Abstract
Cheese adulteration represents a growing concern in the global dairy sector, especially for products with Protected Designation of Origin (PDO) status. This systematic review critically examines the application of eight major spectroscopic techniques—such as NMR, FTIR, NIR, Raman, IRMS, and MS-based methods—for detecting diverse forms of cheese adulteration, including species substitution, fat and protein replacement, non-dairy additives, geographical mislabeling, and antibiotic residues. Following PRISMA guidelines, 104 peer-reviewed studies were retrieved from PubMed, Scopus, and Web of Science. The review systematically evaluates these methods across 20 cheese types and 60 unique configurations based on sensitivity, specificity, sample preparation, matrix adaptability, and real-world applicability. Results demonstrate that while no single technique is universally optimal, each offers distinct advantages based on adulterant type and detection context. The combination of spectroscopic tools with chemometric models substantially enhances detection robustness. This is the first comprehensive review focused exclusively on spectroscopic authentication of cheese, providing a practical reference for researchers, regulatory bodies, and industry stakeholders committed to ensuring dairy integrity and transparency.
Keywords: Cheese adulteration, Food authentication, PDO cheese, Quality control, Spectroscopic techniques
Highlights
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104 studies reviewed to assess spectral tools for cheese adulteration detection.
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Main fraud types: species, fat/protein shifts, additives, and mislabeling.
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1H NMR, FTIR, and MS showed high accuracy for detecting adulterants.
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Combining spectroscopy with chemometrics improves fraud detection.
1. Introduction
Cheese is one of the most consumed dairy products worldwide, valued for its economic, nutritional, and cultural significance. Rising consumer demand for premium and authentic cheeses—especially those with Protected Designation of Origin (PDO) status—has intensified concerns about fraud and adulteration in the dairy sector (Bontempo et al., 2019; Cardin et al., 2022). Cheese adulteration encompasses various illicit practices, including substitution with non-declared animal species, incorporation of plant-based fats, addition of non-dairy proteins, dilution with water, and misrepresentation of geographical origin. Such practices undermine consumer confidence, breach regulatory standards, and may also present health and economic risks (Guarino et al., 2010; Joolaei Ahranjani et al., 2025; Russo et al., 2016).
To address these issues, both the food industry and regulatory bodies have turned to analytical tools to verify cheese authenticity. Among these, spectroscopic techniques have emerged as powerful, non-destructive, rapid, and often cost-effective tools for detecting cheese adulteration. A range of vibrational, nuclear magnetic, and mass spectrometric techniques have been applied for cheese authentication, including Near-Infrared (NIR), Mid-Infrared (MIR), Fourier-Transform Infrared (FTIR), Raman, and Nuclear Magnetic Resonance (NMR) spectroscopy MS-based methods (Balthazar et al., 2021; Cajka et al., 2016; Cozzolino et al., 2002; Kuckova et al., 2019). In addition to spectroscopic tools, emerging non-invasive sensor-based technologies such as electronic nose (E-nose) systems have also been explored in dairy product monitoring (Yakubu et al., 2022).
Each technique offers distinct advantages based on its operational principle and application context. NIR spectroscopy, for example, has demonstrated utility in detecting water addition, milk source substitution, and fat adulteration in a variety of cheese matrices with minimal sample preparation (Atanassova, Yorgov, & Veleva, 2023; Pu et al., 2021). FTIR and ATR-FTIR are valuable for functional group detection and surface compositional analysis, offering rapid screening capabilities (Leite et al., 2019; Silva et al., 2022). Raman and its variants, such as Surface-Enhanced Raman Spectroscopy (SERS) and Spatially Offset Raman Spectroscopy (SORS), provide molecular vibrational fingerprints useful for identifying foreign substances and analyzing samples through packaging (Arroyo-Cerezo et al., 2023; Genis et al., 2021). Furthermore, 1H NMR spectroscopy has gained prominence due to its high-resolution metabolomic profiling capabilities and its ability to differentiate PDO cheeses from non-authentic counterparts based on lipid and aqueous phase biomarkers (Haddad et al., 2022; Maestrello et al., 2024).
Advanced mass spectrometry-based techniques, including LC-MS/MS and MALDI-TOF-MS, have also been effectively utilized for the detection of protein-based adulterants and species-specific peptides in complex cheese matrices, enabling quantification at trace levels (Barbu et al., 2021; Faraji Sarabmirza et al., 2023; Kritikou et al., 2022). Additionally, Isotope Ratio Mass Spectrometry (IRMS) and other isotope-based techniques have proven crucial in verifying geographical and botanical origin by assessing stable isotope compositions such as δ13C, δ15N, and δ34S (Bontempo et al., 2019; Camin et al., 2012).
Despite the growing body of literature, a comprehensive comparison of these spectroscopic techniques in the context of cheese adulteration has yet to be systematically consolidated. This review aims to fill this gap by critically evaluating the recent advances in spectroscopic technologies employed for the detection of cheese adulteration. Emphasis is placed on analytical accuracy, detection limits, sample preparation requirements, operational efficiency, and real-world applicability. Through this systematic analysis, we aim to provide stakeholders—including researchers, quality control laboratories, and regulatory agencies—with an informed perspective on the strengths and limitations of each technique, thereby supporting the development of more robust authentication frameworks within the dairy industry.
2. Methodology
2.1. Literature search approach
To comprehensively explore the application of spectroscopic methods in the authentication of cheese and detection of its adulteration, a systematic literature search was performed across three major academic databases: Scopus, Web of Science (WoS), and PubMed. The review strategy was structured according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure methodological transparency and rigor (Moher et al., 2009). A Venn diagram was added to demonstrate the inclusion of studies from each database (Fig. 1).
Fig. 1.
Venn diagram illustrating the overlap and unique contributions of included studies retrieved from each database searched.
The search spanned all available literature from the earliest indexed records in each database up to November 2024, ensuring comprehensive temporal coverage (see Supplemental Table 1). Publications such as opinion pieces, editorials, and letters to the editor were excluded to maintain scientific robustness and data reliability.
2.2. Eligibility criteria
Studies were included if they explicitly addressed cheese analysis in the context of authenticity assessment, adulteration detection, or fraud investigation. Each selected study was required to apply at least one spectroscopic technique for the identification or quantification of adulterants. Eligible spectroscopic methods encompassed a broad range, including Hyperspectral Imaging (HSI), SORS, MS, Infrared (IR) and Mid-Infrared (MIR) Spectroscopy, Synchronous Fluorescence Spectroscopy (SFS), FTIR, NIR and Ultraviolet-Visible (UV–Vis) Spectroscopy, Raman Spectroscopy, NMR, Fluorescence Spectroscopy, and Laser-Induced Breakdown Spectroscopy (LIBS). Studies were only considered if they discussed the methodological implementation, analytical performance (e.g., sensitivity, accuracy), and, where applicable, the limitations or advantages of the techniques. Studies were excluded if they did not employ spectroscopic methods or if their focus was unrelated to cheese adulteration.
2.3. Study selection and screening process
Once the search was completed, all identified records were imported into reference management software for organization and de-duplication. Initial screening was performed based on titles and abstracts, using the inclusion criteria as filters. Full-text versions of potentially eligible articles were then reviewed in depth to confirm relevance and compliance with selection standards. To ensure objectivity and minimize bias, the entire selection and screening process was independently conducted by three reviewers, with disagreements resolved through discussion and consensus.
2.4. Data extraction and interpretation
For each study included in the final review, data were systematically extracted using a standardized form developed to capture critical details. Studies were classified based on (i) the type of cheese analyzed, (ii) the adulterant(s) targeted, and (iii) the primary spectroscopic technique(s) employed. When multiple adulterants or techniques were involved, classification was based on the dominant focus of the study as reported by the authors. Inclusion in summary tables required sufficient methodological detail, including concentration ranges, detection limits, or spectral features, to ensure comparability across datasets. The form also noted any reported advantages, constraints, calibration requirements, or combined analytical approaches. In addition, reference lists from selected articles were manually scanned to identify any further relevant publications that might have been missed during the initial search, thereby reinforcing the comprehensiveness of the review (Moher et al., 2009).
3. Results
A comprehensive and structured search across PubMed, Scopus, and Web of Science resulted in the identification of 104 publications that met the inclusion criteria and directly addressed the aims of this systematic review. The selection process, including initial retrieval, duplicate elimination, and final eligibility screening, was visually represented using a PRISMA flowchart (Fig. 2), ensuring clarity and reproducibility of the methodology. The selected studies collectively demonstrate the prevalence of species substitution and fat replacement as the most common forms of adulteration across both industrial and artisanal cheeses. These adulteration types were most frequently addressed using MALDI-TOF-MS, LC-MS/MS, NIR, and FTIR techniques, each selected for their respective resolution, speed, or matrix adaptability. The variation in methodological application highlights not only the chemical diversity of adulterants but also the critical need for technique-specific calibration and validation strategies.
Fig. 2.
PRISMA flow diagram illustrating the study selection process for the current systematic review, including identification, screening, eligibility, and inclusion stages.
The finalized set of studies encompassed a diverse array of cheese types, derived from various animal species, and were systematically classified into 20 distinct cheese categories. These were linked to eight primary spectroscopic modalities—each applied alone or in combination with others—yielding 60 unique spectroscopic application scenarios tailored to the chemical composition and physical characteristics of specific adulterants. This classification framework provides a broad and nuanced overview of the scientific landscape addressing cheese adulteration, offering insights into both the heterogeneity of fraudulent practices and the versatility of spectroscopic methods used to uncover them.
An in-depth breakdown of the categorized data is presented in Table 1, which details the associations between cheese types and adulterants, including the concentration ranges of the adulterants, the specific detection methods applied, and the sample preparation protocols required. Among the 104 reviewed studies, species substitution was addressed in 41.3 % of cases, followed by fat adulteration (21.2 %), protein-based adulteration (13.5 %), addition of non-dairy additives (11.5 %), geographical mislabeling (7.7 %), and detection of antibiotic residues (4.8 %). These proportions illustrate the dominant research focus on species- and fat-based fraud, particularly in cheeses with PDO status. This level of granularity facilitates a clear understanding of how spectroscopic approaches have been tailored to detect particular forms of adulteration across different cheese matrices. Furthermore, the 104 selected studies were analyzed for their methodological diversity in applying spectroscopic techniques. These findings have been synthesized and presented in Table 2, which offers a comparative overview of each technique based on several critical parameters including the underlying analytical principle, the reported detection accuracy, methodological limitations, and practical advantages in cheese authentication contexts. The results highlight that spectroscopic tools are not only capable of detecting a broad spectrum of adulterants—including non-declared animal proteins, vegetable oils, starches, antibiotics, and water—but are also adaptable to various cheese types, from fresh to aged and from industrial to artisanal products. These findings underscore the integral role of spectroscopy in modern food authentication and its expanding application in safeguarding cheese integrity.
Table 1.
Classification of cheese adulteration cases detected by spectroscopic techniques.
