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. 2023 Mar 5;6:100476. doi: 10.1016/j.crfs.2023.100476

Influence of maturity on bruise detection of peach by structured multispectral imaging

Ye Sun a,b, Xiaochan Wang b, Leiqing Pan c, Yonghong Hu a,∗
PMCID: PMC10023935  PMID: 36941891

Abstract

Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669–955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1–S3) were 91–92%, 90.71–97.43%, and 97.14–99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels.

Keywords: Structured illumination, Non-destructive detection, Watershed algorithm, Phenolics metabolism, Bruising

Graphical abstract

Image 1

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Highlights

  • •

    Measure the phenolic compound metabolism in bruised peaches with different maturity.

  • •

    Develop structured multispectral imaging system for detecting bruised peach.

  • •

    S-MSI system coupled with watershed algorithm enhanced the detection capability.

1. Introduction

Peaches mature in the hot and rainy summer, and the flesh is delicate and soft. Therefore, it is susceptible to bruising during picking, transportation and all stages of postharvest handling. While, bruises are difficult to immediately detect by naked eyes. Over time, the bruises gradually appear. In order to reduce the occurrence of bruising damage, fruit growers often pick relatively raw peaches. Previous reports also have revealed that mature fruits are more susceptible to bruise damage than immature fruit (Cañete et al., 2015). At the immature stage, the flesh is firmer, which can reduce the frequency of bruising. However, at this stage, the sugar content and taste of peaches are relatively poor, and consumers prefer the naturally ripe peaches on the tree. It is for this reason that the maturity of fruit growers is not consistent when picking, and bruising occur in different maturity stages. For relatively immature peaches, a slight impact energy has no effect on the pulp; when the impact energy increase to a certain critical value, the pulp has no obvious change in a short time, then, depending on the extent of damage, the presence of a bruise may take up to 12 h of oxidation to become browning and visible, which means that the bruised peach may not be detected until it reaches the retail store or consumer. When the fruit is more mature, the pulp becomes soft, a slight impact energy will cause visible damage to the flesh, which may even be infected by microorganisms and decay over time. Therefore, considering the different maturity is a very important factor in the detection of peach bruises.

To date, a wide range of non-destructive technology have been extensively reported and used for the detection of defects, such as multichannel hyperspectral (Huang et al., 2017), hyperspectral imaging (Baek et al., 2019; Garhwal et al., 2020), near-infrared spectroscopy (Minas et al., 2021) and so on. Preliminary studies have demonstrated that these non-destructive technologies successfully distinguish external defects in apple (Baek et al., 2022; Li et al., 2019; Pieczywek et al., 2018), citrus (Tian et al., 2021), loquat (Munera et al., 2021), and peach (Minas et al., 2021). These optical technologies most rely on measuring the reflected light of the sample, however, due to the limited photons information from light penetration depth, it is still difficult to detect the internal defects.

In recent years, structured illumination has attracted the interest of researchers as a powerful new tool for defect detection especially in industry and medicine, which can achieve super resolution and contrast beyond the fundamental limit. In contrast to uniform illuminance, structured illumination is known as illuminating an object with structured or patterned light, due to the spectral phase shifting, a certain amount of higher-frequency sample information is down-modulated into the lower-frequency passband and becomes resolvable in structured images (Lu, 2018). At present, there are few researches on structured illumination for agricultural products. Dr. Renfu Lu from the United States Department of Agriculture systematically studied the detection potential for structured illumination reflection imaging (SIRI) system, applied this system for detection of bruise in apples and chilling injury in cucumbers, and developed a series of detection algorithms to increase the speed of image acquisition and improve the acquisition mode (Lu et al., 2016; Lu and Lu, 2018, 2020). In addition, Saberioon, MM and P. Cisar (2016) proposed a method using near-infrared coding dot matrix, the new method of machine vision system that combines structured illumination and fast brightness correction to identify apple defects makes the overall recognition accuracy rate of apple defects reach 90.2%. Among them, the structured multispectral imaging (S-MSI) can obtain the structured image and spectrum simultaneously, which is a promising nondestructive detection method for detection of internal defects in fruit. The detection capability of S-MSI, mainly depends on its image resolution, image contrast and depth-resolving features. By varying the spatial frequency of illumination, the depth at which light penetrates the tissue can be controlled, which offers potential for better detection of subsurface defects in fruit. Previous studies have confirmed that the enhancement effect of structured illumination on superficial skin defects, however, there are few studies on the effect of maturity on bruise detection. Previous studies have shown that the maturity of the fruit has a strong influence on the severity of bruise damage (Berardinelli et al., 2005). Especially after bruising, the browning degree of bruised pulp with different maturities was different. Generally, pulp browning has been deemed as the consequence of enzymatic oxidation (PAL/PPO) action on polyphenolic substance in the presence of oxygen (Franck et al., 2007). In addition, the existing structured illumination technology can only obtain grayscale images, with fewer applicable data parameters, and the mechanism of detecting peach bruising is still unclear. Therefore, this research develops structured spectra and structured images under different wavelength bands based on the perspective of "structured multispectral system", and explores the mechanism of structured multispectral imaging detection of peach bruises in combination with the phenolics metabolism of peach bruises with different maturities.

