, it is advisable to use NIR estimation models for rapid quality control [303,304,308,319,375,376,377,378,379,380]. Research encompassing various genotypes has shown that the spectral data of these genotypes do not differ, suggesting that these models are universally applicable [334,381,382].

Rapid monitoring of colour, SSC, TA content, textural changes, and sensory shelf life is crucial for this perishable fruit during refrigerated storage [383,384,385].

Strawberries have a brief shelf life and are highly prone to tissue infections, particularly Botrytis cinerea. A correlation has been observed between the SSC value of the fruit and its vulnerability to B. cinerea, allowing these models to be utilized for screening purposes [382].

An NIR estimation model was developed to determine the SSC and anthocyanin content of fresh raspberry samples [386].

During the near-infrared spectroscopic analysis of blueberries, non-invasive detection models based on NIR spectroscopy are often limited and unstable due to biological variability factors (variety, season, changes from harvest to sale, etc.). The detection accuracy of the SSC value of packaged and unpackaged products can be improved by using global modelling procedures and appropriate data processing and neural networks [387,388,389].

Blueberry leaves are very rich in bioactive compounds. Therefore, special attention has been paid to the NIR estimation of total phenol (TPC), total flavonoid (TFC), and total antioxidant capacity (TAC) [390,391]. Classification models based on NIR spectra were prepared to categorize blueberries by texture (hard and soft) and to detect foreign substances in frozen products [392,393].

For kiwi fruit, key selection and pre-harvest grading characteristics include soluble solids content (SSC), flesh firmness (FF), dry matter (DM), and for yellow-fleshed varieties, flesh colour. The NIR technique offers the opportunity to develop accurate models for predicting internal quality characteristics [394,395,396].

The balance between soluble solids in the grape berry and titratable acidity and phenolic ripeness, such as anthocyanin concentration, is a key factor in the production of quality wines. NIR estimation models are useful in monitoring both technological maturity parameters and anthocyanin concentration and grape berry composition [397]. The reliability of models that can be applied directly in the vineyard is disturbed by changes in temperature and sunlight (due to their effect on the spectra).

Developing a global model can correct these influences, so the handheld NIRS device is suitable for outdoor use to assess the quality of the grape cluster [398] (Table 15).

Table 15.

Overview of NIR Results for Soft Fruits.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Regression R2 Root Mean Square Error
Strawberry Soluble solid concentration—SSC, % 4.8–9.9 SGS, 1st der., LOCAL PLS 0.83 0.70 [375]
6.1–11.0 SVM 0.69–0.85 0.98–1.21 [376]
5.09–7.37 1st der., PLS 0.52 0.7926 [377]
3.0–9.5 SNV, PLS 0.96 0.291 [379]
reflectance 7.50–13.70 SGS, SNV, PLS 0.773 0.633 [380]
transmittance 7.50–13.30 SGS, MSC, PLS 0.906 0.467
Titratable acidity, % 0.68–0.96 1st der., PLS 0.3647 0.1140 [377]
0.387–0.887 SNV, PLS 0.91 0.032 [379]
Firmness, N 236–826 SGS, 1st der., LOCAL PLS 0.54 0.11 [375]
external 0.75–1.53 1st der., PLS 0.282 0.3325 [377]
internal 0.20–0.44 0.1688 0.1075
reflectance
transmittance
0.97–3.86 SGS, PLS 0.78
0.81
0.43 [378]
Moisture, % 87.7–92.7 SVM 0.64–0.77 0.89–1.34 [376]
Brittleness, N 0.81–3.40 SGS, PLS 0.77
0.78
0.33
0.33
[378]
Total anthocyanin content, mg/kg 803–2355 SNV, PLS 0.9 132.3 [379]
Chroma colour 33.98–49.11 SNV, PLS 0.93 0.819
Lightness 28.25–54.03 SNV, PLS 0.92 1.71
Classification intact, two varieties LOCAL, PLS-DA correct class. rate57%, 78% [383]
storage shelf-life CARS-PLS-DA (0.05; 0.1; 0.15 m/s) 95.1; 97.4; 93.3% [384]
Raspberry Soluble solid concentration—SSC, % 7.1–16.0 PLS 0.77 0.76 [386]
Anthocyanin, mg/L 16.0–184.0 SNV, PLS 0.77 12.57
Blueberry Soluble solid concentration—SSC, % 8.80–16.90 SGS, MSC, PLS 0.744–0.974 0.383–3.032 [387]
three cultivars 9.0–16.90 PLS [388]
global cultivar 0.874–0.935 0.483–0.639
global season 0.83–0.951 0.442–0.494
global variation 0.861–0.950 0.48–0.634
PE packed 6.9–17.8 BP-PLS 0.947 0.414 [389]
SNV, UVE-CARS-IRIV, PLS 0.758 0.883
Classification four cultivars SVM accuracy: 100, 93.3, 95.6, 100% [387]
Hardness soft-hard random accuracy 78% [393]
Total phenol concentration, mg/100 g 39.6–272.8 PLS 0.98 6.9 [391]
Total flavonoid concentration, mg catechin/g 41.2–269.1 PLS 0.97 6.7
Total antioxidant activity, mmol Trolox/g 22.6–124.8 PLS 0.98 2.9
Berry fruit Total phenol concentration, mg/100 g 39.4–479.5 PLS 0.98 35.48 [390]
Antioxidant activity, DPPH mmol/100 g 1.7–10.1 PLS 0.99 2.2
Kiwifruit Soluble solid concentration—SSC, % 13.18–15.68 SNV, PLS 0.93 0.259 [394]
4.00–19.70 PLS 0.94 0.97 [396]
pH 3.45–4.13 SNV, PLS 0.94 0.076 [394]
Firmness (flesh), N 0.12–10.87 PLS 0.866 9.41 [396]
Flesh hue, °H 94.96–115.60 PLS 0.843 1.82
Dry matter—DM, % 13.526–18.757 GA-siPLS 0.9020 0.5315 [395]
13.62–21.77 PLS 0.854 0.64 [396]
Maturity estimation DM, SSC, TA, Firmness SNV, MSC, VN, SGS, 2nd der.
PLS, LDA, SVMR, LSSVM, MLR, PCR
0.73–0.98 n.i. [319]
Grape Soluble solid concentration—SSC, % 13.8–23.6 2nd der., PLS [398]
EPO + GLSW corr. for temperature interference 0.90–0.91 0.96–0.98
EPO correction for sun 0.98 0.50
Maturity estimation Phenolic comp, TA, pH, colour, BrimA SGS, SNV, MSC, DT, 1st der., 2nd der., PLS, MPLS, MLR, LS-SVM 0.6–0.982 n.i. [319]

5.5.4. Citrus Fruits

NIR testing across various citrus fruits, such as lemons (Citrus × limon), oranges (Citrus sinensis), mandarins (Citrus reticulata), limes (Citrus aurantiifolia), and grapefruits (Citrus × paradisi), is aimed at assessing ripeness, like other fruits. The goal is to swiftly and non-destructively determine soluble solid content (SSC), pH, titratable acidity (TA), and the maturity index derived from these measurements [303,304,308,319,334,399,400,401,402].