| Classification | Dairy product | Adulterant | Concentration (μg/kg) | Detection technique | Sample preparation | Reference |
|---|---|---|---|---|---|---|
| Brined Cheese | Bulgarian White Brined Cheese | Reconstituted Dry Skimmed Milk | N/A2 | NIR3 Spectroscopy | Cheese pieces analyzed directly with a reflection fiber-optics probe | (Atanassova, Yorgov, & Veleva, 2023) |
| Vegetable Oil | Measured as whole pieces | (Atanassova, Yorgov, & Veleva, 2023) | ||||
| Feta Cheese | Animal Species Differentiation | MALDI-TOF-MS4 | Direct extraction, Spotted onto MALDI plate |
(Rau et al., 2020) | ||
| Non-PDO Cheese | ICP-MS5 | Cheese samples homogenized and digested | (Danezis et al., 2020) | |||
| Feta Cheese (PDO1) | Bovine Milk | Down to 1 % | MALDI-TOF-MS | Cheese samples homogenized, proteins extracted, spotted onto MALDI plate |
(Kritikou et al., 2022) | |
| Artisanal Butter Cheese | Soybean Oil | 5 %–100 % (w/w7) | NIR Spectroscopy | Grinding and random surface measurements | (Medeiros et al., 2023) | |
| Brazilian Butter Cheese | 0–100 % (w/w) | ATR-FTIR6 Spectroscopy | Cheese samples prepared with varying levels of butter oil substitution by soybean oil | (Leite et al., 2019) | ||
| Cheese | Goat Cheese | Ovine Milk | Up to 95 % (w/w) | LC/ESI-MS/MS8 | Casein extraction, plasmin digestion, peptide analysis |
(Guarino et al., 2010) |
| Grana Padano Cheese | Glycerol | N/A | IRMS9 | Glycerol extraction, isotopic measurement for δ13C and δ18O values |
(Fronza et al., 2001) | |
| Parmigiano Reggiano Cheese | ||||||
| Trentingrana Cheese | ||||||
| Various Cheese Types | Fatty Acids, Lipid Biomarkers | HR-1H-NMR10 Spectroscopy |
Triacylglycerols extracted using a combination of Ethanol and Petroleum Ether; lipids dissolved in CDCl₃ for NMR analysis |
(Haddad et al., 2022) | ||
| Foreign Fat | NIR Spectroscopy | Grated and freeze-dried samples for better accuracy | (Rodriguez-Otero et al., 1997) | |||
| Cheese-like Product | Cheese Analogue | Milk Fat | 10–90 % (w/w) | SFS11 | Extracted fat, mixed in calibration mixtures |
(Dankowska et al., 2015) |
| Fermented Cheese | Bovine Cheese | Bovine Milk | 5–100 % (w/w) | Cheese samples prepared by mixing bovine milk with other milk types in varying ratios | (Ozer Genis et al., 2019) | |
| Buffalo Cheese | ||||||
| Ewe Cheese | ||||||
| Goat Cheese | ||||||
| Fresh Cheese | Buffalo Mozzarella Cheese | 0.4–4.6 % (w/w) | UPLC-MS/MS12 | Exudate centrifuged, digested with Trypsin |
(Russo et al., 2012) | |
| Cottage Cheese | Milk Proteins | 0.5 % (w/w) | NIR Spectroscopy | Ground sample, blended for uniformity |
(Yakubu et al., 2020) | |
| Fresh Cheese | Corn Starch | 0.0055–1.2705 % (w/w) | HSI13 | Cheese molded | (Barreto et al., 2018) | |
| Goat Cheese | Amoxicillin | 10.5 | LC-MS/MS14 | Cheese homogenized with Trisodium Citrate, centrifuged, extracted via Solid-Phase Extraction |
(Quintanilla et al., 2019) | |
| Benzylpenicillin | 12.8 | |||||
| Ciprofloxacin | 285.8 | |||||
| Cloxacillin | 109.2 | |||||
| Bovine Milk | 10 %, 15 %, 20 % | FT-NIR15 Spectroscopy | Direct cheese slice analysis | (Teixeira et al., 2021) | ||
| N/A | DIGS-MS16 | Thin lipid layer imprinted on glass surface, no matrix |
(Damario et al., 2015) | |||
| Enrofloxacin | 250.9 | LC-MS/MS | Cheese homogenized with Trisodium Citrate, centrifuged, extracted via Solid-Phase Extraction |
(Quintanilla et al., 2019) | ||
| Erythromycin | 98.38 | |||||
| Neomycin | 3916.70 | |||||
| Oxytetracycline | 154.5 | |||||
| Minas Frescal Cheese | Whey Protein | 10 % or higher | Defatting with cold Acetone, protein solubilization, Trypsin digestion |
(Alves et al., 2022) | ||
| Mozzarella Cheese | Corn Flour (Starch) | N/A | his | Direct cheese slice analysis | (Hebling e Tavares et al., 2022) | |
| Bovine Milk | 0.001 % (v/v17) | LC-MS/MS | Dilution in Bicarbonate buffer, Trypsin hydrolysis, specific peptide monitoring |
(Cajka et al., 2016) | ||
| Water | Up to 10 % (w/w) | NIR Spectroscopy | Minimal Preparation | (Cardin et al., 2022) | ||
| Mozzarella Cheese (Farmstead) | N/A | N/A | MALDI-TOF-MS | Homogenized with reduction buffer, centrifuged |
(Kandasamy et al., 2021) | |
| Mozzarella Cheese (Imported) | ||||||
| Mozzarella di Bufala Campana Cheese (PDO) | Bovine Milk | LC-MS18 | Cheese samples processed by isolating whey proteins; proteins separated using LC, analyzed by MS for β-Lactoglobulin markers |
(Czerwenka et al., 2010) | ||
| Geographical Origin Indication | HR-MAS-1H-NMR19 Spectroscopy | Direct cheese sample analysis | (Mazzei & Piccolo, 2012) | |||
| IRMS | Casein extraction, isotopic analysis |
(Bontempo et al., 2019) | ||||
| Non-Compliant Stored Milk | ICP-MS | Mineralization | ||||
| N/A | GC-MS20 | Extraction, purification, derivatization of metabolites |
(Salzano et al., 2020) | |||
| Piedmont Ricotta Cheese | Monoterpenes (e.g., α-Pinene, Limonene) | HS-SPME/GC-MS21 | Cheese sample homogenized, then 2.5 g placed in a vial and heated at 53 °C for 10 min for equilibrium |
(Giuseppe et al., 2005) | ||
| Sesquiterpenes (e.g., β-Copaene, Caryophyllene) | ||||||
| Ricotta Cheese (PDO) | Bovine Milk | As low as 5 % (w/w) | MALDI-TOF-MS | Protein extraction, tryptic digestion, peptide profiling |
(Russo et al., 2016) | |
| Swiss Cheese | Whey Protein | 3 % - 50 % (w/w) | NIR Spectroscopy (Handheld) | Cheese blocks | (Pu et al., 2021) | |
| Water Buffalo Mozzarella Cheese | Bovine Milk | 2–50 % (w/w) | MALDI-TOF-MS | Crumbled, centrifuged, diluted with TFA23 solution |
(Cozzolino et al., 2002) | |
| N/A | MALDI-MS22 | (Angeletti et al., 1998) | ||||
| Ovine Milk | 2–50 % | MALDI-TOF-MS | (Cozzolino et al., 2002) | |||
| Water Buffalo Mozzarella Cheese (PDO) | Volatile Organic Compounds | N/A | GC–MS | Finely grated sample, addition of Sodium Phosphate, internal standard and conditioning at 50 °C |
(Magliulo et al., 2024) | |
| Holstein Cheese | Starch | HS-SPME/GC–MS | Finely diced cheese | (Lee Rangel et al., 2022) | ||
| Jersey Cheese | Water | 0 % - 25 % (w/w) | ||||
| Queso Fresco Cheese | Melamine | 0.5 % - 5.5 % (w/w) | 2 g in a sealed vial | |||
| Grated Hard Cheese | Grated Hard Cheese | Microcellulose | 3–15 % (w/w) | FT-NIR Spectroscopy | Direct cheese analysis | (Visconti et al., 2024) |
| Silicon Dioxide | ||||||
| Wheat Flour | ||||||
| Wheat Semolina | ||||||
| Sawdust | ||||||
| Parmesan Cheese | Cellulose | 1.96–5.01 | NIR Spectroscopy | Ground cheese, defatting, enzymatic digestion |
(Tyl et al., 2020) | |
| 4–7 % (w/w) | Adulterants mixed and homogenized with grated cheese, analyzed directly |
(Visconti et al., 2020) | ||||
| Silicon Dioxide | ||||||
| Wheat Flour | 5–6 % (w/w) | |||||
| Wheat Semolina | ||||||
| Sawdust | ||||||
| Hard Cheese | Bergkäse Cheese | Geographical Origin Indication | N/A | IRMS | Minimal, assesses isotope ratios (δ13C and δ15N) |
(Huck-Pezzei et al., 2014) |
| Cheddar Cheese | Non-Specific Milk | FTIR24 Spectroscopy | Freeze-dried, lyophilized, homogenized |
(Tarapoulouzi & Theocharis, 2021) | ||
| 1H-NMR25 Spectroscopy | ||||||
| N/A | SIRA26 (δ13C, δ15N, δ34S, δ18O) | Petroleum spirit and Diethyl Ether extraction, pH adjustment, freeze-drying |
(O'sullivan et al., 2022) | |||
| Dalia Cheese | Vegetable Fat (e.g., Palm Oil) | 1H-NMR Spectroscopy | Fat extraction via Soxhlet, sample diluted in Deuterium Chloroform |
(Tociu et al., 2018) | ||
| Edam Cheese | Trans Fatty Acids | FTIR Spectroscopy | ATR crystal with minimal preparation | (Tociu et al., 2017) | ||
| Emmental Cheese | Foreign Fat | FT-MIR27 Spectroscopy | Homogenization of cheese samples | (Karoui, 2010) | ||
| Geographical Origin Indication | GC–MS with Purge & Trap | Grated sample homogenized in boiled water | (Pillonel, Ampuero, et al., 2003) | |||
| DR-NIR28 Spectroscopy | Grated and freeze-dried | (Pillonel, Luginbühl, et al., 2003) | ||||
| ATR-MIR29 Spectroscopy | Thin Slice | |||||
| TR-MIR30 Spectroscopy | ||||||
| Non-PDO Cheese | ATR-MIR Spectroscopy | Grated and homogenized | (Silva et al., 2022) | |||
| MC-ICP-MS31 | Freeze-dried, fat-extracted Casein fraction |
(Fortunato et al., 2004) | ||||
| Sr (Strontium) Ratio | TIMS32 | Freeze-dried, ground |
||||
| Emmental Cheese (PDO) | Botanical Origin Indication | 1H-NMR Spectroscopy | Cheese samples prepared by extracting lipids or specific metabolites | (Karoui & De Baerdemaeker, 2007) | ||
| Geographical Origin Indication | NIR Spectroscopy | Grated cheese samples placed in Petri dish, diffuse reflection measurements |
(Karoui et al., 2005) | |||
| MIR33 Spectroscopy | ||||||
| Fiore Sardo Cheese (PDO) | Proteins | Varies with ripening stage | 1H-NMR Spectroscopy | Freeze-dried aqueous extracts | (Piras et al., 2013) | |
| Organic Acids | ||||||
| Amino Acids | ||||||
| Various Bacterial Cultures | N/A | |||||
| Generic Cheese | Plant Oil | 3.0 % (w/w) | SFS | Extracted fat using Chloroform-Methanol, diluted in n-Hexane |
(Dankowska et al., 2015) | |
| Grana Padano Cheese (PDO) | N/A | N/A | GC–MS | Fat extraction, saponification, separation of hydrocarbons |
(Povolo et al., 2009) | |
| Grana Padano Cheese | Bovine Milk | MALDI-TOF-MS | Grated, dried, enzymatically digested |
(Kuckova et al., 2019) | ||
| LC-ESI-Q-TOF-MS34 | (Ostovar pour et al., 2021) | |||||
| Water | 0–25 % (w/w) | NIR Spectroscopy | Whole and grated cheese | (Pu et al., 2021) | ||
| Grana Padano Cheese (Non-PDO) | Various Mineral Elements (e.g., Sr, Cu, Mo) | Varies by element | ICP-MS | Cheese grated, homogenized, acid-digested with HNO₃, H₂O₂, ultrapure water |
(Camin et al., 2012) | |
| Grana Padano Cheese (PDO) | Counterfeit non-PDO Grana-Type | N/A | UHPLC/Q-TOF-MS35 | Thawed, ground, freeze-dried, solvent extraction, filtration |
(Rocchetti et al., 2018) | |
| Cyclopropyl Fatty Acids | 0.06–0.22 % (w/w) | GC–MS | Grated cheese samples extracted with Hexane/Acetone | (Marseglia et al., 2013) | ||
| Non-PDO Cheese | N/A | 1H-NMR Spectroscopy | Grated, aqueous and lipid fractions |
(Maestrello et al., 2024) | ||
| N/A | IRMS | Casein fraction defatted cheese | (Pianezze et al., 2020) | |||
| Graviera Cheese | Non-PDO Cheese | ICP-MS | Cheese samples homogenized and digested, elemental profile analysis |
(Danezis et al., 2020) | ||
| Graviera Cheese (PDO) | N/A | 1H-NMR Spectroscopy | Polar extraction using water, non-polar extraction with chloroform |
(Spyros & Ralli, 2023) | ||
| Graviera Naxos Cheese (PDO) | Non-PDO Milk | Sr (Strontium) Isotope Analysis | Freeze-dried and ground | (Nikezić et al., 2024) | ||
| Rennet | 11.7 % (w/w) | ICP-MS | ||||
| Sea Salt | 73.1 % (w/w) | |||||
| Gruyère Cheese | Vegetable Oil | N/A | NIR Spectroscopy | Grated, dried samples |
(Silva et al., 2022) | |
| Kasseri Cheese | Non-PDO Cheese | ICP-MS | Cheese samples homogenized and digested, elemental profile analysis |
(Danezis et al., 2020) | ||
| Kefalotyri Cheese | Non-Specific Milk | FTIR Spectroscopy | Freeze-dried, lyophilized, homogenized |
(Tarapoulouzi & Theocharis, 2021) | ||
| 1H-NMR Spectroscopy | ||||||
| Oscypek Cheese (PDO) | Up to 40 % | SPME-MS37 | Frozen, grated, and sealed in vials |
(Majcher et al., 2015) | ||
| Parmesan Cheese | Non-Milk Fat | N/A | IR38 Spectroscopy | Defatting followed by homogenization | (Dal Bosco et al., 2018) | |
| Palm Oil | 1H-NMR Spectroscopy | 50 mg with Deuterated Chloroform, agitation, filtered for analysis |
(Woodcock et al., 2008) | |||
| Proteins | SEP36 0.303 | NIR Spectroscopy (Reflectance) | No advanced preparation, quick analysis |
|||
| Starch | N/A | NIR Spectroscopy | Ground cheese | (Pu et al., 2021) | ||
| Vegetable Oil | 30–70 % (w/w) | 1H-NMR Spectroscopy | Organic extraction with deuterated chloroform, agitation, and filtration |