The main objectives of this study were to: 1) investigate the potential of structured multispectral imaging to detect the bruised peaches of different maturity levels; 2) quantify the changes in peach physiological metabolisms and microstructure of different maturity levels during bruising; 3) explore the relationship between maturity and structured multispectral parameters of bruised peaches.

2. Materials and methods

2.1. Peach samples collection

‘Baifeng’ peaches were picked from a commercial orchard at Yangshan town, in Jiangsu province, China. The sample set included three maturity stages assigned based on fruit firmness and farmers' experience, with 720 fruits for each stage (S1: stage 1, relatively immature; S2: stage 2, commercially ripe; S3: stage 3, overripe). Among each set, 700 peaches were used for acquiring the S-MSI and the remaining 20 peaches were used for the destructive measurement of physical and chemical parameters. Before the bruising test, analyzed the peaches as a healthy group for each stage. Bruising tests were performed as description in Sun et al. (2021), and the bruise was impacted by a wooden ball with impact energy of 0.4 J. As a result, the bruised area could not be clearly observed from the appearance after bruising. Then, the bruised peaches were imaged three times at 0, 4, and 24 h after bruising, to monitor the process of bruising.

2.2. Physical and chemical parameters

In this research, the cellular microstructure of bruise, the enzyme activity and gene expression of browning-related enzymes, and the metabolism of phenolic substances related to browning have been studied. Total 60 peaches (20 peaches for each maturity stage) were used for this part, half of them (10 peaches*3 maturity levels) were the control group before bruising as healthy peaches, and the other (10 peaches*3 maturity levels) were the experimental groups after bruising.

Three fruits from each maturity level of healthy group were randomly selected for microstructural analysis. Similarly, after bruising for 4 h, three samples were taken from each maturity level. Confocal laser scanning microscopy (CLSM, Olympus Fluo View 1000 LSM, Olympus America Inc., Center Valley, PA, USA) was conducted on the tissue specimens to observe the microstructural changes of bruises at different maturity levels before and after bruising. The specific operation mode was described in Sun et al. (2021).

After bruising for 4 h, the bruised area from each maturity level of experimental groups were cut and quick frozen with liquid nitrogen, and then stored at −80 °C, the same operation was used for sampling in the control group.

The determination of four main antioxidative enzymes including Phenylalanine ammonia lyase (PAL), Catalase (CAT), Peroxidase (POD), Superoxide dismutase (SOD), were performed by the assay kit (Jiancheng Bioengineering Institute, Nanjing, China). Two grams of frozen tissues was homogenized with 7.0 mL of phosphate buffered saline and centrifuged at 12,000*g at 4 °C for 10 min, then aspirated the supernatant for extraction of crude enzyme solution. The next steps are performed according to the instructions of assay kits. The real-time quantitative PCR analysis of antioxidative enzymes were performed according to Zhou et al. (2019), and the total RNA from peach samples was extracted and synthesized by the Column Plant Total RNA Extraction and Purification Kit (B518661, Sangon Biotech, Shanghai, China). The sequences of all primers were shown in Supplementary Table S1 and gene TEF2 was used as an internal control gene. The relative quantification was measured by the method of 2−ΔΔCt.