The peel thickness of citrus fruits can pose challenges during spectral recording. Investigations have been conducted to identify the optimal location for spectral fixation, considering the stem, equator, and navel positions. While peel thickness can interfere with the spectral data collection of the flesh layer, the prediction model’s accuracy and robustness can be enhanced by integrating spectral data from multiple regions. Hence, more focus on the fusion of multi-information sets is warranted to develop a practical model. Citrus fruits with different peel thicknesses are the primary subjects of the NIR penetration capacity analysis. It was discovered that permeability is influenced by the shell’s composition in addition to its thickness. By prolonging the integration period, the penetration potential can be somewhat increased. Compared to long-wave near-infrared light (LWNIR), higher-energy short-wave near-infrared light (SWNIR) penetrates more deeply. Furthermore, SWNIR is a better option for evaluating the fruit’s internal quality because the peel’s absorption peaks are primarily in the LWNIR range [403,404].

The non-destructive method has also been successfully employed to detect surface damage and fungal infections in citrus fruits [336].

Postharvest rind pitting (RP) is a progressive physiological disorder of the rind that affects citrus fruits during postharvest storage, diminishing their external quality. This disorder manifests 3–5 weeks after harvest, complicating its detection during the grading and sorting processes on commercial packing lines. Principal component analysis has effectively differentiated fruits based on canopy position and their susceptibility to rind pitting disorder. Vis/NIR spectroscopy, in conjunction with chemometric analysis, is suggested as an alternative method for clustering fruits according to canopy position, which is beneficial for identifying fruits with a higher risk of RP, as the incidence of RP is greater in fruits from the outer canopy [405] (Table 16).

Table 16.

Overview of NIR Results for Citrus Fruits.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Regression R2 Root Mean Square Error
Orange Soluble solid content—SSC, % 6.80–15.30 LOCAL; MPLS 0.81; 0.75 0.80.; 0.97 [400]
pH 3.01–4.15 LOCAL; MPLS 0.25; 0.15 0.16; 0.18
Titratable acidity-TA, % 0.36–1.02 LOCAL; MPLS 0.45; 0.47 0.11; 0.11
Maturity index, SSC/TA 8.24–40.03 LOCAL; MPLS 0.65; 0.67 3.56; 3.70
BrimA 4.29–13.31 LOCAL; MPLS 0.82; 0.80 0.85; 0.89
Mandarin Soluble solid content—SSC, % 9.95–15.65 LOCAL; MPLS 0.57; 0.39 0.71; 0.84
pH 2.08–3.80 LOCAL; MPLS 0.74; 0.74 0.11; 0.11
Titratable acidity-TA, % 0.68–2.15 LOCAL; MPLS 0.76; 0.65 0.13; 0.18
Maturity index, SSC/TA 5.41–17.27 LOCAL; MPLS 0.79; 0.68 1.13; 1.38
BrimA 2.93–10.33 LOCAL; MPLS 0.75; 0.68 0.70; 0.79
Orange, mandarin Soluble solid content—SSC, % 6.8–15.65 LOCAL; MPLS 0.78; 0.72 0.86; 0.95
pH 2.08–4.15 LOCAL; MPLS 0.72; 0.64 0.15; 0.17
Titratable acidity-TA, % 0.36–2.15 LOCAL; MPLS 0.84; 0.75 0.14; 0.18
Maturity index, SSC/TA 5.41–40.03 LOCAL; MPLS 0.77; 0.72 2.98; 3.52
BrimA 2.93–13.31 LOCAL; MPLS 0.78; 0.73 0.84; 0.94
Citrus species Soluble solid content—SSC, % 5.2–14.7 Full-ANN 0.823 0.560 [402]
Stem, Equator, Navel 10.70–16.90 MN, PLS 0.8424 0.5901 [403]
Equator, Navel 10.80–16.90 0.8507 0.6015
Classification surface damage LDA accuracy 97.80% [336]
fungal infection SVM accuracy > 90.8%
Lemon Soluble solid content—SSC, % 6.32–9.71 PLS 0.84 0.42 [401]
Titratable acidity—TA, % 4.74–7.29 PLS 0.72 0.45
Grapefruit Total antioxidant capacity, mgAS/g n.i. normalization, PLS 0.71 0.17 [405]
β-carotene, n.i. SNV, PLS 0.99 0.17
Total carotene n.i. SNV, PLS 0.91 0
Chlorophyll-a, μg/g n.i. SNV, PLS 0.86 2.69
Chlorophyll-b, μg/g n.i. SNV, PLS 0.92 0.01
Dry matter, % n.i. SNV, PLS 0.88 0.01
Carbohydrates n.i. SNV, PLS
sucrose 0.79 0.03
glucose 0.88 0.02
fructose 0.92 0.03
Rind pitting n.i. normalization, PLS 0.89 5.21 × 10−4

5.5.5. Pumpkin Fruits (Cucurbitaceae)

Melon (Cucumis melo L.) and watermelon (Citrullus lanatus), which are part of the cucurbit family, originate from Asia and Africa, respectively (watermelon is considered a vegetable in terms of cultivation technology).

NIR models are basically total soluble solids content (TSS, an indicator of sweetness), acidity (an indicator of sourness), dry matter (sometimes an indicator of maturity), moisture content (an indicator of juiciness), lycopene content [304,319,406,407,408,409] texture properties, e.g., are aimed at a quick and non-destructive determination of strength and toughness [410]. A study was carried out over two years for cut and intact melons. For cantaloupe, the model derived from two years of data for intact samples was used, whereas for watermelon, the model based on a single year’s data gave superior statistical attributes [411]. The possibilities of rapid measurement of water activity and colour changes during the solar drying of melon slices were also investigated. [412]. Due to the fruits’ thick skin, finding the optimal measuring position is crucial. The mesocarp’s TSS is highest around the equator of the fruit and increases towards the seed cavity, while the inner mesocarp’s TSS levels decrease towards both the proximal and distal ends of the fruit [413,414].