(Ray et al., 2023) | ||
| Parmigiano Reggiano Cheese | Grana Padano Cheese | 300–830 | GC–MS | Fat extracted from cheese, methylated |
(Caligiani et al., 2016) | |
| Mycotoxins | Low PPM39 Levels | HPLC-MS40 | Cheese samples homogenized, extracted using solid-phase extraction |
(Di Stefano et al., 2012) | ||
| Non-Italian Milk | N/A | LC-HR-MS41 | Extraction with acidic Water/Acetonitrile solution, centrifugation, and defatting |
(Popping et al., 2017) | ||
| Non-Native Amino Acids | Varies by Amino Acid Content | 1H-NMR Spectroscopy | Minimal Preparation | (Marcone et al., 2013) | ||
| Non-PDO Cheese | N/A | IRMS | Defatted cheese or casein extraction | (Camin et al., 2016) | ||
| Quality Variance | GC–MS | 2 g of grated cheese placed in a vial, sealed for headspace analysis |
(Bhandari et al., 2016) | |||
| Ricotta Cheese | FTIR Spectroscopy | Grated, freeze-dried |
(Maestrello et al., 2024) | |||
| Rind Presence | NIR-HSI42 | Grated and mixed with known ratios | (Hebling e Tavares et al., 2022) | |||
| Parmigiano Reggiano Cheese (PDO) | N/A | IRMS | Casein fraction defatted cheese | (Pianezze et al., 2020) | ||
| Rind Presence | >18 % (w/w) | UHPLC-Orbitrap-MS43 | Extraction with Acetonitrile, Formic Acid; filtration |
(Becchi et al., 2024) | ||
| Stable Isotopes (H, C, N, S) | N/A (Ratios) | IRMS | Casein extracted from grated cheese, defatted, washed, lyophilized |
(Camin et al., 2012) | ||
| Pecorino Romano Cheese | Bovine Milk | N/A | SORS44 | Sealed packaging analysis, No preparation |
(Ostovar pour et al., 2021) | |
| Pecorino Siciliano Cheese (PDO) | N/A | IRMS | Defatting, freeze-drying, and analysis of stable isotopes |
(Valenti et al., 2017) | ||
| Pecorino Toscano Cheese (PDO) | Geographical Origin Indication | Homogenized and centrifuged to separate casein | (Faberi et al., 2018) | |||
| Rucar Cheese | Vegetable Fat (e.g., Palm Oil) | 1H-NMR Spectroscopy | Fat extraction via Soxhlet, sample diluted in Deuterium Chloroform |
(Tociu et al., 2018) | ||
| Telemea Cheese | ||||||
| Vintage Cheddar Cheese | Bovine Milk | SORS | Sealed packaging analysis, No preparation |
(Ostovar pour et al., 2021) | ||
| Industrial Cheese | Oscypek-Like Cheese | Geographical Origin Indication | SPME-MS | Grated and stored under vacuum | (Majcher et al., 2015) | |
| Mixed Cheese | Mixed Milk (Cow, Goat, Sheep) Cheese | N/A | DIGS-MS | Thin lipid layer imprinted on glass surface, no matrix |
(Damario et al., 2015) | |
| Mountain Cheese | Asiago Cheese | Sesquiterpenes (e.g., β-Caryophyllene, α-Humulene) | 21–65 | SPME-GC-MS45 | Cheese samples were grated, heated to 50 °C, and distilled under vacuum |
(Favaro et al., 2005) |
| Vegetable Oil | Up to 10 % (w/w) | UHPLC/Q-TOF-MS | Grated, freeze-dried |
(Maestrello et al., 2024) | ||
| Asiago Cheese (PDO) | Diet Variation | N/A | 1H-NMR Spectroscopy | homogenized and prepared | (Balthazar et al., 2021) | |
| Geographical Origin Indication | δ13C, δ15N Ratio | IRMS | Homogenized, freeze-dried samples for isotopic analysis |
(Faberi et al., 2018) | ||
| Asiago d'Allevo Cheese (PDO) | N/A | NIR Spectroscopy | Cheese samples collected from different locations and ripening stages | (Ottavian et al., 2012) | ||
| Gruyère Cheese (PDO) | MIR Spectroscopy | Cheese samples collected from varying altitudes; slices placed on crystal |
(Karoui et al., 2007) | |||
| L'Etivaz Cheese (PDO) | ||||||
| Pasta Filata Cheese | Kashar Cheese | Emulsifying Salts | ATR-FT-MIR46 Spectroscopy | Cheese samples grated, stored in sealed containers |
(Ozturk et al., 2022) | |
| Mozzarella di Bufala Campana Cheese | Frozen Curd | 15 %, 30 %, 50 % | TD-NMR47 Relaxometry | Sliced samples | (Mengucci et al., 2021) | |
| N/A | Not specified | GC–MS | Fat extraction, saponification, separation of hydrocarbons |
(Povolo et al., 2009) | ||
| Provolone Cheese | ||||||
| Water Buffalo Mozzarella Cheese | Volatile Compounds (e.g., alcohols, aldehydes, ketones) | Varies by Compound | HRGC-MS48 | Cheese samples prepared with natural whey cultures; Volatile compounds extracted from whey after curd ripening |
(Mauriello et al., 2003) | |
| Pressed Semi-Cooked Curd | Abondance Cheese | Preserved Forage Diet | Not specified | Vis/NIR49 Spectroscopy | Fresh or freeze-dried | (Andueza et al., 2013) |
| Pressed Uncooked Curd | Cantal Cheese | Pasture Diet | ||||
| Tomme de Savoie Cheese | Preserved Forage Diet | |||||
| Processed Cheese | Gouda Cheese | Proteins | Low Levels | FT-NIR Spectroscopy | Smoothing and SNV pretreatment | (Woodcock et al., 2008) |
| Fats | ||||||
| Processed Cheese | Casein | N/A | NIR Spectroscopy | Minimal Preparation | (Vlasiou, 2023) | |
| Ripened Cheese | Cas Cheese | Bovine Milk | IRMS | Extraction of casein and isotopic analysis | (Magdas et al., 2019) | |
| Model Cheese | Dairy System Differentiation | PTR-TOF-MS50 | VOC52 analysis of 1.1 cm diameter cheese core, freeze-stored and then analyzed |
(Bergamaschi, Cecchinato, & Bittante, 2020) | ||
| Farming System of Origin | NIR Spectroscopy | Grinding, placed in a 100 mm diameter ring cup |
(Bergamaschi, Cipolat-Gotet, et al., 2020) | |||
| PTR-TOF-MS | Grinding, analyzed for volatile compounds |
|||||
| Semi-Hard Cheese | Asiago Cheese | Fatty Acids | 1H-NMR Spectroscopy | Minimal, assessing fatty acid content affected by grazing diet |
(Marcone et al., 2013) | |
| Caciotta Cheese | Elemental Composition Variation | Region-Dependent | ICP-MS | Acid digestion for trace element profiling | (Cardin et al., 2024) | |
| Caciotta Cheese (PDO) | Geographical Origin Indication | NIR Spectroscopy | Cheese ground, scanned in reflectance mode |
|||
| Cantal Cheese | Plant-Derived Terpenes | N/A | DH-GC-MS51 | Centrifuged fat extraction, dynamic headspace |
(Cornu et al., 2005) | |
| Cantal Cheese (PDO) | Diet Variation | MIR Spectroscopy | Milk samples collected from dairy herds | (Coppa et al., 2021) | ||
| Cheddar Cheese | Melamine | 0.5 % - 5.5 % (w/w) | NIR Spectroscopy (Portable) | Ground cheese | (Pu et al., 2021) | |
| Moisture | 40–50 % (w/w) | NIR Spectroscopy (Reflectance) | Basic preparation, no extensive treatment |
(Woodcock et al., 2008) | ||
| Non-dairy Fats | ≥5 % (w/w) | GC-MS | Sample preparation via fat extraction and derivatization | (Karoui, 2010) | ||
| Vegetable Oil | 1 % (w/w) | DART-Orbitrap-MS53 | Extraction with toluene, followed by centrifugation and analysis |
(Cajka et al., 2016) | ||
| Commercial Cumin Cheese | Non-PDO Cheese | N/A | PTR-TOF-MS | Cubes of 3 g, headspace sampling at 30 °C |
(Galle et al., 2011) | |
| Edam Cheese | Whey Protein | NMR Relaxometry | Sealed package, minimal preparation |
(Alekseev & Khripov, 2014) | ||
| Emmental Cheese | Cheese Analogue | SERDS54 | Cylindrical subsamples, 2 cm diameter |
(Sowoidnich & Kronfeldt, 2016) | ||
| Feta Cheese | Vegetable Oil | Variable | IR Spectroscopy | Ethanolic extraction | (Vlasiou, 2023) | |
| Gouda Cheese | Fats | 2 % - 10 % (w/w) | ATR-FTIR Spectroscopy | Sliced or grated | (Silva et al., 2022) | |
| Casein | Up to 3 % (w/w) | NMR55 Spectroscopy | Sealed package, No preparation |
(Alekseev & Khripov, 2014) | ||
| Milk Powder | 10–30 % (w/w) | FTIR Spectroscopy | Dilution | (Cardin et al., 2022) | ||
| Vegetable Oil | 1 % (w/w) | NIR Spectroscopy | Minimal preparation, often no extraction |
(Yakubu et al., 2020) | ||
| Halloumi Cheese | Bovine Milk | Variable | FTIR Spectroscopy | Freeze-drying of samples | (Tarapoulouzi et al., 2020) | |
| N/A | Freeze-dried, lyophilized, homogenized |
(Tarapoulouzi & Theocharis, 2021) | ||||
| 1H-NMR Spectroscopy | ||||||
| Leiden Cheese (PDO) | Non-PDO Cheese | HS-PTR-MS56 | Cubes of 3 g, headspace sampling at 30 °C |
(Galle et al., 2011) | ||
| Manchego Cheese | Geographical Origin Indication | SORS | Sealed packaging analysis, No preparation |
(Ostovar pour et al., 2021) | ||
| Mountain Alpine Cheese | N/A | GC-MS | Fat extraction, thin-layer chromatography for hydrocarbon isolation |
(Povolo et al., 2009) | ||
| Pecorino Cheese | Geographical Origin Indication | Variable | C, N Isotope Analysis | Casein analysis | (Camin et al., 2016) | |
| Region-Dependent | LIBS57 | Dried, powdered, and pellet-formed cheese sample |
(Markiewicz-Keszycka et al., 2019) | |||
| Prato Cheese | Mislabeling | N/A | MIR Spectroscopy | Cheese samples analyzed directly on ATR crystal | (de Tolentino et al., 2023) | |
| Raclette Cheese | Cheese Analogue | SERDS | Cylindrical subsamples, 2 cm diameter |
(Sowoidnich & Kronfeldt, 2016) | ||
| Saint-Nectaire Cheese (PDO) | N/A | ATR-MIR Spectroscopy | Thin Slice | (Boubellouta et al., 2010) | ||
| Fluorescence Spectroscopy | Direct cheese analysis | |||||
| Semi-Soft Cheese | Saint-Nectaire Cheese | Plant-Derived Terpenes | DH-GC-MS | Centrifuged fat extraction, dynamic headspace |
(Cornu et al., 2005) | |
| Taleggio Cheese (PDO) | Geographical Origin Indication | δ13C, δ15N Ratio | IRMS | Casein and fat extracted, lyophilized, and homogenized |
(Faberi et al., 2018) | |
| Soft Cheese | Azeitão Cheese (PDO) | Animal Rennet | N/A | HPLC-DAD58 | Extraction of flavonoids from curd | (Roseiro et al., 2005) |
| Brie Cheese | Vegetable Fat | Up to 15 % (w/w) | 1H-NMR Spectroscopy | Simple extraction | (Cardin et al., 2022) | |
| Camembert Cheese | Fats | SEP up to ∼1 % (w/w) | ATR-MIR Spectroscopy | Requires stabilization before spectral recording | (Woodcock et al., 2008) | |
| Cottage Cheese | Urea | N/A | 1H-NMR Spectroscopy | Minimal preparation | (Vlasiou, 2023) | |
| Crescenza Cheese | N/A | GC-MS | Fat extraction, saponification, separation of hydrocarbons |
(Povolo et al., 2009) | ||
| Goat Cheese | Bovine Milk | MALDI-TOF-MS | Grated, dried, enzymatically digested |
(Kuckova et al., 2019) | ||
| LC-ESI-Q-TOF-MS | ||||||
| ≥1 % (w/w) | FT-NIR Spectroscopy | Grated | (Dvorak et al., 2016) | |||
| Mozzarella Cheese | Other Dairy Products | N/A | NIR Spectroscopy | Pre-equilibrated at 22 °C | (Huck-Pezzei et al., 2014) | |
| Geographical Origin Indication | Variable | Multi-Element Isotope Analysis | Bulk product | (Camin et al., 2016) | ||
| Starch | Up to 5 % (w/w) | MIR Spectroscopy | Homogenized with minimal preparation | (Silva et al., 2022) | ||
| Whey Protein | Variable | ATR-FTIR Spectroscopy | Sliced | (Maestrello et al., 2024) | ||
| Mozzarella di Bufala Campana Cheese | Bovine Milk | ≥5 % (w/w) | LC-MS/MS | Liquid-liquid extraction, saponification |
(Dal Bosco et al., 2018) | |
| Ricotta Cheese | Whey Protein | 1 %–50 % (v/v) | Protein extraction and digestion with trypsin | (Camerini et al., 2016) | ||
| Non-Milk Fat | N/A | NIR Spectroscopy (Reflectance) | Homogenized for uniformity | (Hebling e Tavares et al., 2022) | ||
| Serpa Cheese (PDO) | Animal Rennet | HPLC-DAD | Extraction of flavonoids from curd | (Roseiro et al., 2005) | ||
| Soft cheese | Vegetable Oil (rapeseed, sunflower, soybean) | 1 % (w/w) | DART-HRMS59 | Toluene extraction | (Hrbek et al., 2013) | |
| Traditional Cheese | Anari Cheese (PDO) | Animal Species Differentiation | N/A | FTIR Spectroscopy | Freeze-dried samples, analyzed as KBr pellets |
(Tarapoulouzi & Theocharis, 2023) |
| Boerenkaas Cheese | Heat-Treated Milk Proteins | LC-MS | Protein extraction, chromatographic separation |
(Barbu et al., 2021) | ||
| Erzincan Tulum cheese (PDO) | Starch | FTIR Spectroscopy | Ethanol and n-Hexane extraction, dried on Zinc Selenide crystal |
(Menevseoglu et al., 2023) | ||
| Bovine Milk | ||||||
| Vegetable Oil | ||||||
| Ultra-Filtered Cheese | Ultra-Filtered White Cheese | Margarine | 2.5 %–25 % (w/w) | Raman Spectroscopy | Lipid extraction using Folch method | (Ozer Genis et al., 2020) |
| Corn Oil | ||||||
| Pal Oil |
Protected Designation of Origin.