Metabolomics analysis of healthy and bruised peaches at different maturity levels was performed by the ultra-performance liquid chromatography (UHPLC) (Nexera UHPLC LC-30A, Shimadzu Corp., Japanese) coupled with fast scanning and quadrupole mass spectrometer system (TripleTOF5600, AB SCIEX™, Massachusetts, USA). After freeze-drying of the frozen tissues, the 0.02 g tissues with 1 ml deionized water were sonicated for 30 min, then centrifuged at 12000 r/min for 10 min, the supernatant was filtered with 0.45 μm membrane for further test. The column was Shimadzu InerSustain C18 (100 × 2.1 mm, 2 μm), and the temperature was kept at 35 °C with the flow rate of 0.3 mL/min. The mobile solutions were acetonitrile (A) and ultrapure water with 0.1% formic acid (B) with a gradient as follows: 0–7 min 5:95 V(A)/V(B); 7–12 min 70:30 V(A)/V(B); 12–14 min 100% V(A); 14–16 min 5:95 V(A)/V(B). Electrospray ionization was used to acquire Mass spectra based on positive and negative mode, the test conditions were: Ion Source Gas1:50; Ion Source Gas2: 50; Curtain Gas: 25; Source temperature: 500 °C/450 °C; Ion Sapary Voltage Floating (ISVF): 5500 V/4400V; TOF MS scan range: 100–1200Da; product ion scan range: 50–1000Da; TOF MS scan accumulation time: 0.2s; and the product ion scan accumulation time 0.01s. Import the raw data into MS-DIAL 4.20 MS-DIAL software (data independent MS/MS deAvolution for comprehensive metabolome analysis) (Nature Methods, 12, 523–526, 2015) for preprocessing, including peak extraction; noise removing; Deconvolution; peak alignment. Compare the extracted peak information with the database, and perform a full database search on the three libraries of MassBank, Respect, and GNPS.

2.3. Structured multispectral imaging system

The structured multispectral imaging system (S-MSI), as shown schematically in Fig. 1, was used for this study. This system mainly consisted of an adjustable halogen lamp (3900 ER, Illumination Technologies Inc, New York, USA), a digital micromirror device (DMD)-based projector (DLi CEL5500, Digital Light Innovations, Austin, TX, USA) with a resolution of 768 × 1024 pixels, a real time imaging hyperspectral camera (Snapshot-NIR, IMEC, Leuven, Belgium) with a resolution of 640 × 480 pixels which contained 25 bands from 669 to 955 nm, and a computer coupled with image acquisition software (Spectral Image, Isuzu, Taiwan, China). The light source was set at 140 W with 15° of incident angle relative to the vertical axis, and the exposure time for the camera was set at 500 ms. The sinusoidal patterns at frequency 60 m−1 were generated in Matlab (The Mathworks, Inc., Natick, MA, USA) and sent to the projector. The fiber optic cable channeled the output of the QTH lamp into the projector, and sinusoidally-modulated patterns in 8-bit gray-scale bitmap format were uploaded to the projector's bundled control software for pattern projection.

Fig. 1.

Fig. 1

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The data acquisition process of structured multispectral imaging system.

In S-MSI, the raw reflectance images acquired from samples are 2-D fringe patterns. For this research, three fringe patterns, with phase offsets of −2/3π, 0 and 2/3π, were used to be processed to remove their fringes to obtain alternating component (AC) and direct component (DC) images, which is referred to as demodulation (such concept comes from the fields of communications and signal processing). The detail principles and methods of data acquisition based on structured illumination were described in Sun et al. (2019). Taking the S-MSI image with 2π/3 phase shift interval as input, a mathematical model is constructed by image difference, mathematical derivation and simplification, and the high-resolution image is obtained by direct analysis in the spatial domain:

AC=23[(I1−I2)2+(I2−I3)2+(I3−I1)2] (1)
DC=13(I1+I2+I3) (2)

where I1, I2 and I3 each represent one of the three phase-shifted reflectance images. The DC images were comparable to the results obtained by a broadband machine vision system under uniform illumination. The AC images attenuated at varying rates depending on the spatial frequency of the pattern, which contains the depth specific information related to spatial frequency of illumination used for image acquisition. Since the spectral value on each pixel of the sample is the grayscale intensity at the corresponding band, the image of each band was demodulated by Matlab, and then the corresponding spectrum of each pixel on the image can be obtained. Each peach sample was placed on the stage, with the bruised area facing up towards the camera. Hence, each peach was scanned 3 times (3 phases).

2.4. Data preprocessing

The physical and chemical parameters were measured three biological replicates, and data were presented as mean standard deviation (SD), and data were compared by Bonferroni and Tukey's test at P < 0.05 using SPSS for Windows version 18.0 (SPSS Inc., Chicago, IL).

The acquired phase images were processed by dark-and-white correction and three-phase image demodulation. The corrected images were calculated using the white and dark reference images based on the described in Zheng et al. (2022). A Spectralon panel with the reflectance rate of 98% was imaged under uniform illumination as the white reference, and a dark image was collected by turning off the light source to correct the dark current effect of the camera. Since the spectral value on each pixel of the sample is the grayscale intensity at the corresponding band, the structured multispectral value on each pixel of the sample can also be obtained by de-modulating the spectral values at three phases. Therefore, the structured spectrum of the sample is obtained after programming of spectral demodulation by Matlab. Take a part of peaches, manually extract the structured spectrum of the bruised area of different maturity and compare them with the structured spectrum of healthy peaches, and select the characteristic bands with significant differences.

According to the characteristic bands selected by the preprocessing, the original structured images at the characteristic wavebands of all samples were extracted. After image demodulation, the images were further preprocessed to remove the background and improve contrast prior to disease detection. The background usually has much lower intensity than the objective, therefore, a uniform threshold can be chosen for background removal to generate a mask for each peach sample. The morphological operations (i.e. Matlab-imfill.m) were implemented to fill the holes. The obtained mask was then applied to the demodulated images at other wavelengths for background removal from the peach sample. Watershed algorithm was performed on AC and DC images to locate the edges of the diseased area, which can solve a variety of image segmentation problem and is suitable for the images that have high contrast. The watershed transformation treats the image it operates upon like a topographic map, with the brightness of each point representing its height, bright areas are ‘high’ and dark areas are ‘low’ and finds the lines that run along the tops of ridges. Because of the browning of decayed areas, nondecayed areas showed brighter color; therefore, the decayed peaches are segmented using 8-connected neighborhood for the watershed computation. The DC image represents the average intensity of the sinusoidal illumination pattern used in image acquisition and is analogous to a conventional, uniform illumination image. Therefore, DC images were used to segment the diseased area based on the watershed, which can represent the results of common images, and compare to the results of AC images. To determine the performance of the proposed watershed algorithm, the obtained results was analyzed with the detection rates. The watershed segmentation was implemented using Matlab 2017 (The MathWorks, Inc., Natick, MA, USA).