Although melon rinds are not consumed, the determination of surface pesticide residues is a key task. A one-dimensional convolutional neural network, with a deep feature fusion structure to capture multi-scale spectral information, has a better identification of pesticide residues on the melon surface. The model is suitable for answering the question “Does it contain pesticide residues or not″, but it was not accurate for estimating imidacloprid and pyraclostrobin residues [415] (Table 17).

5.5.6. Tropical Fruits

“Tropical fruits″ primarily include pineapple (Ananas), avocado (Persea americana), papaya (Carica papaya L.), banana (Musaceae), passion fruit (Passiflora edulis), and pomegranate (Punica granatum L.). Numerous summary articles have presented NIR measurement models for these fruits, assessing attributes such as total soluble solids content (TSS), titratable acidity (TA), maturity index (TSS/TA), pH, firmness, dry matter, vitamin C, polyphenols, pigments, starch content, and colour [302,303,305,319,334,416,417,418,419,420].

In the case of pineapple, a well-liked tropical fruit, spectra recorded from the whole fruit and its slices are used to determine SSC and nitrate content, thereby aiding quality control and sorting processes [421,422,423]. Additionally, a NIR model for passion fruit was created to measure soluble solids content (SSC), titratable acidity (TA), ascorbic acid content (ASC), ethanol concentration (EtOH), peel firmness (PF), and pulp percentage (PP) [424].

Determining the optimal harvest maturity for avocados is crucial. Traditionally, this has been carried out by destructively measuring the oil, dry matter, or moisture content of the mesocarp. However, the Vis-NIR model, introduced as a non-destructive alternative [425,426], has changed this approach. Similarly, for pomegranates, a Vis-NIR model using TSS, pH, and hardness as reference values for quality assessment was developed [427], and for papayas, SSC and starch values were used [428]. In addition to chemical and microbiological parameters, a principal component analysis (PCA) was utilized on the second derivative of the spectra to reveal molecular changes during storage. This analysis clearly distinguished between “fresh″ and “old″ samples, and established a stability time that marks the onset of freshness loss at various temperatures [429] (Table 18).

Table 17.

Overview of NIR Results for Pumpkin Fruits (Cucurbitaceae).

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Regression R2 Root Mean Square Error
Watermelon Soluble solid content—SSC, % 5.3–13.7 SNV, PLS 0.707 1.4 [407]
cut 2.00–11.50 2nd der., MPLS 0.84–0.88 0.61–0.65 [411]
intact 1st der., PLS 0.72–0.76 1.89–2.05
Lycopene, mg/kg 2.65–151.75 SNV, PLS 0.805 16.19 [407]
β-carotene, mg/kg 0.19–9.39 SNV, PLS 0.737 0.96
Melon Soluble solid content—SSC, % SGS, MSC, CARS, PLS [408]
stylar end 5.5–13.9 0.72 0.82
equatorial 5.7–13.6 0.53 1.03
cut 4.00–14.00 1st der., PLS 0.85 0.49 [411]
intact 2nd der., PLS 0.65 0.93
Calyx 5.70–15.70 Smoothing, PLS 0.89 1.05 [414]
Equator 5.30–14.85 Smoothing, normalization, PLS 0.91 0.86
Stem 5.10–13.15 Smoothing, normalization, PLS 0.87 0.95
Calyx 5.70–15.70 Smoothing, PLS 0.93 0.85
full spectra MC-UVE-SPA, LS-SVM or CARS LV-SVM 0.91 0.96
Variable selection MC-UVE-SPA, MLR 0.91 0.95
Texture—using intact fruit spectra [410]
Initial firmness, N/mm 0.22–11.17 MSC, PLS 0.387 2.13
Ruprure force, N 1.05–18.05 Min-max normalization, PLS 0.850 1.70
Average firmness, N/mm 0.22–8.82 SNV, 1st der., PLS 0.502 1.55
Rupture distance, mm 0.31–9.17 Min-max normalization, PLS 0.561 1.52
Toughness, N/mm 0.18–36.28 SLS, PLS 0.674 3.85
Average penetrating force, N 2.59–18.77 Constant offset elimination, PLS 0.845 1.59
Penetrating energy, N/mm 446.61–336.46 2nd der., PLS 0.749 35.40
Moisture, % 89.4 → 17.8 SNV, 2nd der., PLS 0.99 2.49 [412]
Water activity 0.9994 → 0.4666 SNV, 2nd der., PLS 0.97 0.03
Colour SNV, 2nd der., PLS
a* 7.92 → 22.48 0.91 1.13
b* 24.61 → 48.78 0.86 2.49
C* 25.81 → 53.81 0.87 2.52
Browning index 64 → 150 SNV, 2nd der., PLS 0.86 11.00
Classification pesticide residue 1D-CNN accuracy 91.67–95% (validation)
accuracy 90.00–95.85 (test set)
[415]
Table 18.