Not Available.
Near-Infrared Spectroscopy.
Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry.
Inductively Coupled Plasma Mass Spectrometry.
Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy.
Weight/Weight.
Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry.
Isotope Ratio Mass Spectrometry.
High-Resolution Proton Nuclear Magnetic Resonance Spectroscopy.
Synchronized Fluorescence Spectroscopy.
Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry.
Hyperspectral Imaging.
Liquid Chromatography-Tandem Mass Spectrometry.
Fourier Transform Near-Infrared Spectroscopy.
Direct Injection Gas Chromatography-Mass Spectrometry.
Volume/Volume.
Liquid Chromatography-Mass Spectrometry.
High Resolution Magic Angle Spinning Proton Nuclear Magnetic Resonance Spectroscopy.
Gas Chromatography-Mass Spectrometry.
Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry.
Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry.
Trifluoroacetic Acid Solution.
Fourier Transform Infrared Spectroscopy.
Proton Nuclear Magnetic Resonance Spectroscopy.
Stable Isotope Ratio Analysis.
Fourier Transform Mid-Infrared Spectroscopy.
Diffuse Reflectance Near-Infrared Spectroscopy.
Attenuated Total Reflectance Mid-Infrared Spectroscopy.
Transmittance Mid-Infrared Spectroscopy.
Multicollector Inductively Coupled Plasma Mass Spectrometry.
Thermal Ionization Mass Spectrometry.
Mid-Infrared Spectroscopy.
Liquid Chromatography-Electrospray Ionization-Quadrupole Time Of Flight Mass Spectrometry.
Ultra High-Performance Liquid Chromatography/Quadrupole Time Of Flight Mass Spectrometry.
Standard Error of Proportion.
Solid Phase Microextraction-Mass Spectrometry.
Infrared Spectroscopy.
Part Per Million.
High Performance Liquid Chromatography-Mass Spectrometry.
Liquid Chromatography-High Resolution Mass Spectrometry.
Near-Infrared Hyperspectral Imaging.
Ultra High-Performance Liquid Chromatography-Orbitrap Mass Spectrometry.
Surface Enhanced Raman Spectroscopy.
Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry.
Attenuated Total Reflectance-Fourier Transform Mid-Infrared Spectroscopy.
Time Domain-Nuclear Magnetic Resonance.
High Resolution Gas Chromatography-Mass Spectrometry.
Visible/Near-Infrared Spectroscopy.
Proton Transfer Reaction-Time of Flight Mass Spectrometry.
Dynamic Headspace-Gas Chromatography-Mass Spectrometry.
Volatile Organic Compound.
Direct Analysis in Real Time-Orbitrap Mass Spectrometry.
Second Order Differential Spectroscopy.
Nuclear Magnetic Resonance Spectroscopy.
Headspace-Proton Transfer Reaction-Mass Spectrometry.
Laser Induced Breakdown Spectroscopy.
High Performance Liquid Chromatography with Diode Array Detection.
Direct Analysis in Real Time-High Resolution Mass Spectrometry.
Table 2.
Key features of spectroscopic methods, constraints, and applicability for cheese authentication.
| Spectroscopic Technique | Principle | Detection Accuracy | Limitation | Benefits | Reference |
|---|---|---|---|---|---|
| 1H-NMR1 Spectroscopy | Chemical Shift Measurement in Proton environment for Compound Identification | Effective Ripening Stage Characterization | Specialized Equipment Complex Sample Preparation |
Detailed Chemical Fingerprints High Sensitivity (PDO2 Authentication) |
(Piras et al., 2013) |
| High Accuracy | Expensive Equipment Complex Sample Preparation |
Complex Mixture Analysis | (Cardin et al., 2022) | ||
| Sensitive to Matrix Complexity Expensive Equipment |
Fast Detection Minimal Sample Preparation Sample Integrity Maintenance |
(Marcone et al., 2013) | |||
| Complex Sample Preparation | High Specificity | (Tarapoulouzi & Theocharis, 2021) | |||
| Expensive Equipment Complex Sample Preparation |
(Maestrello et al., 2024) | ||||
| Limited to Specific Compounds (Casein) | Non-destructive Detection Suitable for Sealed Packages |
(Alekseev & Khripov, 2014) | |||
| High Accuracy for Botanical and Geographic Origin Indication | Expensive Equipment Trained Personnel |
Non-destructive Detection Detailed Chemical Fingerprints High Sensitivity (PDO Authentication) |
(Karoui & De Baerdemaeker, 2007) | ||
| High Accuracy for Detailed Compound Analysis | Detailed Chemical Fingerprints Minimal Sample Preparation |
(Vlasiou, 2023) | |||
| High Accuracy for Diet Origin Discrimination | Expensive Equipment Complex Sample Preparation |
Non-destructive Detection High Sensitivity (PDO Authentication) |
(Balthazar et al., 2021) | ||
| High Accuracy for Lipid Composition | Detailed Chemical Fingerprints (Fatty Acid Profile) High Sensitivity (PDO Authentication) |
(Haddad et al., 2022) | |||
| Complex Sample Preparation Chemometrics Equations Required |
Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Tociu et al., 2018) | |||
| Limited to Lipid Analysis Baseline Comparison Required |
Minimal Sample Preparation High Throughput Sensitive to Adulteration |
(Ray et al., 2023) | |||
| High Accuracy for Metabolite Differentiation Composition | Complex Sample Preparation | High Sensitivity (PDO Authentication) | (Spyros & Ralli, 2023) | ||
| API-MS3 | Molecule Ionization at Atmospheric pressure for Mass Analysis | High Accuracy | Specialized Equipment Sensitive to Contamination |
High Sensitivity Suitable for Protein Analysis |
(Angeletti et al., 1998) |
| High Specificity High Sensitivity (PDO Authentication) |
(Cozzolino et al., 2002) | ||||
| ATR-FT-MIR4 Spectroscopy | Molecular Vibrational Mode Measurement in the Mid-Infrared Region in ATR Crystal using Fourier Transform | High Accuracy for Cheese Processing Method Differentiation | Complex Sample Preparation | Fast Detection Non-destructive Detection Ideal Distinguishing for Different Cheeses |
(Ozturk et al., 2022) |
| ATR-FTIR5 Spectroscopy | Molecular Vibrational Mode Measurement in the Infrared Region in ATR Crystal using Fourier Transform | High Accuracy | Limited to Surface Analysis (Preprocessing for Bulkier Samples Required) |
Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Silva et al., 2022) |
| Limited to Surface Analysis | Fast Detection Non-destructive Detection Sensitive to Adulteration |
(Leite et al., 2019) | |||
| Moderate Accuracy | Minimal Sample Preparation Suitable for Solids |
(Maestrello et al., 2024) | |||
| ATR-MIR6 Spectroscopy | Molecular Vibrational Mode Measurement in the Mid-Infrared Region in ATR Crystal | High Accuracy for Metabolite Differentiation Composition | Detailed Chemical Fingerprints (Composition Analysis) High Sensitivity (PDO Authentication) |
(Boubellouta et al., 2010) | |
| Moderate Accuracy | Detailed Chemical Fingerprints (Composition Analysis) | (Pillonel, Luginbühl, et al., 2003) | |||
| Moderate to High Accuracy | Expensive Equipment Limited Portability |
(Woodcock et al., 2008) | |||
| DART-HRMS7 | Combining Ambient Ionization with High Resolution Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge Ratio | High Accuracy (1 % adulteration Level Detection for Plant Oil) |
Sensitive to Lipid Variability (Triacylglycerol) | Fast Detection Minimal Sample Preparation Effective for TAG-based Detection |
(Hrbek et al., 2013) |
| DART-Orbitrap-MS8 | Combining Ambient Ionization with Orbitrap Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge Ratio | High Accuracy (Dependent on Sample Preparation) |
Limited to Nonpolar Particles | Fast Detection Minimal Sample Preparation High Sensitivity |
(Cajka et al., 2016) |
| DH-GC-MS9 | Combining Dynamic Headspace Sampling with Gas Chromatography-Mass Spectrometry for Volatile Compound Analysis | High Accuracy | Specialized Equipment | High Sensitivity (Low-Concentration Volatile Compounds) | (Cornu et al., 2005) |
| DIGS-MS10 | Combining Affinity Purification with Mass Spectrometry for Ligand-Protein interaction Identification | High Accuracy for Lipid Composition | Limited to Lipid Detection Limited by Spectral Overlap |
Minimal Sample Preparation high Signal Cleanliness No Matrix Interference |
(Damario et al., 2015) |
| DR-NIR11 Spectroscopy | Molecular Vibrational Mode Measurement of the Diffuse Reflected Light in the Near-Infrared Region | High Accuracy for Regional Differentiation | Limited by Spectral Overlap | Fast Detection Minimal Sample Preparation Suitable for Grated Samples |
(Pillonel, Luginbühl, et al., 2003) |
| Fluorescence Spectroscopy | Emission Measurement of Light by a Compound after Electromagnetic Radiation Absorption | High Accuracy for Metabolite Differentiation Composition | Sensitive to Surface Conditions | Non-invasive Detection Fast Detection High Sensitivity (PDO Authentication) |
(Boubellouta et al., 2010) |
| FT-MIR12 Spectroscopy | Molecular Vibrational Mode Measurement in the Mid-Infrared Region using Fourier Transform | High Accuracy for Lipid Composition | Limited by Spectral Overlap | Fast Detection Non-destructive Detection Suitable for Fat Composition Analysis |
(Karoui, 2010) |
| FT-NIR13 Spectroscopy | Molecular Vibrational Mode Measurement in the Near-Infrared Region using Fourier Transform | 100 % Sensitivity and Specificity | Sensitive to Moisture Interference Preprocessing Required |
Fast Detection Non-destructive Detection Suitable for Direct Analysis of Cheese |
(da Teixeira et al., 2021) |
| High Accuracy | Complex Sample Preparation | Complex Matrix Analysis | (Silva et al., 2022) | ||
| Chemometrics Equations Required | Fast Detection Non-destructive Detection Minimal (No) Sample Preparation |
(Visconti et al., 2024) | |||
| Calibration Maybe Required (Specific Adulterants) | Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Dvorak et al., 2016) | |||
| High Accuracy (R2 ∼ 0.96 for Proteins) |
Complex Sample Preparation | Detailed Chemical Fingerprints | (Pu et al., 2021) | ||
| Very High Accuracy (SEP15 ∼ 0.5 %) |
Sensitive to Moisture Interference | Fast Detection Non-destructive Detection Minimal Calibration |
(Woodcock et al., 2008) | ||
| FTIR14 Spectroscopy | Molecular Vibrational Mode Measurement in the Infrared Region using Fourier Transform | High Accuracy | Sensitive to Moisture Interference Complex Sample Preparation |
Fast Detection Minimal Sample Preparation |
(Menevseoglu et al., 2023) |
| Sensitive to Moisture Interference | Fast Detection Non-destructive Detection |
(Tarapoulouzi & Theocharis, 2021) | |||
| High Accuracy for Origin Discrimination | Sensitive to Moisture Interference Complex Sample Preparation |
Fast Detection Minimal Sample Preparation |
(Tarapoulouzi & Theocharis, 2023) | ||
| Chemometrics Equations Required | Fast Detection Non-destructive Detection Minimal Sample Preparation High Sensitivity (PDO Authentication) |
(Tarapoulouzi et al., 2020) | |||
| Moderate to High Accuracy | Limited to Functional Group Identification | Fast Detection Non-destructive Detection |
(Maestrello et al., 2024) | ||
| Sensitive to Contamination | Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Tociu et al., 2017) | |||
| Limited to Functional Group Identification Non-Quantitative Analysis |
Minimal Sample Preparation Good for Routine Analysis |
(Cardin et al., 2022) | |||
| GC-MS16 | Combining Gas Chromatography with Mass Spectrometry for Compound Seperation and Analysis based on Mass-to-Charge Ratio | High Accuracy | Complex Sample Preparation Sensitive to Contamination |
High Sensitivity High Specificity |
(Caligiani et al., 2016) |
| Complex Sample Preparation | High Sensitivity (PDO Authentication and tracibility) | (Salzano et al., 2020) | |||
| Complex Sample Preparation (Saponification) | (Povolo et al., 2009) | ||||
| Specialized Equipment Complex Sample Preparation |
High Sensitivity (Volatile Compounds) High Specificity (Volatile Compounds) |
(Bhandari et al., 2016) | |||
| Complex Sample Preparation Sensitivity Dependent on Compound |
High Specificity Detailed Chemical Fingerprints |
(Giuseppe et al., 2005) | |||
| High Accuracy for Lipid Composition | Complex Sample Preparation | High Sensitivity (PDO Authentication) Precise Quantification of Trace Compounds |
(Marseglia et al., 2013) | ||
| Derivatization for Non-volatile Compounds Required | High Specificity (Foreign Fat) | (Karoui, 2010) | |||
| High Accuracy for Volatile Compound Detection | Complex Sample Preparation | Detailed Compound Identification | (Pillonel, Ampuero, et al., 2003) | ||
| Moderate Accuracy | High Sensitivity (Aroma Profiles) | (Magliulo et al., 2024) | |||
| Very High Accuracy | Complex Mixture Analysis | (Cornu et al., 2005) | |||