3. Results and discussion

3.1. Changes in physical and chemical parameters during peach bruising

3.1.1. Macroscopic and microstructural changes in peach tissue under bruising

In general, the application of an external force can cause surface and/or internal mechanical damage to the peach, while it is difficult for the naked eye to directly observe bruising in the early stages, especially for peaches with a firm texture (Fig. 2). According to our impact test, this impact energy was not enough to cause the external damage such as peel rupture, especially for S1, even after 24 h of bruising, there is still no change in appearance. After cutting into the 5 mm outer skin, it was found that the flesh of bruised area from all maturity stages was browning after bruising for 24 h, with the improvement of maturity, under the same external force, the bruised area became larger and the pulp brown seriously. Although there was no apparent damage from the appearance of the immature hard peaches, but the damage to the cell microstructure was still significant. The effects of maturity levels on peach bruising were observed by confocal laser scanning microscopy as shown in Fig. 3. For the S1-relatively immature peaches, the cells were arranged neatly and tightly, and the cell membrane was intact. After bruising for 4 h, the cracks were neat, and the cells around the cracks were evenly distributed of the S1 samples. In comparison, the S2-commercially ripe samples of healthy group had similar neat, tight arrangement of cells, with some cell membranes shrunken, the bruised tissue had larger cracks and more obvious shrinkage of the cell membrane. For healthy tissue of S 3-overripe peaches, the cell membrane was shrunken, and loose connections were observed between cells, after bruising, the cracks ran through the entire image, and the cells were severely shrunken. Moreover, pectin breakdown in the weak intercellular lamella and water redistribution in the cells and cellular space caused changes in the cell structure, indicating that the cells had aged. These results confirmed that bruising is a result of cell breakage, which is caused by stress and distortion of individual cells (Husseina et al., 2018). Comparing different maturity levels, it can be seen that under the same stress, with the increase of maturity, the damage resistance of the fruit is weakened, resulting in more serious tissue damage.

Fig. 2.

Fig. 2

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The RGB and S-MSI images at 781 nm of bruised peaches at different detection time.

Fig. 3.

Fig. 3

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CLSM (confocal laser scanning microscopic) images of a cross-section of healthy and bruised peach flesh tissue for three maturity stages (S1: stage 1, relatively immature; S2: stage 2, commercially ripe; S3: stage 3, overripe).

3.1.2. Identification of polyphenolic metabolites in peach fruit

Peach fruit extracts of different maturity levels from healthy and bruised group were analyzed using UHPLC-MS. The main 27 phytochemicals belonging to different polyphenolic classes were detected as shown in Fig. 4, including phenolic acids, flavonoids and their derivatives. The phytochemicals were identified on the retention times (RT), m/z, and molecular formula generated based on the MassBank, Respect, and GNPS databases and the related literature (Guo et al., 2020; Monti et al., 2016). Phenolic acids and their derivatives were the major polyphenol metabolites in peach. In this study, the detected phenolic acid compounds were mainly hydroxycinnamic acid, including: D-(+)-Malic acid, Quinic acids and derivatives, Methyl chlorogenic acid, Dicaffeoylquinic acid, and Hydrated chlorogenic acid. Previous studies have shown that neochlorogenic acid and chlorogenic acid are the main hydroxycinnamic acids in peaches, accounting for more than 90% (Guo et al., 2020; Mokrani et al., 2016). In addition, the hydroxybenzoic acids, hydroxyphenylacetic acid and their derivatives were also detected. The hydroxybenzoic acids derivatives included Epicatechin, Syringic acid, and P-hydroxybenzoic acid glucoside; the hydroxyphenylacetic acids derivatives included E-Resveratrol trimethyl ether and P-hydroxyphenylacetic acid.

Fig. 4.

Fig. 4

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Metabolic compounds detected in peach extract by UHPLC -MS.

Flavonoids in peach accounted for about 40% of total Polyphenols (Saidani et al., 2017). Eleven kinds of flavonoids were shown in Fig. 4 which detected in peach including flavanols, flavonols, flavanones and dihydrochalcone, Further subdivisions including: Primuletin, Formononetin, Gossypin, Gluconate, 8-Prenylnaringenin, Cirsimaritin, Quercetin, Catechin hexoside, Isoquercetin, Kaempferol-3-o-rutinoside and Phlorizin.

Apart from phenolic acids, flavonoids and their derivatives, other compounds in peach and nectarine fruit were also identified. Atractylodin found in peach before (Guo et al., 2020) was detected in this study. Moreover, Heteropeucenin and 5-hydroxy-7-(hydroxymethyl)-2-methyl-2-(5-oxooxolan-2-yl)-3H-chromen-4-one, the derivative of Chromones were detected in this study. In addition, Diphenylamine and Sesamin were inferred based on m/z and fragmentized information.