Overview of NIR Results for Tropical Fruits.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Processing, Regression R2 Root Mean Square Error
Pineapple Soluble solid content—SSC, % 11.90–18.60 MSC, PLS 0.854 0.842 [422]
7.0–18.5 MSC, PLS 0.88 1.04 [423]
Maturity index (colour based) 0.55–1.20 1st der., PLS 0.97 0.034 [423]
Nitrate level, mg/kg [421]
Individual spectrum model 3.71–51.07 MSC, SNV, 1st der., PLS <0.90 n.i.
Average spectrum model 6.56–28.27 1st der., PLS 0.94 2.08
Classification organic and inorganic fruits MSC, kNN or MSC, LDA accuracy 100% [422]
Passion fruit Soluble solid content—SSC, % 13.70–20.07 2nd der., PLS 0.908 0.76 [424]
Titratable acidity—TA, % 0.38–2.85 2nd der., PLS 0.68 0.26
Ascorbic acid, mg/100 g 14.20–27.67 2nd der., PLS 0.663 2.46
EtOH, g/L 0.60–2.94 2nd der., PLS 0.849 0.25
Peal firmness, N 4.85–22.76 2nd der., PLS 0.829 2.38
Pulp percent, % 43.55–82.31 2nd der., PLS 0.883 3.76
Avocado Dry matter, % 19.4–34.2 PLS 0.75–0.89 1.14–2.60 [425]
14.15–39.59 PLS 0.95 2.49 [426]
Moisture content, % 65.8–80.6 PLS 0.84–0.92 1.14% [425]
63.89–85.85 PLS 0.95 2.49 [426]
Bananas Soluble solid content—SSC, % n.i. PLS 0.99 0.80 [303]
6.47–24.10 PLS 0.81 3.91 [420]
mesocarp 11.07 ± 7.79 PLS 0.97 1.77 [342]
ripe, over ripe 18.62 ± 2.06 0.79 0.54
pH 5.23–6.31 PLS 0.83 n.i. [303]
n.i. PLS 0.69 0.36 [420]
Dry matter, % n.i. MLR 0.83 n.i. [303]
mesocarp 24.60 ± 1.53 PLS 0.88 0.73 [342]
ripe, overripe 24.53 ± 1.58 0.88 0.54
Pomegranate Soluble solid content—SSC, % 18.42–19.2 SNV, median filter, 1st der., MC, PLS 0.94 0.21 [427]
pH 3.42–3.65 SNV, median filter, 2nd der., MC, PLS 0.86 0.069
Firmness, N 38.5–41.97 SNV, median filter, 1st der., MC, PLS 0.94 0.68
Papaya Soluble solid content—SSC, % 3.47–8.9 MSC, PLS 0.9 0.12 [428]
Starch, mg/g 0.3–5.31 MSC, 1st der., PLS 0.9 0.12

5.6. Luxury Items

Coffee, tea and chocolate are sought-after luxury items. They do not belong in our regular diet; thus their intake is insignificant. When ingested in sufficient amounts, the alkaloids and polyphenol chemicals included in them also have a positive physiological impact.

It is no accident that most research on luxury products concentrate on identifying these vital physiological components. Thanks to the evolution of the instrumental analytical methods employed as a reference, today, e.g., not only can we establish the total polyphenol content, but we can also identify them individually and estimate their number using the NIR spectroscopic approach.

Since these are expensive foods, it is crucial to identify their origin (e.g., Arabica or Robusta in the case of coffee), their location (varying quality depending on geological origin), and any potential adulteration.

Most publications from 2004 to 2014, as Table 19, Table 20, Table 21 and Table 22 illustrate, focused on the analysis of different luxury goods.

5.6.1. Tea

Teas, derived from Camellia sinensis, are complex products whose quality and sensory attributes are influenced by a variety of factors such as geographical origin, processing methods, and storage conditions. Generally, there are huge amounts of types and brands of teas in the market, and the price and quality grading are distributed in a large range [430]. After being plucked, the fresh tea leaves are sent immediately to tea factories for manufacturing. Due to the different ways of processing, especially the extent of oxidation, tea is usually divided into three basic types: green tea, oolong tea, and black tea. Alternatively, with the combination of the ways of processing and the characteristic quality of manufactured tea, tea is classified into six types: green tea, yellow tea, dark tea (containing brick tea and pu-erh tea), white tea, oolong tea, and black tea [431].

A wide range of analytical methods and standards are available for testing the quality parameters of tea. The importance of the measurements lies in the fact that the above-mentioned factors determine the price of tea to a large extent. Therefore, NIR spectroscopy has proven highly effective in assessing key quality parameters, including moisture content, polyphenol concentration, caffeine content, and the levels of other bioactive compounds, such as catechins and theanine. In addition to conventional desktop instruments, several studies have examined the applicability of handheld NIR spectrometers.

Based on the reviewed publications, the most frequently studied types of tea were green and black teas. Numerous studies focused on the classification of teas, with a particular emphasis on distinguishing tea types or their geographical origin. For pre-processing the spectral data, the most commonly used technique was SNV correction. Both linear and non-linear mathematical methods were applied for modelling, including PLS-DA, SVM, SIMCA, kNN, and ANN. In all cases, the accuracy of the models exceeded 83%.

Another key area of study was the characterization of teas in different oxidation states through their chemical composition. The most important parameters in tea characterization were sensory properties, caffeine content, total polyphenol content, various catechins, pigments (e.g., thearubigins, theaflavins), and theanine concentration. For quantitative estimation, a variety of chemometric methods were employed, such as PLSR, SVMR, MLR, and PCR. During method development, variable selection techniques were often used, including GA, SPA, CARS, LTSA, RF, ACO, IVSO, FPA, IRIV, IVISSA, and BOSS (Table 19).

Table 19.

Overview of NIR Results for Tea.