| HPLC-DAD17 | Combining High-Pressure Liquid Chromatography with Diode Array Detection for Light Absorbance Measurement across a range of Wavelengths | High Accuracy for Marker Compounds | Specialized Equipment | Cynara Coagulant Cheese Authentication | (Roseiro et al., 2005) |
| HPLC-MS18 | Combining High-Pressure Liquid Chromatography with Mass Spectrometry for Compound Seperation and Analysis based on Mass-to-Charge Ratio | Complex Sample Preparation (Complex Matrices) |
High Sensitivity Ideal Contamination Detection (Mycotoxin) |
(Di Stefano et al., 2012) | |
| HR-MAS-1H-NMR19 Spectroscopy | Chemical Shift Measurement in Proton environment for Compound Identification using Magic Anlge Spinning for Line Broadening Reduction | High Accuracy for Metabolite Differentiation Composition | Specialized Equipment Complex Sample Preparation |
Non-destructive Detection Detailed Chemical Fingerprints (Metabolomic) High Sensitivity (PDO Authentication) |
(Mazzei & Piccolo, 2012) |
| HRGC-MS20 | Combining High Resolution Gas Chromatography with Mass Spectrometry for Compound Separation and Analysis based on Mass-to-Charge Ratio | High Accuracy for Volatile Compound Detection | Limited to Volatile Compounds Complex Sample Preparation |
High Sensitivity (Aroma Profiles and PDO Authentication) | (Mauriello et al., 2003) |
| HRMS21 | High Resolution Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge Ratio | Moderate Accuracy for Regional Differentiation | Limited to Pattern Recognition without Compound Identification | Fast Detection High Throughput |
(Pillonel, Ampuero, et al., 2003) |
| Moderate to High Accuracy | Variable Sensitivity (Based on Calibration) |
Fast Detection Non-destructive Detection |
(Pu et al., 2021) | ||
| HS-PTR-MS22 | Combining Headspace Sampling with Proton Transfer Reaction and Mass Spectrometry for Volatile Compound Analysis | High Accuracy | Limited to Specific Compounds | Fast Detection Non-destructive Detection High Sensitivity |
(Galle et al., 2011) |
| HS-SPME/GC-MS23 | Combining Dynamic Headspace Sampling and Solid-Phase Microextraction with Gas Chromatography-Mass Spectrometry for Volatile Compound Analysis | High Accuracy for Volatile Compound Detection | Complex Sample Preparation | High Sensitivity (Aroma Profiles) | (Pu et al., 2021) |
| Limited to Volatile Compounds | High Sensitivity (Aroma Profiles) High Specificity (Volatile Compounds) |
(Giuseppe et al., 2005) | |||
| HSI24 | Spatial and Spectral Information Capture across a wide range of Wavelengths | High Accuracy | Specialized Equipment | Starch and Fluor Adulterant Detection | (Hebling e Tavares et al., 2022) |
| High Accuracy (R2 ∼ 0.9915) |
Limited to Specific Compounds (Calibration Required) |
Fast Detection Non-destructive Detection Starch Adulterant Detection |
(Barreto et al., 2018) | ||
| ICP-MS25 | Combining Plasma Ionization with Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge Ratio | High Accuracy | Complex Sample Preparation | High Specificity High Sensitivity (PDO Authentication) |
(Cardin et al., 2024) |
| High Accuracy for Geographic Origin Indication | Specialized Equipment Complex Sample Preparation |
High Specificity (Chemical Fingerprinting) High Sensitivity (Chemical Fingerprinting) |
(Danezis et al., 2020) | ||
| High Accuracy for Marker Compounds | Complex Sample Preparation | High Sensitivity Simultaneous Element Detection |
(Camin et al., 2012) | ||
| High Accuracy for Metabolite Differentiation Composition | Complex Sample Preparation Mineralization Required |
High Sensitivity (Elemental Profiles) | (Bontempo et al., 2019) | ||
| Very High Accuracy | Expensive Equipment Trained Personnel |
Accurate Quantification (Elemental Composition) | (Nikezić et al., 2024) | ||
| IR26 Spectroscopy | Molecular Vibrational Mode Measurement in the Infrared Region | Moderate Accuracy | Limited by Spectral Overlap Baseline Comparison Required |
Fast Detection Minimal Sample Preparation Versatile Detection |
(Vlasiou, 2023) |
| Limited by Spectral Overlap | Fast Detection Non-destructive Detection High Sensitivity (Fats) |
(Dal Bosco et al., 2018) | |||
| IRMS27 | Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge Ratio and Relative Isotope Abundance | High Accuracy for Geographic Origin Indication | Limited to Specific Compounds (Casein) | Accurate Origin Discrimination | (Pianezze et al., 2020) |
| Expensive Equipment Specialized Equipment |
Accurate Origin Discrimination (Isotopic Composition) | (Huck-Pezzei et al., 2014) | |||
| Complex Sample Preparation | Accurate Species Discrimination | (Magdas et al., 2019) | |||
| Expensive Equipment | High Sensitivity (PDO Authentication) | (Bontempo et al., 2019) | |||
| High Accuracy for Lipid Composition | Complex Sample Preparation | High Sensitivity (PDO Authentication and tracibility) | (Fronza et al., 2001) | ||
| High Accuracy for Origin Discrimination | Sensitive to Isotope Ratio Variation | Accurate Origin Discrimination | (Faberi et al., 2018) | ||
| Sensitive to Feed Variation | High Sensitivity (PDO Authentication) | (Valenti et al., 2017) | |||
| Expensive Equipment | High Sensitivity (PDO/PGI28 Authentication) | (Camin et al., 2016) | |||
| Sensitive to Environmental Factors (Affecting δ15N) |
High Sensitivity (Soil Geology) | (Pianezze et al., 2020) | |||
| Very High Accuracy for Isotope Detection | Limited to Stable Isotope Complex Sample Preparation |
Non-destructive Detection High Sensitivity (PDO Authentication and traceability) |
(Camin et al., 2012) | ||
| Isotope Analysis | Isotopic Composition Differentiation based on δ13C and δ15N | Moderate Accuracy | Limited to Carbon and Nitrogen | Minimal Sample Preparation Good for Routine Analysis |
(Camin et al., 2016) |
| LC-ESI-MS/MS29 | Combining Liquid Chromatography for Sample Seperation, and Electrospray for Sample Ionization with Mass Spectrometry for Compound Analysis based on Mass-to-Charge Ratio | High Accuracy | Complex Sample Preparation | High Sensitivity Quantitative Detection |
(Guarino et al., 2010) |
| LC-ESI-Q-TOF-MS30 | Combining Liquid Chromatography for Sample Seperation, Electrospray for Sample Ionization, and Quadrupole Filtering with Time-of-Flight Mass Spectrometry for Compound Analysis based on Mass-to-Charge Ratio | Very High Accuracy | Detailed Chemical Fingerprints (Molecular Information) | (Kuckova et al., 2019) | |
| LC-HRMS31 | Combining Liquid Chromatography with High Resolution Mass Spectrometry for Compound Seperation and Analysis based on Mass-to-Charge Ratio | High Accuracy | Specialized Equipment Trained Personnel |
Non-targeted Detection High Sensitivity (PDO Authentication) |
(Popping et al., 2017) |
| LC-MS32 | Combining Liquid Chromatography with Mass Spectrometry for Compound Seperation and Analysis based on Mass-to-Charge Ratio | Expensive Equipment Complex Sample Preparation |
High Sensitivity (PDO/TSG34 Authentication) | (Barbu et al., 2021) | |
| High Accuracy for Marker Compounds | Specialized Equipment Complex Sample Preparation |
Fast Detection High Sensitivity (PDO Authentication) |
(Czerwenka et al., 2010) | ||
| LC-MS/MS33 | High Accuracy | Complex Sample Preparation | High Specificity High Throughput |
(Valdemiro Alves et al., 2022) | |
| High Accuracy (5 % Sensitivity Threshold) |
Specialized Equipment | High Sensitivity High Specificity |
(Dal Bosco et al., 2018) | ||
| High Accuracy (R2 > 0.99) |
Trained Personnel Complex Sample Preparation |
High Sensitivity Suitable for Forensic Detection |
(Camerini et al., 2016) | ||
| High Accuracy for Metabolite Differentiation Composition | Complex Sample Preparation | High Sensitivity High Specificity |
(Quintanilla et al., 2019) | ||
| Very High Accuracy for Marker Compounds | Expensive Equipment Complex Sample Preparation |
High Specificity | (Cajka et al., 2016) | ||
| LIBS35 | Emission Measurement of Light by a Compound's Surface Plasma after High-Energy Laser Pulsation | High Accuracy for Mineral Composition | Limited to Specific Compounds (Organic) | Fast Detection Minimal Sample Preparation Suitable for On-Site Verification |
(Markiewicz-Keszycka et al., 2019) |
| MALDI-MS36 | Combining Matrix-Assisted Laser with Mass Spectrometry for Compound Ionization, Desorption and Analysis based on Mass-to-Charge Ratio | High Accuracy | Trained Personnel Complex Sample Preparation |
Fast Detection High Specificity |
(Angeletti et al., 1998) |
| MALDI-TOF-MS37 | Combining Matrix-Assisted Laser with Time-of-Flight Mass Spectrometry for Compound Ionization, Desorption and Analysis based on Mass-to-Charge Ratio | Limited to Specific Compound (Peptides) | Fast Detection Accurate Species Discrimination |
(Kuckova et al., 2019) | |
| Complex Sample Preparation | Fast Detection High Sensitivity (PDO Authentication) |
(Kandasamy et al., 2021) | |||
| Trained Personnel Complex Sample Preparation |
(Cozzolino et al., 2002) | ||||
| Complex Sample Preparation Complex Data Analysis |
Fast Detection Reproducibility High Throughput |
(Kritikou et al., 2022) | |||
| High Accuracy for Origin Discrimination | Sensitive to Processing Conditions | High Sensitivity (PDO Authentication) | (Kandasamy et al., 2021) | ||
| High Accuracy for Species Identification | Baseline Comparison Required | Fast Detection Good for Routine Analysis |
(Rau et al., 2020) | ||
| High Sensitivity for Marker Compounds | Complex Sample Preparation (Peptide Tryptic Digestion) |
Fast Detection High Sensitivity (PDO Authentication) |
(Russo et al., 2016) | ||
| MC-ICP-MS38 | Combining Multi-Collection and Plasma Ionization with Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge and Isotopic Ratio | High Accuracy | Specialized Equipment Complex Sample Preparation |
High Specificity (Geographic Verification) | (Fortunato et al., 2004) |
| MIR39 Spectroscopy | Molecular Vibrational Mode Measurement in the Mid-Infrared Region | Sensitive to Matrix Complexity Expensive Equipment |
High Sensitivity High Specificity |
(Cardin et al., 2022) | |
| High Accuracy for Feed Authentication | Limited to Specific Compounds (Indicators) Predictive Model Required |
Fast Detection High Sensitivity (PDO Authentication) |
(Coppa et al., 2021) | ||
| High Accuracy for Geographic Origin Indication | Sensitive to Moisture Interference Complex Sample Preparation |
Detailed Chemical Fingerprints Non-destructive Detection High Sensitivity (PDO Authentcation) |
(Karoui et al., 2007) | ||
| High Accuracy for Origin Discrimination | Limited to Functional Group Identification Sensitive to Moisture Interference |
Accurate Origin Discrimination | (Karoui et al., 2005) | ||
| Sensitive to Moisture Interference Calibartion Required |
Fast Detection Non-destructive Detection Effective in Distinguishing Prato from Mozzarella |
(Tolentino et al., 2023) | |||
| Very High Accuracy | Complex Sample Preparation | Clear Signals | (Yakubu et al., 2020) | ||
| Sensitive to Moisture Interference Dry Sample Required |
Detailed Chemical Fingerprints (Molecular Information) | (Silva et al., 2022) | |||
| MS40 | Compound Analysis based on Mass-to-Charge Ratio | Complex Data Interpretation | (Lee Rangel et al., 2022) | ||
| Multi-Elemental Analysis | Simultaneous Multiple Element Analysis based on Mass-to-Charge Ratio | High Accuracy | Calibration Required | High Sensitivity (Elemental Profiles) | (Nikezić et al., 2024) |
| NIR41 Spectroscopy | Molecular Vibrational Mode Measurement in the Near-Infrared Region | Limited Portability | Fast Detection | (Pu et al., 2021) | |
| Sensitive to Moisture Interference | Fast Detection Non-destructive Detection |
(Bergamaschi, Cipolat-Gotet, et al., 2020) | |||
| Chemometrics Equations Required | Fast Detection Non-destructive Detection Good for Routine Analysis |
(Visconti et al., 2020) | |||
| Limited to Specific Compounds | Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Atanassova, Yorgov, Veleva, Stoyanchev, & Zlatev, 2023) | |||
| Sensitive to Moisture Interference Chemomterics Equations Required |
Fast Detection Non-destructive Detection Suitable for On-Site Verification |
(da Medeiros et al., 2023) | |||
| High Accuracy (Dependent on Calibration) |
Complex Sample Preparation | Fast Detection Non-destructive Detection |
(Tyl et al., 2020) | ||
| Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Yakubu et al., 2020) | ||||
| High Accuracy (R2 ∼ 0.98 for Fats) |
Variable Sensitivity (Based on Concentration) |