From the total relative content of phenolic compounds, with the increase of peach maturity, the content of phenolic compounds decreased (Fig. 4). These results were consistent with the results of previous studies, immature fruit contained significantly higher concentrations of the target phenolic compounds than ripe fruit (Nuncio-Jáuregui et al., 2015; Zheng et al., 2012). Compared with S1-relatively immature peaches, the three substances with the largest relative content difference of S3-overripe peaches were: the content of Methyl chlorogenic acid, Isoquercetin, and Phlorizin had the largest relative decrease, and the content of Dicaffeoylquinic acid, Catechin hexoside, and Quercetin had the largest relative increase. The Methyl chlorogenic acid, Isoquercetin, and Phlorizin belong to hydroxycinnamic acid derivatives. In this research, the relative content of Methyl chlorogenic acid, Isoquercetin, and Phlorizin of S1-peaches were 58.8-fold, 47.1-fold and 13.7-fold higher than those found in S3-peaches (Fig. 4). The reduction of the content of Phenolic acids and their derivatives might be the main cause of the changes in antioxidant activities during the growth of the peaches.

As shown in Fig. 4, after bruising, the total relative contents of phenolic compounds of bruised S1/S2 peaches were lower than these in healthy S1/S2 peaches, while the content of phenolic compounds in bruised S3 peaches was greatly increased. The decreasing trend in phenolics concentration of peach fruit in bruised S1/S2 peaches may be associated with the breakdown of phenolics because of senescence phenomena and enzymatic activity by the external force stress (Fawole and Opara, 2013; Liu et al., 2019). For the bruised S3 peaches, it is mainly the huge increase in the content of chromones that leads to the increase in the total content of phenolic substances, the other compounds with larger increases were Kaempferol-3-o-rutinoside, Dicaffeoylquinic acid and Catechin hexoside. Previous studies had shown that in plants as well as peach fruit, the stress could induce the accumulation of phenolics through the phenylpropane pathway (Sanchez-Ballesta et al., 2010). It is speculated that these compounds may be related to the severe bruising of peaches.

3.1.3. Changes and relative expression of antioxidative enzymes

Considering dramatically increased browning by the ripening of peach, the enzymatic browning metabolism was further studied. Bruised peaches from both maturity levels maintained higher relative expression levels of browning relative enzymes to the controls (Fig. 5A). For POD and CAT, the gene expression level of enzyme activity reached the highest at maturity level of S2, meanwhile significant differences were found between the healthy and bruised groups. The PPO and PAL decreased first and then increased, and reached the maximum at maturity level of S3, and significant differences were found between the healthy and bruised groups at S1 and S3. Previous studies have shown that, PAL involved in the biosynthesis of phenylpropanoid skeleton, while PPO catalyzes the oxidation of phenolic compounds to o-quinines, POD and CAT are the key antioxidant enzymes in reactive oxygen species scavenging (Liu et al., 2019; Mai and Glomb, 2013; Zhang et al., 2019). As time passes after bruising of overripe (S3) peaches, damage to cell tissue initiates contact between the phenolic compounds and oxidases, and the polyphenolic compounds were oxidized to produce browning of the pulp (Husseina et al., 2018; Sun et al., 2021). The accumulation of phenolics occurred with the increasing of PPO and PAL activity and obvious PB was observed in peaches exposed to bruising of S3 (Fig. 4, Fig. 5), indicating that a plentiful of phenolics may be used as antioxidants in peach deterioration under post-maturity and bruising. Fig. 5 (B) shows the corresponding activity changes in four antioxidative enzymes after bruising for 4 h. In all four antioxidative enzymes, the activity decreased first, and then increased to the peak values at maturity level of S3. PAL activity significantly increased at maturity level of S3, and PAL activity in S3 was more than 5 times of S1. For PAL, the significant differences were found between three maturity levels. In conclusion, the degree of pulp browning induced by bruising is causally associated with antioxidative enzymes.

Fig. 5.

Fig. 5

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Effects of bruising (after 4 h) on the relative gene expression (A) and enzyme activity (B) of on POD, CAT, PPO and PAL in peach different maturity levels (S1: stage 1, relatively immature; S2: stage 2, commercially ripe; S3: stage 3, overripe). Values are the means ± SD of triplicate assays, different lowercase letters indicate significant difference for (A) different group and (B) different maturity levels using Bonferroni and Tukey's test, respectively (P < 0.05).