Sample Investigated Parameter Concentration Range Chemometrics Data References
Pre-Treatment, Regression R2 Root Mean Square Error
Green tea TAC, μmol Trolox/25 μg leaf 14.53–35.79 PCR 0.76 1.81 [432]
Ranking 0–58 SNV, MC, 2nd der., PLS 0.99 3.05 [433]
Sensory 61–94 SNV, BP AdaBoost 0.77 6.0807 [434]
Classification of origin 1st der., PLS 100.0% [435]
Moisture, % 6–76.75 Z-score, PCA-SVM 0.97 0.046 [436]
Whole leaves Caffeine, μg/kg n.i. 1st der., PLS 0.96 0.18 [437]
Catechin, μg/kg 0.14–1.08 GA-PLS 0.98 0.99 [438]
n.i. SPA-PLS 0.931 1.002 [439]
CG n.i. SPA-PLS 0.892 0.487
EC, μg/kg n.i. SNV, PLS 0.61 0.071 [437]
n.i. SPA-MLR 0.955 1.033 [439]
0.15–0.39 siPLS 0.91 0.78 [438]
EGCG, μg/kg n.i. 1st der., PLS 0.85 0.54 [437]
7.65–14.30 siPLS 0.97 0.85 [438]
n.i. SPA-PLS 0.964 2.143 [439]
ECG, μg/kg 1.76–3.78 siPLS 0.96 0.78 [438]
n.i. SPA-PLS 0.989 0.664 [439]
Gallocatechin n.i. SPA-MLR 0.985 0.199 [439]
GCG n.i. SPA-MLR 0.890 0.302 [439]
Theanine, μg/kg 0.86–2.80 SA-PLS 0.93 0.8 [438]
AC, Trolox eq n.i. 1st der., PLS 0.92 88 [437]
AC, % 65.07–80.59 SA-PLS 0.80 0.72 [438]
Discrimination of grade MSC, MC, siPLS 93% [440]
EGC n.i. SPA-PLS 0.981 0.658 [439]
Gallic acid n.i. SPA-PLS 0.894 0.094 [439]
Green powder Caffeine, % n.i. 2nd der., PLS 0.97 0.19 [437]
m/g 4.6–35.9 weighted MSC, mPLS 0.97 1.538 [441]
% 2.2611–3.7616 SNV, PLS 0.97 0.08 [442]
mg/g 16.09–55.31 SNV, SVM 0.95 2.4 [443]
Catechin, % 0.1–2.8 MSC, mPLS 0.91 0.25 [441]
mg/g 92.05–194.13 SNV, SVM 0.97 7.23 [443]
Gallic acid, mg/g 0.02–0.89 weighted MSC, mPLS 0.85 0.045 [441]
Gallocatechin, mg/g 0.3–2.9 DT, mPLS 0.78 0.374
EC, mg/g 2.0–15.2 SNV, DT, mPLS 0.95 0.848
Green powder EGC, mg/g 1.0–59.8 weighted MSC, mPLS 0.95 3.333
EGCG, % 7.34–14.30 DT, SNV, GA-siPLS 0.96 0.35 [444]
mg/g 5.6–143.9 mPLS 0.97 4.313 [441]
ECG, mg/g 1.9–26.6 SNV, DT, mPLS 0.94 1.419 [441]
EGC-3-(3′-O-methyl) gallate, mg/g 0.07–2.60 SNV, mPLS 0.58 0.256 [441]
GCG, mg/g 0.08–3.28 mPLS 0.85 0.2 [441]
Total catechins, mg/g 22.1–206.8 weighted MSC, mPLS 0.97 9.463 [441]
Total polyphenol content,% 19.1543–30.2329 2nd der., PLS 0.93 1.11 [442]
14.93–25.46 SNV, siPLS 0.96 0.7327 [445]
EGC,% 2.126–5.428 MC, PLS 0.99 13.65 [446]
EC,% 0.131–0.397 MC, PLS 0.96 1.74
EGCG,% 7.340–14.088 SNV, PLS 0.98 38.39
ECG,% 1.764–3.784 SNV, PLS 0.98 11.76
AC, Trolox eq. n.i. DT, PLS 0.88 124 [437]
Antioxidant activity 0.442–0.806 min/max norm., SVM 0.97 0.02 [447]
Lutein,% 0.285–1.063 DT, SPA-MLR 0.98 0.003 [440]
Chlorophyll-a,% 0.075–1.041 MSC, SPA-MLR 0.97 0.005
Chlorophyll-b,% 0.012–0.536 1st and 2md der., 0.99 0.001
Pheophytin a,% 0.131–0.343 N, SPA-MLR 0.92 0.001
Pheophytin b,% 0.299–1.205 SPA-MLR 0.96 0.006
ß-carotene,% 0.119–0.879 1st and 2nd der., SPA-MLR 0.97 0.004
Sensory score 69.5–90.0 LTSA-RVM 0.96 1.461 [448]
Physical quality 19–25 MSC, 1st der., PLS 0.90 0.496 [449]
Total cup quality 77–83 VN, 1st der., PLS 0.90 0.504
Colour 7–10 MSC, PLS 0.91 0.217
Aroma 20–25 VN, 1st der., PLS 0.90 0.371
Taste quality 19–29 MSC, 2nd der., PLS 0.89 0.744
Leaf 7–10 MSC, 1st der., PLS 0.90 0.214
Bitterness 1–5 VN, 1st der., PLS 0.91 0.306
Flavour 1–5 MSC, 1st der., PLS 0.95 0.297
Body 1–5 MSC, PLS 0.96 0.261
Overall quality 1–5 MSC, 1st der., PLS 0.92 0.376
Classification Grade SNV, MC, SOLPP 100% [450]
Varieties SNV, MC, SOLPP 100%
Origin SNV, MC, SOLPP 100%
Green powder Adulteration SNV, SVM 97.47% [451]
with sugar, glutinous rice
with sugar, % 0.2–40 SNV, IRIV-SVM 0.998 0.67
with glutinous rice, % 0.2–15 SNV, SVM 0.97 1.16
Powder and granules Caffeine, mg/100 mL ca. 15–95 2nd der., PLS 1.00 1.81 [452]
Roasted Classification of origin SNV, SVM 100% [453]
Infusion Polyphenols: amino acids ratio 2.724–4.575 SNV, PLS 0.87 0.316 [454]
Chinese green Classification of grade SNV, PLS-DA >92.4% [455]
Instant Caffeine, % 1.95–9.89 SNV, PLS 0.99 0.165 [456]
Catechin, % 3.51–23.4 SNV, GA-PLS 0.96 1.13
EGC, % 2.41–9.94 SNV, PLS 0.88 0.654
EGCG, % 0.24–9.43 SNV, GA-PLS 0.95 0.578
EC, % 0.64–3.29 SNV, PLS 0.96 0.533
ECG, % 0.06–5.92 SNV, PLS 0.94 0.349
Black tea Moisture, % n.i. SNV, PCA, SNV-PCA 0.99 0.00953 [457]
2.8–5.0 SGS, Normalization, PLS 0.89 0.19 [458]
Colour 10.0–19.0 SGS, Normalization, PCR 0.84 0.81
Body 11.0–19.0 SGS, SNV, PLS 0.97 0.29
Quality 7.0–19.0 SG, MSC, PLS 0.85 0.9
Appearance 6.0–19.0 SGS, Normalization, PCR 0.93 0.62
Density 127.0–550.0 SNV, PLS 0.89 29.66
Water extract 27.6–42.0 SGS, Normalization, PCR 0.81 1.39
Cellulose 10.0–18.7 SGS, SNV, PLS 0.66 1.07
Catechin, mg/g 5.97–7.46 SNV, CARS-LSSVMR 0.98 0.0024 [459]
CG, mg/g 0.03–0.05 SPA-LSSVMR 1.00 0.0005
EC, mg/g 0.77–5.61 MSC, CARS-LSSVMR 0.99 0.001
ECG, mg/g 1.77–2.09 SNV, CARS-LSSVMR 0.98 0.0021
EGC, mg/g 0.80–1.18 SNV, CARS-LSSVMR 0.98 0.004
EGCG, mg/g 2.55–4.00 MSC, SPA-LSSVMR 0.99 0.0009
Gallocatechin, mg/g 7.64–18.2 SNV, CARS-LSSVMR 0.99 0.0006
GCG, mg/g 1.17–1.63 SNV, CARS-LSSVMR 1.00 0.0002
Ash, % 5.84–7.95 IVISSA-PLS 0.95 0.0192 [460]
Black powder Caffeine, % 2.13–4.28 MSC, PLS 0.96 0.16 [461]
mg/g ca. 0.5–5 SNV, BP_AdaBoost 0.94 0.21 [462]
mg/g 0.98–3.55 biPLS 0.92 0.209 [463]
20.65–56.67 SNV, SVM 0.93 2.51 [443]