Fast Detection Non-destructive Detection |
(Pu et al., 2021) | ||
| High Accuracy for Detailed Compound Analysis | Variable Sensitivity (Weak for Minor Constituents e.g. Salt) |
Fast Detection Minimal Sample Preparation |
(Woodcock et al., 2008) | ||
| High Accuracy for Diet Origin Discrimination | Sensitive to Moisture Interference | Fast Detection Non-destructive Detection |
(Andueza et al., 2013) | ||
| High Accuracy for Geographic Origin Indication | Sensitive to Spectral Noise Chemometrics Equations Required |
Fast Detection Non-destructive Detection High Sensitivity (PDO Authentication) |
(Ottavian et al., 2012) | ||
| High Accuracy for Metabolite Differentiation Composition | Complex Sample Preparation | Fast Detection Non-destructive Detection |
(Huck-Pezzei et al., 2014) | ||
| High Accuracy for Origin Discrimination | Variable Sensitivity (Based on Spectral Difference) |
Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Atanassova, Yorgov, & Veleva, 2023) | ||
| Sensitive to Moisture Interference | Fast Detection Non-destructive Detection Suitable for Solids |
(Karoui et al., 2005) | |||
| Moderate Accuracy | Calibration Required | Fast Detection Non-destructive Detection |
(Vlasiou, 2023) | ||
| Limited by Regional Origin | (Cardin et al., 2024) | ||||
| Moderate to High Accuracy | Calibration Required (Larger Samples) |
Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Cardin et al., 2022) | ||
| Moderate to High Accuracy for Metabolite Differentiation Composition | Limited by Spectral Overlap Variable Sensitivity (Based on Mineral Content) |
Fast Detection No Requirement for Pretreatment |
(Rodriguez-Otero et al., 1997) | ||
| NIR Spectroscopy (Portable) | Molecular Vibrational Mode Measurement in the Near-Infrared Region with on-site Analysis | Variable Accuracy | Limited by Spectral Overlap | Convenient Field Testing | (Pu et al., 2021) |
| NIR Spectroscopy (Reflectance) | Molecular Vibrational Mode Measurement of the Reflected Light in the Near-Infrared Region | Moderate to High Accuracy | Calibration Maybe Required (Specific Adulterants) | Fast Detection High Portability |
(Silva et al., 2022) |
| Complex Sample Preparation | Fast Detection Non-destructive Detection Suitable for Bulk Analysis |
(Hebling e Tavares et al., 2022) | |||
| NIR-HSI42 | Spatial and Spectral Information Capture in the Near-Infrared Region | High Accuracy | Expensive Equipment Specialized Equipment |
Effective Identification of Rind | |
| NMR43 Relaxometry | Relaxation Time Measurement in Proton environment for Compound Identification | Very High Accuracy | Expensive Equipment Trained Personnel |
Complex Matrix Analysis | (Alekseev & Khripov, 2014) |
| PTR-TOF-MS44 | Combining Proton Transfer Reaction and Time-of-Flight Mass Spectrometry for Compound Analysis based on Mass-to-Charge Ratio | High Accuracy for Diet Origin Discrimination | Cross-Validation Required | Fast Detection Non-invasive Detection High Sensitivity (Volatile Compounds) |
(Bergamaschi, Cecchinato, & Bittante, 2020) |
| High Accuracy for Volatile Compound Detection | Specialized Equipment Sensitive to Contamination |
Non-destructive Detection High Sensitivity (Flavor Profile) |
(Bergamaschi, Cipolat-Gotet, et al., 2020) | ||
| Very High Accuracy | Calibration Required | Detailed Chemical Fingerprints | (Galle et al., 2011) | ||
| Raman Spectroscopy | Molecular Vibrational Mode Measurement based on Monochromatic Light Scattering | High Accuracy | Complex Sample Preparation | Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Ozer Genis et al., 2020) |
| Moderate Accuracy | Limited to Surface Analysis | Non-invasive Detection | (Ostovar pour et al., 2021) | ||
| SERDS45 | Molecular Vibrational Mode Measurement based on Monochromatic Light Scattering with Fluorescence Interference Subtraction | High Accuracy | Sensitive to Fluorescence Background | Clear Seperation of Protein and Lipid Signals | (Sowoidnich & Kronfeldt, 2016) |
| SFS46 | Simultaneous Excitation and Emission Measurement of Light by a Compound after Electromagnetic Radiation | Sensitive to Spectral Noise | Fast Detection | (Dankowska et al., 2015) | |
| High Accuracy for Species Identification | Limited to Specific Compounds (Fluorescents) | Fast Detection Non-destructive Detection Minimal Sample Preparation High Sensitivity (PDO Authentication) |
(Ozer Genis et al., 2019) | ||
| SIRA47 | Stable Isotope Relative Abundance Measurement for Compound Analysis based on Mass-to-Charge Ratio | High Accuracy for Origin Discrimination | Complex Sample Preparation | Accurate Origin Discrimination | (O'sullivan et al., 2022) |
| SORS48 | Molecular Vibrational Mode Measurement based on Monochromatic Light Scattering at Spatial Offsets from Laser Excitation | High Accuracy | Limited to Surface Analysis | Non-destructive Detection Suitable for Sealed Packages |
(Ostovar pour et al., 2021) |
| SPME-GC-MS49 | Combining Solid-Phase Microextraction with Gas Chromatography-Mass Spectrometry for Volatile Compound Analysis | High Accuracy for Volatile Compound Detection | Limited to Volatile Compounds Complex Sample Preparation |
Non-destructive Detection High Sensitivity (PDO Authentication and traceability) High Sensitivity (Aroma Profile) |
(Favaro et al., 2005) |
| SPME-MS50 | Combining Solid-Phase Microextraction with Mass Spectrometry for Volatile Compound Analysis | High Accuracy | Limited by Minor Differentiation | Fast Detection High Sensitivity (PDO Authentication) |
(Majcher et al., 2015) |
| Sr (Strontium) Isotope Analysis | Strontium Relative Abundance Measurement for Compound Analysis based on Mass-to-Charge Ratio | Specialized Equipment Complex Sample Preparation |
High Specificity (Geographic Verification) | (Nikezić et al., 2024) | |
| TD-NMR51 Spectroscopy | Time-Domain to Frequency-Domain Conversion and Measurement for Compound Identification | Complex Sample Preparation | Fast Detection Non-destructive Detection Minimal Sample Preparation |
(Mengucci et al., 2021) | |
| High Accuracy for Moisture Determination | Limited to Specific Compounds | Monitoring Moisture Distribution | (Marcone et al., 2013) | ||
| TIMS52 | Combining Thermal Ionization with Mass Spectrometry for Compound Ionization and Analysis based on Mass-to-Charge Ratio | Very High Accuracy | Complex Sample Preparation | Accurate Isotope Analysis | (Fortunato et al., 2004) |
| UHPLC-Orbitrap-MS53 | Combining Ultra-High Pressure Liquid Chromatography with Orbitrap-Mass Spectrometry for Direct Compound Analysis based on Mass-to-Charge Ratio | High Accuracy for Origin Discrimination | Specialized Equipment Complex Data Interpretation |
Detailed Chemical Fingerprints | (Becchi et al., 2024) |
| UHPLC/Q-TOF-MS54 | Combining Ultra-High Pressure Liquid Chromatography for Sample Separation, and Quadrupole Filtering with Time-of-Flight Mass Spectrometry for Compound Analysis based on Mass-to-Charge Ratio | Expensive Equipment Complex Data Interpretation |
(Rocchetti et al., 2018) | ||
| Very High Accuracy | Calibration Required | Complex Mixture Analysis | (Maestrello et al., 2024) | ||
| UPLC-MS/MS55 | Combining Ultra-Pressure Liquid Chromatography with Mass Spectrometry for Compound Seperation and Analysis based on Mass-to-Charge Ratio | Complex Sample Preparation | High Sensitivity (Low-Concentration Compounds) | (Russo et al., 2012) | |
| Vis/NIR56 Spectroscopy | Molecular Vibrational Mode Measurement in the Visible and Near-Infrared Region | High Accuracy | Limited to Coagulation Properties | Non-invasive Detection Suitable for Online Use |
(Woodcock et al., 2008) |
| Visible Spectroscopy | Molecular Vibrational Mode Measurement in the Visible Region | Moderate Accuracy | Variable Sensitivity (Based on Range) |
Fast Detection Non-destructive Detection |
(Andueza et al., 2013) |
Proton Nuclear Magnetic Resonance Spectroscopy.
Protected Designation of Origin.
Atmospheric Pressure Ionization-Mass Spectrometry.
Attenuated Total Reflectance-Fourier Transform Mid-Infrared Spectroscopy.
Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy.
Attenuated Total Reflectance Mid-Infrared Spectroscopy.
Direct Analysis in Real Time-High Resolution Mass Spectrometry.
Direct Analysis in Real Time-Orbitrap Mass Spectrometry.
Dynamic Headspace-Gas Chromatography-Mass Spectrometry.
Direct Injection Gas Chromatography-Mass Spectrometry.
Diffuse Reflectance Near-Infrared Spectroscopy.
Fourier Transform Mid-Infrared Spectroscopy.
Fourier Transform Near-Infrared Spectroscopy.
Fourier Transform Infrared Spectroscopy.
Standard Error of Proportion.
Gas Chromatography-Mass Spectrometry.
High Performance Liquid Chromatography with Diode Array Detection.
High Performance Liquid Chromatography-Mass Spectrometry.
High Resolution Magic Angle Spinning Proton Nuclear Magnetic Resonance Spectroscopy.
High Resolution Gas Chromatography-Mass Spectrometry.
High Resolution Mass Spectrometry.
Headspace-Proton Transfer Reaction-Mass Spectrometry.
Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry.
Hyperspectral Imaging.
Inductively Coupled Plasma Mass Spectrometry.
Infrared Spectroscopy.
Isotope Ratio Mass Spectrometry.
Protected Geographical Indication.
Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry.
Liquid Chromatography-Electrospray Ionization-Quadrupole Time Of Flight Mass Spectrometry.
Liquid Chromatography-High resolution Mass Spectrometry.
Liquid Chromatography-Mass Spectrometry.
Liquid Chromatography-Tandem Mass Spectrometry.
Traditional Speciality Guaranteed.
Laser Induced Breakdown Spectroscopy.
Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry.
Matrix-Assisted Laser Desorption/Ionization Time Of Flight Mass Spectrometry.
Multicollector Inductively Coupled Plasma Mass Spectrometry.
Mid-Infrared Spectroscopy.
Mass Spectrometry.
Near-Infrared Spectroscopy.
Near-Infrared Hyperspectral Imaging.
Nuclear Magnetic Resonance.
Proton Transfer Reaction-Time of Flight Mass Spectrometry.
Second Order Differential Spectroscopy.
Synchronized Fluorescence Spectroscopy.
Stable Isotope Ratio Analysis.
Surface Enhanced Raman Spectroscopy.
Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry.
Solid Phase Microextraction-Mass Spectrometry.
Time Domain-Nuclear Magnetic Resonance.
Thermal Ionization Mass Spectrometry.
Ultra High-Performance Liquid Chromatography-Orbitrap Mass Spectrometry.
Ultra High-Performance Liquid Chromatography/Quadrupole Time Of Flight Mass Spectrometry.
Ultra Performance Liquid Chromatography-Tandem Mass Spectrometry.
Visible/Near-Infrared Spectroscopy.
4. Discussion
4.1. Classification of cheese adulteration types
Cheese adulteration encompasses a wide spectrum of fraudulent practices, each varying in complexity, health implications, and economic impact. These adulteration strategies are typically implemented to reduce production costs, increase product yield, or simulate the properties of high-quality or PDO-labeled cheeses. Quantitative analysis of the included studies revealed that species substitution was the most frequently investigated adulteration type (41.3 %), followed by fat substitution (21.2 %), protein adulteration (13.5 %), incorporation of non-dairy additives (11.5 %), geographical mislabeling (7.7 %), and antibiotic contamination (4.8 %). These figures underscore the industry's emphasis on detecting animal-species fraud and lipid profile manipulation, especially in high-value cheeses with PDO designation.
4.1.1. Species substitution
One of the most pervasive adulteration methods involves the substitution or addition of milk from non-declared animal species. This practice is of particular concern for consumers with dietary restrictions or allergies, as well as for products marketed under PDO status, which often stipulate specific species. For example, several studies have identified the fraudulent addition of bovine milk to cheese products marketed as being derived from buffalo, goat, or ewe milk. Bovine milk was detected at concentrations as low as 0.001 % (v/v) in buffalo mozzarella using LC-MS/MS (Cajka et al., 2016), and as low as 1 % using MALDI-TOF-MS in PDO Feta cheese (Kritikou et al., 2022). Techniques such as MALDI-TOF-MS, LC-MS/MS, and NMR have demonstrated high sensitivity in detecting species-specific peptides and protein markers (Czerwenka et al., 2010; Guarino et al., 2010; Kuckova et al., 2019; Russo et al., 2016).