3.2. Reflectance structured spectra and images of healthy, and bruised peaches

10 samples were selected from the healthy peaches of each maturity level, the structured spectral values of the equatorial parts of the samples were manually extracted, and the structured spectra of the 30 samples were averaged as the structured spectra of the healthy group. Then extract the structured spectra of the bruised area with different maturity levels after 24h of bruising, and take 30 samples for each maturity level, the average structured spectra were taken as the bruised structure spectra of different maturity levels. Fig. 6 shows the structured spectral reflectance (475–900 nm) under different samples. Compared with the ordinary hyperspectral spectra as described in the previous studies (Sun et al., 2017), the structured multispectral spectra obtained in this study is not similar. The possible explanation is that the imaging spectrometer used in this study is fast imaging, with fewer bands and a larger gap between bands, so the spectrum cannot reflect the spectral information of peaches well. It can be seen that the waveforms of the healthy group and the bruised peaches are generally similar, and the biggest difference is in the relative value of the reflection spectrum. This is because after the bruise occurs, the pulp browns and the reflection intensity of light decreases. In addition, as the maturity increases, the spectral reflectance value is getting smaller, the spectrum tends to be smooth, and the peaks and troughs are not significant, therefore, the effective information that the spectrum can reflect is also less. Especially in the three bands of 781, 824, 867 nm, there were significant peaks for healthy peaches, but with the increase of maturity, the peaks gradually became flat.

Fig. 6.

Fig. 6

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The mean structured multispectral spectra of 669–995 nm for healthy and bruised peaches after 24h (S1: stage 1, relatively immature; S2: stage 2, commercially ripe; S3: stage 3, overripe).

To reduce the amount of image processing, images at 781, 824, 867 nm were selected for post-processing based on the largest difference between the spectra of healthy and bruised samples. The DC, AC and RGB images of bruised peaches form different maturity levels for the selected wavelengths at 781 nm were shown in Fig. 1. The diseased areas were hard to be ascertained in the DC images for S1 and S2. On the contrary, the AC images obtained by the structured multispectral imaging system clearly revealed the bruised area, which verified the light was able to penetrate deeply enough into the bruised area and the superior capability of the S-MSI technique. With the prolongation of bruising time, the bruising displayed by AC images became clearer, and with the deepening of the browning degree of the pulp, the bruised area gradually increased. In addition, the higher the maturity, the more obvious the bruised area, which is also consistent with the previous difference results between the spectra.

3.3. Classification for bruised peaches

Based on the above research, the selected S-MSI images at 781, 824, 867 nm were used for image processing for bruise detection. Therefore, the demodulated AC and DC images of healthy and bruised peaches were classified by watershed algorithm based on Matlab. Since the DC images are analogous to those images that would be acquired under conventional and uniform illumination, the results of DC images were considered as uniform light modes which were compared to the results of AC images of S-MSI system. Table 1 summarizes the detection results for bruised sample obtained from the AC and DC images of three maturity levels for the three bands. The detection rates of the healthy group were defined as the number of peaches with unrecognized defect areas divided by the total number of 700 healthy peaches, and the detection rates of bruised groups (0, 4, 24 h) were defined as the number of peaches with correctly identified in the bruised area divided by the total number of 700 bruised peaches for each band and maturity level. Among the three maturity levels, AC images had consistently higher detection rates for bruised groups, ranging between 91% and 99.86%, while DC images resulted in the lower detection rates, ranging between 44.86% and 87.43%, thus showing the superior capability of the S-MSI system for detecting peach bruising than the uniform illumination system. For the healthy peaches, the recognition rates of DC image were slightly higher than those of AC image, because the AC images enhanced the surface resolution, therefore, some texture on the surface of peaches would be misjudged as bruised area. Comparing the detection rates of different maturity levels, it can be seen that with the increase of maturity, the bruise detection rate increases, especially for the bruised peaches of S3, whose bruise detection rates were higher than 97%. These results were similar to the results of changes in phenolic components as shown in 3.1.2, the total phenolic compounds between S1 and S2 had little change, while the total phenolic compounds of S3 had a relatively large increase, resulting in accelerated browning of the bruised area and better identification of bruised areas for S3. At the same time, the detection rates of S3 for DC images were also relatively high (more than 60%), indicating that the bruising depth is gradually approaching the peel for the over-ripen peaches. This is also consistent with our previous research that with increasing maturity, the peel color (L* value) of the bruised area gradually decreased and the bruised area became more obvious. In terms of the occurrence time of bruising, with the prolongation of bruising time, the recognition rate of bruising is higher. For the S1 samples, the impact of bruising time on the recognition rate is small, while the effect of bruising time on the bruising recognition rate became greater as the maturity increased. The possible reason is that due to the degradation of pectin and the loss of cell water in S3 peach, with the occurrence of impact, the cell structure changed, the cell aging was accelerated, and the oxidative browning was serious. In addition, there was no significant difference in the recognition accuracy between the three-band images.