Catechins, mg/g 48.33–156.29 SNV, SVM 0.97 8.4 [443]
EGCG, mg/g 0.78–19.62 CARS-PLS 0.94 1.74 [464]
Total catechins, mg/g ca. 0–8 SNV, BP_AdaBoost 0.72 0.95 [462]
Water extracts, % 22.63–49.50 min/max norm., PLS 0.96 0.685 [461]
mg/g ca. 20–46 SNV, BP_AdaBoost 0.91 1.73 [462]
mg/g 26.31–42.09 GA-PLS 0.88 1.47 [463]
Free amino acids, % 0.52–3.69 SNV, PLS 0.93 0.273 [461]
TPC, % 4.21–20.52 min/max norm., PLS 0.95 0.594 [461]
mg/g ca. 2–20 SNV, BP_AdaBoost, 0.71 2.35% [462]
Colour Sensory score 5.5–9.5 GA-BP-ANN 0.86 0.461 [465]
Taste quality 1–10 SNV, BP_AdaBoost 0.85 0.64 [462]
Free amino acids, mg/g ca. 2.5–6 SNV, BP_AdaBoost 0.89 0.36 [462]
2.87–5.56 GA-PLS 0.95 0.214 [463]
Theaflavin-3-gallate, mg/g ca. 0–1 SNV, BP_AdaBoost 0.72 0.18 [462]
Theaflavin-3′-gallate, mg/g ca. 0–0.6 SNV, BP_AdaBoost 0.81 0.08 [462]
Theaflavins, mg/g ca. 0–2.5 SNV, BP_AdaBoost 0.77 0.34 [462]
0.09–1.91 biPLS 0.92 0.162 [463]
Bitterness 1.83–7.00 CARS-MLR 0.94 0.5058 [464]
Astringency 1.57–6 CARS-PLS 0.91 0.541
Caffeine, mg/g 16.60–57.92 CARS-PLS 0.95 3.13
Classification Origin SNV, kNN 93.30% [466]
Quality categories SG, SNV, IGA-PSO 95.28% [467]
Congou black Theaflavins: thearubigins ratio 0.090–0.156 SNV, BP_AdaBoost 0.89 0.0044 [468]
Darjeeling black Classification, authentication SNV + 2nd der., PLS-DA 95.45% 4.55 [469]
Black infusion Caffeine, % 1.35–2.39 VN, PLS 0.97 0.08 [470]
Black and green—powder mg/g 16.94–55.31 SNV, SVM 0.91 2.93 [443]
mg/g 7.34–29.26 SNV, ACO-PLS 0.91 1.04 [471]
Catechins, mg/g 48.33–190.02 SNV, SVM 0.98 9.83 [443]
TPC, mg GAE/g 46.05–169.02 SNV, ACO-PLS 0.83 14.38 [471]
Classification Origin SG-1st der., SPA-LDA 100% [472]
Categories SNV, SVM >90% [473]
partially fermented Total catechins, mg/g 3.95–138.37 S, 1st der, 2nd der., mPLS 0.90 13.52 [474]
Theanine, mg/g 1.43–6.04 smoothing, 1st der., PLS, 2nd der., PLS 0.90 0.29
Black, green, yellow oolong Caffeine, mg/g 16.08–65.24 IVSO-PLSR 0.92 3.96 [475]
Catechin, mg/g 32.28–198.21 SG + 1st der., IVSO-PLS 0.95 11.41
Theanine, mg/g 0.51–24.50 SGS, SNV, IVO-PLS 0.84 2.53
Chinese tea TPC g GAE/100 g DM 6.08–34.29 MSC + 1st der., SGS, CARS-PLS 0.99 0.595 [476]
dark, black, oolong, green
Caffeine, % 2.10–4.99 MSC + 1st der., SGS, CARS-PLS 0.99 0.07
Free amino acids, TE% 0.96–3.65 MSC + 1st der., PLS + SGS, CARS-PLS 0.99 0.063
Fresh tea leaves Caffeine, mg/g 12.871–25.965 SGS, CARS-SPA-MLR 0.89 0.9506 [477]
EC, mg/g 9.815–17.515 MSC, SGS, CARS-SPA-MLR 0.92 0.4595
ca. 30–70 SNV, CARS-LS-SVM 1.00 0.41 [478]
EGC, mg/g ca. 40–140 SNV, CARS-LS-SVM 1.00 1.586 [478]
11.996–33.365 MSC, SGS, CARS-SPA-MLR 0.94 1.5494 [477]
EGCG, mg/g 28.79–69.533 SGS, CARS-SPA-MLR 0.92 2.6633 [477]
ca. 75–300 none, CARS-LS-SVM 0.99 4.23 [478]
ECG, mg/g 7.730–25.979 SGS, CARS-SPA-MLR 0.89 1.3881 [477]
ca. 30–110 none, CARS-LS-SVM 0.99 1.799 [478]
Lusan-Yunwu powder TPC n.i. biPLS 0.95 8.33 [479]
Free amino acids n.i. siPLS 0.91 4.96
TPC/FAA n.i. siPLS 0.93 0.437
Matcha TPC, mg/g 11.848–18.943 1st der., SPA-siPLS 0.97 0.4806 [480]
% 2.10–3.76 SNV, RF-PLS 0.86 0.82 [481]
Free amino acids, % 8.51–14.58 SNV, RF-PLS 0.96 0.14
Free amino acids, mg/g 3.035–4.785 SGS, GA-siPLS 0.98 0.0887 [480]
Polyphenols: amino acids ratio 2.421–6.214 SNV, SPA-siPLS 0.99 0.1602
Oolong Theanine 1.4262–6.0383 S, DT, PLSR, SVMR, GPR varsel. RC, UVE, VIP, SR, FPA 0.88 0.3219 [482]
Theanine 1.42–6.04 DT, FPA-GPR 0.88 0.3191
Classification of origin SNV + 2nd der., PLS-DA 85% [483]
Green, oolong Identification of varieties SNV, ANN 100.00% [445]
Pu-erh Theanine, mg/g 5.32–19.41 SNV, weighted PLS 0.85 1.317 [462]
Polysaccharides, g glucose/100 g extract 0.065–0.33 SGS, SNV, weighted PLS 0.84 0.0192
Total flavonoid, rutin/100 g ext. 0.568–1.798 SGS, MSC, weighted PLS 0.84 0.1528
Antioxidant activity 0.25–0.73 SNV, weighted PLS 0.87 0.0652
TPC, g GAE/100 g 7.02–13.55 SGS, MSC, weighted PLS 0.83 0.4532
Pu-erh ripen powder Caffeine, mg/g 18.7–33.4 1st der.,DT, PLS 0.87 1.58 [484]
Catechin, mg/g 0.036–0.799 N, PLS 0.84 0.091
CG, mg/g 0.006–0.829 SNV, MSC, PLS 0.85 0.082
Gallocatechin, mg/g 0.009–0.797 MSC, SGS, 1st der., PLS 0.91 0.074
GCG, mg/g 0.004–0.326 MC, PLS 0.79 0.097
EC, mg/g 0.029–0.808 MSC, PLS 0.86 0.093
ECG, mg/g 0.007–0.703 MC, DT, PLS 0.85 0.077
EGC, mg/g 0.018–1.51 MC, DT, PLS 0.84 0.16
EGCG, mg/g 0.006–1.14 N, DT, PLS 0.81 0.066
Bitterness 2.15–5.20 1st der., 2nd der., PLS 0.57 0.391
Astringency 2.125–5.125 MC, 1st der., PLS 0.76 0.252
Yuezhou Longjing Caffeine, % 2.435–4.291 MC, CARS-PLS 0.91 0.1401 [485]
2.5–4.3 BOSS-SVM 0.96 0.11 [486]
Total catechins, % 10.1–27.693 SNV, VCPA-IRIV-PLS 0.88 0.8823 [485]
10.10–23.66 MSC, CARS-PLS 0.79 1.06 [486]
Sensory score ca. 65–95 SNV, VCPA-IRIV-PLS 0.91 2.5784 [485]
72.55–92.92 BOSS-SVM 0.94 2.06 [486]
Tea leaf Caffeine, % 1.42–5.94 SVM 0.65 0.07 [487]
Tea varieties Classification SNV, SIMCA α-error 0.2 [488]
White, albino Discrimination SNV, DA 100% [489]
Partially fermented Classification origin 1st der., SVM >83% [490]
type and origin 1st der., SVM 100%
Commercial TPC, mg/kg 6.56–15.11 MSC, iSPA-PLS 0.93 0.599 [472]
Classification 1st der., SVM 93% [491]