As shown in Fig. 3a, Mozzarella cheese, due to its stretching process, develops a unique structure featuring serum-filled channels and fat globules dispersed within a protein gel network, which results in a multi-exponential decay in T₂ relaxation behavior as observed through 1H NMR. In “Mozzarella di Bufala Campana” PDO, four distinct relaxation components were identified: T₂₁ from water protons exchanging at the gel surface, T₂₂ from slow-diffusing water in small channels, T₂₃ from water in medium channels and liquid fat, and T₂₄ from water in larger compartments influenced by whey proteins and saccharides. While frozen curd (FC) addition did not significantly alter relaxation coefficients, some variations in signal intensity and T₂ distribution were noted—particularly a shift toward shorter T₂ times and increased dispersion around 30 ms, indicating changes in water mobility and compartment structure. These changes suggest that FC content affects the internal gel network, redistributing water protons across different environments. Continuous T₂ relaxation distributions provided more detailed insight than discrete coefficients, though they require careful parameterization. To avoid potential fitting errors and maintain data integrity, raw CPMG decays were ultimately used to construct classification models for detecting adulteration based on FC content in mozzarella cheese (Mengucci et al., 2021). Fig. 3 b and c presents the total ion chromatogram of volatile organic compounds (VOCs) and the chemical composition of fermented milk cheese (F-MC) made from Holstein cow milk, analyzed using HS-SPME/GC–MS. A total of 18 VOCs were identified, including various siloxanes, esters, acids, and aromatic compounds, each listed with their respective retention times. These compounds contribute differently to the overall flavor profile, though only a few significantly impact flavor development. In comparison, F-MC made with Jersey cow milk (Fig. 3c) exhibited a simpler VOC profile, with only 11 compounds detected. This highlights the greater chemical complexity in cheeses derived from Holstein milk, potentially useful in discriminating cheese authenticity or origin through spectroscopic techniques (Lee-Rangel et al., 2022). These methods allow for both qualitative and quantitative evaluation of milk origin, thereby enabling enforcement of labeling standards in products like Mozzarella di Bufala Campana or Feta cheese (Bontempo et al., 2019; Kritikou et al., 2022; Rau et al., 2020).
Fig. 3.
(a) Mean T₂ relaxation time distribution of day 2 samples as a function of frozen curd (FC) content, with varying FC concentrations shown as solid-colored lines (adapted from Mengucci et al., 2021). (b) (b) Total ion chromatogram (TIC) of VOCs in F-MC made from Jersey milk, with compound identification by HS-SPME/GC–MS (retention time (rt): 1. Cyclotrisiloxane, hexamethyl-; 2. Cyclotetrasiloxane, octamethyl-; 3. 2-Methyl-7-phenylindole; 4. Cyclopentasiloxane, decamethyl-; 5. Cyclohexasiloxane, dodecamethyl-; 6. 2-Hexen-4-ol, 5-methyl-; 7. Cycloheptasiloxane, tetradecamethyl-; 8. Propanoic acid, 2-methyl-, 3-hydroxy-2,4,4-trimethylpentyl ester; 9. Nonahexacontanoic acid; 10. 1,4-Dioxaspiro[4,5]decane-7-butanoic acid, 6-methyl-, 2-(methylsulfonyloxy)ethyl ester; 11. 1-Monolinoleoylglycerol trimethylsilyl ether); (c) TIC of VOCs in F-MC made from Holstein milk, with corresponding compound identification (retention time (rt): 1. 1-Benzazirene-1-carboxylic acid, 2,2,5a-trimethyl-1a-[3-oxo-1-butenyl] perhydro-, methyl ester; 2. Silicic acid, diethyl bis(trimethylsilyl) ester; 3. 1H-Trindene, 2,3,4,5,6,7,8,9-octahydro-1,1,4,4,9,9-hexamethyl-; 4. 4-Trimethylsilyl-9,9-dimethyl-9-silafluorene; 5. Cyclotrisiloxane, hexamethyl-; 6. Cyclopentasiloxane, decamethyl-; 7. Methyl-[4-[2,6-dimethyl-3-[methylthio]-1,2,4-triazin-5(2H)-ylidene]-2-butenylidene]methylhydrazinecarbodithioate; 8. Cyclohexasiloxane, dodecamethyl-; 9. Propanoic acid, 2-methyl-, 2,2-dimethyl-1-(2-hydroxy-1-methylethyl)propyl ester; 10. Hexane, 3-methyl-; 11. Silicic acid, diethyl bis(trimethylsilyl) ester; 12. Heptasiloxane, 1,1,3,3,5,5,7,7,9,9,11,11,13,13-tetradecamethyl-; 13. Cycloheptasiloxane, tetradecamethyl-; 14. Cyclodecasiloxane, eicosamethyl-; 15. Propanoic acid, 2-methyl-, 1-(1,1-dimethylethyl)-2-methyl-1,3-propanediyl ester; 16. Benzoic acid, 3,4-dichloro-, methyl ester; 17. 1H-Indole, 2-methyl-3-phenyl-; 18. 2,5-Cyclohexadien-1-one, 2,5-dimethyl-4-[(2,4,5-trimethylphenyl)imino]-) (adapted from Lee-Rangel et al., 2022).
4.1.2. Fat substitution and foreign lipid addition
Another major adulteration strategy is the replacement of milk fat with cheaper plant-based oils, including soybean, palm, or corn oil. This practice alters the lipid profile and nutritional value of the cheese while misleading consumers about product quality. Several studies have confirmed the detection of foreign fats using spectroscopic techniques such as 1H NMR, FTIR, and DART-HRMS, which can reveal subtle variations in fatty acid composition and triacylglycerol structure (Dankowska et al., 2015; Ray et al., 2023; Tociu et al., 2017). These methods are particularly effective due to their ability to profile lipid fingerprints and detect adulteration even at low substitution levels (Cardin et al., 2024; Tociu et al., 2018). 1H NMR has been used to detect palm oil in Dalia cheese down to 2.5 % (w/w) (Tociu et al., 2018), and NIR spectroscopy identified soybean oil adulteration in artisanal butter cheese from 5 % up to 100 % (w/w) (da Medeiros et al., 2023).
4.1.3. Protein adulteration
Adulteration via non-milk proteins such as whey protein isolates or plant proteins is another frequent occurrence, particularly in fresh and processed cheeses. This is often done to simulate desired textural or compositional features. Advanced mass spectrometry-based methods such as LC-MS/MS have enabled the detection of low-level protein adulterants, even in complex matrices, by targeting specific peptide biomarkers (Barbu et al., 2021). NIR and FTIR have also been successfully employed for non-destructive identification of abnormal protein levels (Pu et al., 2021; Woodcock et al., 2008). For example, whey protein was detected in Swiss cheese at 3–50 % (w/w) using handheld NIR, while LC-MS/MS successfully quantified as low as 0.5 % (w/w) protein adulteration in cottage cheese (Pu et al., 2021; Yakubu et al., 2020).
4.1.4. Incorporation of non-dairy additives
Economic adulteration may also involve the addition of low-cost, non-dairy ingredients such as starch, water, or in some cases, hazardous compounds like melamine. HSI and FTIR have proven effective in detecting corn starch in cheese, which is often added to increase volume or mimic curd density (Barreto et al., 2018; Vinciguerra et al., 2019). HSI detected starch adulteration ranging from as low as 0.0055 % to 1.27 % (w/w) in fresh cheese (Barreto et al., 2018). Similarly, NIR spectroscopy has been extensively used to detect water addition, which compromises texture and weight accuracy (Atanassova, Yorgov, Veleva, Stoyanchev, & Zlatev, 2023). Cheese samples adulterated with up to 10 % (w/w) added water were reliably identified using NIR (Cardin et al., 2022). Alarmingly, cases involving melamine, a toxic nitrogen-rich compound, have been detected using both NIR and mass spectrometry techniques (Pu et al., 2021), underscoring the potential public health implications of such adulteration practices.
4.1.5. Geographical and production origin fraud
Geographical origin misrepresentation, particularly in cheeses with PDO status, represents a significant form of food fraud. In Fig. 4, a FTIR Spectrometer equipped with a KBr beam splitter was used to analyze cheese samples. Distinct spectral features enabled differentiation of cheese and milk samples based on species origin, as demonstrated in Fig. 4a. Key absorption bands associated with various functional groups—such as –OH and –NH stretching, C—H stretching in fatty acids, carbonyl (C=O) and amide vibrations, as well as phosphate and polysaccharide stretches—allowed the identification of compositional differences. These variations formed the basis for constructing chemometric models to support cheese adulteration detection through species-specific spectral markers (Tarapoulouzi et al., 2020). Fig. 4b marks the first application of FTIR spectroscopy for the characterization of Anari cheese, using a methodology previously applied to Halloumi cheese. The supervised chemometric technique OPLS-DA was employed, revealing that only the FTIR spectral subregions between 3000 and 2800 cm−1 and 1700–1090 cm−1 were significant for distinguishing samples based on milk species origin. This highlights key differences, particularly in the 1400–1300 cm−1 region, which corresponds to amide III protein bands. Other important spectral features include peaks at 1150–1200 cm−1 (associated with –NH₂, –COH, and C—C), 1650–1550 cm−1 (related to amides I and II), and 2930–1745 cm−1 (indicative of fat content) (Tarapoulouzi & Theocharis, 2023). Furthermore, Isotopic ratio analysis (e.g., δ13C, δ15N, δ34S) via IRMS and elemental fingerprinting using ICP-MS or MC-ICP-MS have been effectively used to determine the geographical authenticity of cheeses like Grana Padano, Parmigiano Reggiano, and Mozzarella di Bufala Campana (Bontempo et al., 2019; Camin et al., 2012; Maestrello et al., 2024; Pianezze et al., 2020). These methods are capable of distinguishing regional production characteristics based on soil and feed composition, thus offering reliable forensic tools for geographic authentication.
Fig. 4.
FTIR spectra of (a) cheese and milk samples, demonstrating species-specific spectral differences used for sample differentiation (adapted from Tarapoulouzi, Kokkinofta and Theocharis, 2020, and (b) Anari cheese, representing the first application of FTIR spectroscopy for its compositional characterization and discrimination based on milk species origin (adapted from Tarapoulouzi and Theocharis, 2023).
4.1.6. Antibiotic residue contamination
Although not a deliberate form of adulteration, the presence of antibiotic residues in cheese can indicate improper veterinary practices and pose regulatory and safety concerns. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has been widely employed to detect a range of antibiotic residues—including amoxicillin, enrofloxacin, and oxytetracycline—in cheese samples (Quintanilla et al., 2019). Quantified residue levels include 10.5 μg/kg of amoxicillin, 250.9 μg/kg of enrofloxacin, and 3916.7 μg/kg of neomycin, demonstrating the high sensitivity of LC-MS/MS for regulatory compliance monitoring (Quintanilla et al., 2019). The high sensitivity and specificity of these techniques make them indispensable for regulatory monitoring and food safety assurance.
4.2. Comparison of spectroscopic techniques for cheese adulteration detection
Spectroscopic techniques have become indispensable tools for identifying adulteration in cheese products due to their high sensitivity, specificity, rapidity, and non-destructive nature. Fig. 5 illustrates a schematic overview of the spectroscopic principles used for detecting adulteration. Across the dataset, NIR and FTIR were the most commonly applied spectroscopic tools, appearing in over 50 % of studies, while high-specificity platforms like LC-MS/MS and MALDI-TOF-MS were reported in approximately 35 % and 28 % of cases, respectively. This distribution reflects the trade-off between throughput, resolution, and cost in selecting appropriate authentication strategies. Each method brings unique analytical strengths and limitations depending on the target adulterant, matrix complexity, and required detection threshold. Below is a comparative discussion of the primary spectroscopic modalities currently utilized for cheese authentication.
Fig. 5.
Schematic representation of spectroscopic techniques employed for the detection of adulteration in cheese.
4.2.1. NMR spectroscopy
NMR spectroscopy, particularly high-resolution 1H NMR and HR-MAS-NMR, has proven effective for the authentication of PDO cheeses and detection of foreign lipids and metabolites. It provides comprehensive molecular fingerprints by analyzing hydrogen environments in cheese lipids, proteins, and aqueous metabolites. Studies have successfully used 1H NMR to differentiate Grana Padano PDO from non-PDO cheeses and to detect adulteration with vegetable oils or non-native amino acids, with quantitative experiments showing that the technique flags vegetable-oil substitution at 30–70 % (w/w) in grated Parmesan (Ray et al., 2023) and ≤ 15 % (w/w) in Brie cheese (Cardin et al., 2022).
Fig. 6 presents the 1H NMR spectra of both the aqueous and lipid extracts of PDO Grana Padano cheese, highlighting key signals relevant for adulteration detection. In the aqueous extract (Fig. 6a), prominent signals correspond to organic acids like acetate and lactate, along with amino acids such as alanine and valine. The lipid extract spectrum (Fig. 6b), obtained using a zg30 pulse program, primarily features methylenic and methyl protons of fatty acids. To enhance detection of minor compounds, an additional acquisition using the noesygppr1d.wvm pulse program with multi-suppression of dominant signals was performed, revealing increased visibility of bis-allylic protons and specific fatty acids like caproleic and rumenic acid in the zoomed inset (Maestrello et al., 2024). Fig. 6c shows a set of ungraded bovine hard cheeses, considered genuine and referred to as “baseline” samples, which were analyzed using 1H NMR spectroscopy to establish reference profiles for adulteration detection. The spectra revealed characteristic signals of lipids, specifically triacylglycerol. It displays the 1H NMR spectrum of a representative parmesan baseline sample, with regions of interest labeled A through E, corresponding to specific proton environments in an idealized triacylglycerol structure. These labeled regions were further analyzed using raw integral values from the associated spectral peaks (Ray et al., 2023). Fig. 6d illustrates a representative 1H NMR spectrum of the aqueous extract of Greek Groviera cheese, in which 32 compounds were identified and quantified using an internal standard by integrating their spectral signals. The spectrum, acquired in D₂O-TSP at 500 MHz, highlights several key metabolites relevant for authentication and adulteration detection (Ralli & Spyros, 2023). Although its resolution and specificity are unmatched, NMR requires sophisticated instrumentation and complex sample preparation, making it less accessible for routine testing (Balthazar et al., 2021).