Table 1.

The detection rates by watershed segmentation using alternating component (AC), and direct component (DC) images for peaches of different maturity levels and bands.

Maturity level Bands AC (%)
 DC (%)
Healthy 0 h 4 h 24 h Healthy 0 h 4 h 24 h
S 1 781 nm 99.29 91.28 91.43 91.57 100 44.86 43.00 45.14
824 nm 100 91.28 91.57 92.00 100 45.00 44.71 45.29
867 nm 100 91.00 91.14 91.29 100 47.57 45.00 47.57
S 2 781 nm 97.43 91.86 92.71 97.43 99.29 52.43 52.57 55.00
824 nm 98.29 91.86 92.57 96.71 99.71 52.29 55.14 55.57
867 nm 98.29 90.71 92.29 97.14 99.71 55.43 55.29 57.57
S 3 781 nm 96.71 97.14 98.29 99.71 96.71 65.14 74.86 85.43
824 nm 97.00 97.28 98.71 99.57 97.43 67.86 75.14 85.00
867 nm 97.00 97.14 98.57 99.86 97.14 65.00 77.86 87.43
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4. Conclusion

In this research, we developed a structured multispectral imaging system, obtained the structured spectrum (25 bands from 669 to 995 nm) and the corresponding structured image of bruised peach at different maturities and different occurrence times, and analyzed the effect of maturity on the identification of bruising in peach. At the same time, through physicochemical analysis, the changes in microstructure, oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism during the bruising process of peach were explored, indicating that mature peaches are more susceptible to outside pressure of bruise damage than immature peaches. As peaches mature, the external impact stress could promote phenolic accumulation through induction of the phenylpropane pathway and deepen oxidative browning of pulp. As the maturity increases, the spectral reflectance value of bruised peach is getting smaller, and the spectral waveform gradually flattens out, then extracted three characteristic bands of 781, 824, 867 nm related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1, S2, S3) was 91–92%, 90.71–97.43%, and 97.14–99.86%, respectively. The highest detection rates of bruised peaches with 99.86% was observed in 24 h bruised peaches of S3 at 867 nm, which means that with the increase of maturity, the bruise detection rate increases, especially for the bruised peaches of S3. With the prolongation of bruising time, the recognition rate of bruising was higher. This research demonstrated that S-MSI, coupled with a proper image detected method, can enhance our capability of detecting the early bruised peaches of immature peaches.

CRediT authorship contribution statement

Ye Sun: Data curation, Writing – original draft, Validation, Writing – review & editing. Xiaochan Wang: Conceptualization. Leiqing Pan: Formal analysis. Yonghong Hu: Supervision, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 31901769) and Jiangsu province agricultural science and Technology Independent Innovation Project (No. CX(22)3172).

Handling Editor: Dr. Maria Corradini

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crfs.2023.100476.

Appendix A. Supplementary data

The following is the Supplementary data to this article.

Multimedia component 1
mmc1.docx (15.1KB, docx)

Data availability

Data will be made available on request.

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Supplementary Materials

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Data Availability Statement

Data will be made available on request.

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