5.6.2. Coffee

The green coffee beans that we roast, grind, and brew to produce the popular beverage known all over the world are actually the seeds contained in fruits from trees and shrubs naturally grown in the shade of African forests, including the islands of Madagascar and Mauritius, and cultivated in tropical areas such as equatorial Africa, Java, Sumatra, and other islands of the Dutch East Indies, West Indies, India, Arabia, the islands of the Pacific, Mexico, and Central and South America [492].

Various species and cultivars of the coffee plant are cultivated, which fundamentally determine the chemical composition of green coffee. Additionally, different growing conditions, climatic factors, and the processing methods of green coffee also influence the quality of the final product, thus affecting its price. The assessment of coffee quality involves numerous aspects related to the coffee plant, green coffee, and the roasted coffee produced from it. Assessment of coffee quality is usually focused on factors that influence utilization of the final product, with consumer preferences being assessed in three primary ways: physical (e.g., bean size), sensorial (cup quality) and chemical analysis (key compounds attributed to quality). However, coffee quality results from interaction among many different factors, including genotype (G) and environment (E) [493]. Due to the high price of coffee, it is also worth investigating coffee adulteration, which can help prevent consumer deception and financial harm.

The potential of NIR spectroscopy to replace traditionally applied methods was examined in numerous cases, particularly in the classification and identification of various coffee types, as well as in relation to their physicochemical parameters and sensory properties. The models developed in connection with these different applications and their key characteristics are summarized in Table 20.

It is important to emphasize the moisture content in the case of green coffee, which must not exceed 12% to ensure microbiological stability. Several standards for reference, routine and rapid methods are already established for the determination of water content in green coffee [494].

Since the price of coffee can be significantly influenced by its geographical origin, NIR spectroscopy is often employed in combination with various chemometric methods to determine this factor. Primarily, scatter correction methods have been used for data pre-processing, while both linear (such as LDA, PLS-DA) and non-linear (such as ANN) multivariate statistical methods have been applied to develop classification models. In terms of chemical composition, the alkaloids of coffee, 5-caffeoylquinic acid (5-CQA), various sugars, and acidity have typically been analyzed. In addition to these, a new research direction has emerged, focusing on the elemental composition of coffee [495].

Green coffee becomes consumable through roasting, during which its chemical composition undergoes significant transformation. Pyrolysis and the Maillard reaction produce numerous compounds that are not characteristic of green coffee. NIR spectroscopy can be applied to monitor the roasting process, either by using spectral data alone or in combination with colour data or by monitoring the first and second cracks. Key quality attributes of roasted coffee include caffeine content, acidity, and sensory properties, which are typically determined using cupping tests. Among the latest research efforts, the analysis of aroma profiles determined by gas chromatography in combination with NIR spectroscopy gained attention. Coffee adulteration can be carried out by adding various ingredients such as chicory, corn, barley, or even sticks of the coffee plant. Additionally, Arabica coffee is often adulterated with Robusta, as the two species represent different price categories, although this price gap has diminished in recent times. The results of the research related to these analyses are summarized in Table 21.

Table 20.

Overview of NIR Results for Green Coffee.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Treatment, Regression R2 Root Mean Square Error
Green coffee Caffeine, % 0.95–4.13 normalization + 1st der., PLS 0.86 0.4 [496]
0.07–3.53 1st der., OPS-PLS 0.98 0.08 [497]
Theobromine, % 0.10–0.67 normalization + 1st der., PLS 0.85 0.1 [496]
Cafestol, mg/100 g 182.62–1392.28 SNV, mPLS 0.92 111.01 [498]
Khaweol, mg/100 g 182.69–1265.41 SNV, mPLS 0.88 92.6
Acidity 6.75–9.0 SNV, PLS 0.83 0.21 [499]
6.64–8.57 MSC, PLS 0.74 0.25 [500]
Aftertaste 6.5–9.0 1st der., +SNV, PLS 0.8 0.22 [499]
6.25–8.57 1st der., PLS 0.77 0.29 [500]
Aroma 6.5–9.0 1st der., PLS 0.59 0.33 [499]
Body 6.5–9.0 1st der., +MSC, PLS 0.78 0.22 [499]
6.64–8.32 1st der., PLS 0.85 0.16 [500]
Flavour 6.5–9.0 1st der., +SNV, PLS 0.66 0.29 [499]
6.61–8.82 1st der., PLS 0.79 0.25 [500]
Overall cup preference 6.5–9.0 1st der., +MSC, PLS 0.89 0.9 [499]
6.57–8.68 1st der., PLS 0.73 0.29 [500]
Preliminary cup quality 42–57 1st der., +SNV 0.67 1.72 [499]
71–91 SLS, PLS 0.48 3.63
Total specialty cup quality 76.8–92.5 MSC, PLS 0.81 1.31
75.57–90.07 1st der., PLS 0.73 1.72 [500]
Moisture content, % 6–22 EMSC, PLS 0.9817 0.57 [501]
g/kg 104.6–134.7 2nd der., PLS 0.81 2.946 [502]
Electrical conductivity, us/cm/g 104.09–193.65 2nd der., PLS 0.94 7.94 [503]
Potassium leaching, ppm 40.41–64.92 2nd der., PLS 0.8 3.22
Ph 5.70–5.84 1st der., PLS 0.781 0.022
Titratable acidity, ml NaOH n/100 g 108.46–150.65 SNV, PLS 0.921 3.752 [504]
Balance 6.71–8.5 1st der., PLS 0.81 0.22 [500]
Green coffee Fragrance 6.82–8.61 1st der., PLS 0.81 0.17
TPC, mg GAE/g 40.97–51.86 MSC, PLS 0.89 0.61
5-caffeoylquinic acid, % 0.75–4.69 1st der., OPS-PLS 0.96 0.27 [497]
Trigonelline, % 0.14–1.62 1st der., OPS-PLS 0.96 0.07
Lipids, % 12.88–16.29 OSC, PLS 0.982 0.106 [505]
Protein, % 13.06–15.98 OSC, PLS 0.991 0.053
Reducing sugar content, g/kg 0.10–2.60 SNV, PLS 0.781 0.236 [502]
Soluble solids, g/kg 271.2–315.1 MSC, PLS 0.516 0.48
Total sugar content, g/kg 74.21–102.97 SNV, PLS 0.694 2.91
Africa d13C, ‰ vs. V-PDB (−28.9573)–(−26.4017) EMSC, PLS 0.88 0.28 [495]
d18O, ‰ vs. V-SMOW 29.8348–32.2833 EMSC, PLS 0.92 0.32
d2H, ‰. vs. V-SMOW (−50.2579)–(−34.7610) EMSC, PLS 0.91 2.48
Lithium, ppm 0.011–0.0109 EMSC, PLS 0.88 0.0012
Sodium, ppm 10.0200–24.4300 EMSC, PLS 0.91 5.35
Manganese, ppm 11.5752–49.1093 EMS, PLS 0.89 5.30
Nickel, ppm 0.1504–0.4721 EMS, PLS 0.71 0.062
Selenium, ppm 0.0506–0.2050 EMS, PLS 0.62 0.024
Strontium, ppm 3.0243–6.4790 EMS, PLS 0.71 0.52
Molybdenum, ppm 0.0653–0.2221 EMS, PLS 0.7 0.018
Cadmium, ppm 0.0031–0.0068 EMS, PLS 0.91 0.00085
Barium, ppm 2.5606–5.9386 EMS, PLS 0.77 0.54
Lanthanum, ppm 0.0019–0.0473 EMS, PLS 0.88 0.0066
South America D13c, ‰. vs. V-PDB (−29.4865)–(−25.9086) EMS, PLS 0.93 0.37 [495]
D18o, ‰. vs. V-SMOW 22.1487–29.6306 EMS, PLS 0.93 0.89
D2h, ‰. vs. V-SMOW (−82.1523)–(−56.8713) EMS, PLS 0.88 4.68
Lithium, ppm 0.0010–0.0080 EMS, PLS 0.7 0.0015
Boron, ppm 1.2369–20.8171 EMS, PLS 0.79 2.55
Nickel, ppm 0.0711–0.5460 EMS, PLS 0.73 0.088
South America Rubidium, ppm 3.4758–41.9333 EMS, PLS 0.69 5.39
Molybdenum, ppm 0.0529–0.5719 EMS, PLS 0.86 0.14
Caesium, ppm 0.0021–0.1844 EMS, PLS 0.74 0.038
Classification Natural, washed Arabica and Robusta SNV, LDA 100% [506]
Origin MSC, SVM 100% [507]
MSC, PLS-DA 98.00% [508]
PDS, SSOM 71% [509]
MSC 99.81% [510]
SNV + SGS, PCA-DA 57.60% 19.10% [511]
Species EMSC, PLS-DA 90.50% 0.3641 [512]
Continent EMSC, RF 0.99 [513]
Region EMSC, RF 0.88
Country EMSC, RF 0.88
Discrimination Civet coffee FFBBANN 99.98% [514]
Table 21.

Overview of NIR Results for Roasted Coffee.

Sample Investigated Parameter Concentration Range Chemometrics Data Ref.
Pre-Treatment, Regression R2 Root Mean Square Error
Roasted Coffee Bitterness 1–5 SNV, C, IPW-PLS 0.9402 4.7364 [515]
1–5 OPS, PLS 0.87 0.35 [516]
1–10 MSC, BLC, PLS 0.8351 0.0996 [517]
1–5 2nd der., Jack-Knife PLS 0.835 0.2 [518]
Mouthfeel 1–5 CC, IPW-PLS 0.8318 7.0117 [515]
Aftertaste 1–5 CC, IPW-PLS 0.8676 6.5683