Fig. 6.
1H NMR spectra of cheese extracts: (a) aqueous extract of PDO Grana Padano with annotated metabolite signals; (b) lipid extract of PDO Grana Padano acquired with zg30 pulse program, with a zoomed region highlighting low-intensity signals enhanced using noesygppr1d.wvm (adapted from Maestrello et al., 2024); (c) 500 MHz spectrum with a model triacylglycerol structure, showing resonance assignments (A–E) corresponding to protons in the NMR spectrum (adapted from Ray et al., 2023); (d) typical 1H NMR spectrum of graviera cheese water extract in D₂O-TSP at 500 MHz, with selected metabolites highlighted (adapted from Ralli & Spyros, 2023).
4.2.2. Infrared and near-infrared spectroscopy
IR and NIR techniques are among the most widely used methods for rapid screening of cheese adulteration. NIR spectroscopy has been effectively applied for detecting foreign fats, added water, starches, and non-milk proteins in various cheese types —for example, NIR detects soybean-oil replacement from 5 % to 100 % (w/w) (da Medeiros et al., 2023), bovine-milk addition down to 0.5 % (w/w) in cottage cheese (Yakubu et al., 2020), and water addition as low as 10 % (w/w) in Mozzarella (Cardin et al., 2022) or 0–25 % (w/w) in grated Grana Padano (Pu et al., 2021). As shown in Fig. 7a-c, with previous findings, the spectral profiles of cheese samples showed similarities to authentic cheeses but differed from adulterated ones primarily in the intensity of absorbance at specific wavelengths—particularly around 970, 1130–1240, 1400, 1450, 1510, and 1660 nm. The peaks at 970 and 1450 nm correspond to O—H stretching overtones associated with water, while the 1130–1240 nm region and variations at 1400 and 1660 nm are linked to C—H stretching overtones from aliphatic chains in fats and unsaturated fatty acids. Additionally, the absorption valley at 1510 nm reflects N—H stretching in proteins. These spectral differences were observed across mean spectra of various cheese types—including pure and adulterated laboratory and commercial butter cheeses—analyzed in raw form and after pre-processing with Savitzky-Golay smoothing combined with standard normal variate (SNV) correction, as well as using the first derivative of Savitzky-Golay with SNV for enhanced signal clarity (da Medeiros et al., 2023). FT-NIR has likewise discriminated grated hard cheeses containing 3–15 % (w/w) micro-cellulose bulking agent (Visconti et al., 2024) and HSI pinpoints corn-starch fraud from 0.006 % up to 1.27 % (w/w) in fresh cheese (Barreto et al., 2018).
Fig. 7.
Mean FTIR spectra of cheeses: (a) raw spectra of PBC (pure laboratory butter cheese), ABC (adulterated laboratory butter cheese), PCC (pure commercial butter cheese), and ACC (adulterated commercial butter cheese); (b) spectra pre-processed using Savitzky–Golay smoothing combined with SNV; (c) first derivative of Savitzky–Golay smoothing with SNV (adapted from Medeiros et al., 2023); and (d) average absorbance spectra of Caciotta cheese by origin, with predictive importance regions (I–III) highlighted in green (adapted from Cardin et al., 2024). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 7d indicates the hold-out validated models showed a notable drop in performance compared to the training models, with Producer 3 displaying the lowest recall, precision, and specificity. This resulted in a classification accuracy of 76 % ± 31.57 %, with the high error likely due to low precision from producers 1, 3, and 4. Recognizing that individual wavelengths offer limited information compared to broader NIR regions, the analysis focused on the 100 most significant wavelengths for predicting the geographic origin of Caciotta cheese. These key wavelengths were grouped into three regions: Region I (around 1090, 1145, and 1204 nm), Region III (around 2230, 2310, and 2360 nm), and Region II, which featured more scattered important wavelengths near 1646, 1735, 1780, and 1870 nm. This figure illustrates the average absorbance spectra of typical Caciotta samples, with green bars denoting the predictive importance of each wavelength (Cardin et al., 2024). FTIR and ATR-FTIR, which operate in the mid-infrared region, are powerful for identifying functional groups associated with chemical adulterants and can distinguish processing methods or the use of non-native ingredients (da Medeiros et al., 2023; Leite et al., 2019; Silva et al., 2022).
Fig. 8a,b compares the FT-NIR spectra of authentic and adulterated goat yogurt and cheese samples, revealing that while both product types exhibit similar spectral profiles, visual distinctions between authentic and adulterated samples are not immediately apparent. In yogurt, strong water absorption bands at 6881 and 5171 cm−1 dominate the spectra, masking potential differences due to other components—an issue also reported in prior studies involving yogurt, milk, and various high-moisture food matrices. This makes FT-NIR analysis of such samples challenging and highlights the importance of careful spectral pre-processing and variable selection when using chemometric models. In contrast, the FT-NIR spectra of goat cheese show less intense water bands at 6860 and 5170 cm−1, allowing clearer detection of overtone and combination bands linked to C—H and N—H bonds from lipids and proteins. Additional bands at 8264, 5780, and 5667 cm−1 suggest the presence of terminal methyl groups, while features at 4330 and 4257 cm−1 are associated with lipid- and protein-related C–H₂ groups. These differences are likely due to the cheese-making process, which concentrates fat and protein in the curd, unlike yogurt, which retains the original milk composition. Previous studies, including those on Emmental, Cheddar, and Parmigiano Reggiano cheeses, have similarly identified key water and fat absorption regions, confirming the potential of FT-NIR and FT-MIR techniques in cheese authentication when supported by robust chemometric approaches (da Teixeira et al., 2021). Fig. 8c-e illustrates the differences in average NIR spectra between cheese groups classified according to cow feeding practices defined in the specifications for Cantal and Laguiole PDO cheeses. For Cantal, comparisons were made between samples based on the presence of pasture (PA vs. No-PA) and whether pasture comprised at least 50 % of the daily dry matter intake (PA < 50 % vs. PA ≥ 50 %). For Laguiole, groups were differentiated by whether pasture made up at least 57 % of the diet (PA < 57 % vs. PA ≥ 57 %), whether concentrate feed was under 28 % (CON <28 % vs. CON ≥28 %), and by the absence or presence of corn silage (No-M vs. M) and fermented herbage (No-FH vs. FH). Additionally, comparisons were made between cheeses that fully met all feeding criteria for each PDO (All-Cantal vs. No-Cantal and All-Laguiole vs. No-Laguiole), with “No-” indicating the absence of the specified feed component (Coppa et al., 2021).
Fig. 8.
Medium-range FT-NIR spectra of (a) authentic and adulterated yogurt and (b) cheese (adapted from Teixeira et al., 2021); differences in average spectra used to authenticate cow feeding regimes for PDO cheeses, including: (c) presence of pasture (PA vs. No-PA) and PA ≥50 % of daily dry matter (DM) intake for Cantal cheese; (d) PA ≥57 %, concentrates (CON) <28 %, absence of corn silage (M), and absence of fermented herbage (FH) for Laguiole cheese; and (e) compliance with all Cantal and Laguiole PDO feeding criteria (All-Cantal vs. No-Cantal; All-Laguiole vs. No-Laguiole) (adapted from Coppa et al., 2021). “No-” indicates the absence of the corresponding feedstuff.
These methods are non-destructive, require minimal sample preparation, and are suitable for inline and portable applications. However, their performance may be influenced by moisture content and matrix complexity.
4.2.3. Ultraviolet-visible spectroscopy
UV–Vis spectroscopy is less frequently used alone but can provide valuable information on specific chromophores or dyes used in cheese fraud. When coupled with chemometric analysis, it can support detection of certain protein- or pigment-based adulterants. However, compared to other techniques, it offers lower molecular specificity and limited applicability to complex cheese matrices, which has led to its decline in stand-alone use for authentication purposes (Andueza et al., 2013; Coppa, Martin, Hulin, Gerber, et al., 2021; Vlasiou, 2023). Published UV–Vis work generally reports detection thresholds above 1 % (w/w), underscoring its lower sensitivity relative to IR-based methods (Andueza et al., 2013; Coppa et al., 2021).
4.2.4. Raman spectroscopy
Raman spectroscopy and its enhanced variants such as Surface-Enhanced Raman Scattering (SERS) and Spatially Offset Raman Spectroscopy (SORS) offer high specificity in molecular structure analysis through vibrational mode measurements. For ultra-filtered white cheeses, Raman profiling quantifies margarine (or palm/corn oil) substitution across the 2.5–25 % (w/w) range with a single Folch-extracted lipid scan (Ozer Genis et al., 2020). These methods are especially advantageous for detecting fat adulteration and protein alterations in situ or through sealed packaging (Genis et al., 2021). SORS has been employed for non-invasive detection of species fraud in cheeses like Mozzarella and Pecorino Romano. Although sensitive and rapid, Raman techniques can be hindered by fluorescence interference and require calibration for accurate quantification (Arroyo-Cerezo et al., 2023; Sowoidnich & Kronfeldt, 2016).
4.2.5. Spectroscopic imaging
HSI integrates spatial and spectral information, making it highly effective in mapping adulterant distribution across cheese surfaces. It has been used successfully for detecting starch, foreign fats and moisture; for instance, HSI detects corn-starch adulteration at 0.0055 % (w/w) and remains linear up to ≈ 1.3 % (w/w) in fresh cheese blocks (Barreto et al., 2018). HSI is non-destructive and particularly suitable for online inspection systems. However, its practical application may be constrained by high instrument cost, large data volume, and the need for advanced chemometric modeling (Hebling e Tavares et al., 2022).
4.2.6. Atomic and elemental spectroscopy
Techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Laser-Induced Breakdown Spectroscopy (LIBS) enable multi-element profiling and have been widely applied in traceability and fraud detection based on elemental signatures. Elemental fingerprints down to the low μg/kg range (e.g., Sr, Cu, Mo in Grana Padano; Camin et al., 2012) are routinely resolved by ICP-MS, while rennet adulteration at 11.7 % (w/w) and sea-salt uptake at 73 % (w/w) have been quantified in PDO Graviera Naxos using the same platform (Nikezić et al., 2024). ICP-MS has been instrumental in differentiating PDO cheeses based on their mineral content and environmental exposure (Cardin et al., 2024; Danezis et al., 2020). LIBS offers rapid, minimal-preparation analysis suitable for on-site verification, although its performance is often limited to inorganic constituents and requires standardization (Wu et al., 2022).
4.2.7. Isotopic spectroscopy and stable isotope ratio analysis (IRMS)
Isotopic methods, particularly IRMS, are powerful for determining the geographic and botanical origin of cheese. Measurements of δ13C, δ15N, δ34S, and δ18O have been used to authenticate PDO cheeses like Grana Padano, Mozzarella di Bufala Campana, and Pecorino Siciliano by linking isotopic profiles to regional feeding systems and soil composition (Bontempo et al., 2019; Camin et al., 2012; Faberi et al., 2018). Typical δ13C separations of ≥1 ‰ and δ34S shifts of 2–4 ‰ are enough to discriminate protected cheeses from neighboring non-PDO productions (Bontempo et al., 2019; Faberi et al., 2018). While these methods offer high precision, their reliance on extensive reference databases and expensive instrumentation can limit scalability.
4.2.8. Mass spectrometry (MS-based techniques)
Mass spectrometry, including MALDI-TOF-MS, LC-MS/MS, and DART-Orbitrap-MS, provides unparalleled sensitivity and specificity in detecting trace-level adulterants. Detection limits now reach the sub-percent level: MALDI-TOF identifies bovine-milk adulteration down to 1 % (w/w) in Feta (Kritikou et al., 2022) and 5 % (w/w) in Ricotta (Russo et al., 2016), while peptide-targeted LC-MS/MS quantifies cow-milk traces at just 0.001 % (v/v) in Mozzarella di Bufala (Cajka et al., 2016). These techniques are capable of identifying non-native proteins, specific peptides, lipid biomarkers, and antibiotic residues in complex cheese matrices (Cajka et al., 2016; Kritikou et al., 2022; Russo et al., 2016).
As illustrated in Fig. 9a, to identify two key biomarkers for cheese adulteration detection, a top-down LC-MS/MS analysis was conducted, targeting the fragmentation patterns of m/z 724.5741 and 1551.9423, which revealed C-terminal truncated forms of κ-casein and αS2-casein. Protein identification was based on bovine milk protein sequences from Uniprot FASTA files. The MS/MS spectra showed three matched fragments for m/z 724.5741 and six for m/z 1551.9423, although full characterization of these truncated forms was limited due to the presence of multiple proteoforms and potential post-translational modifications (PTMs), such as phosphorylation and glycosylation. Incomplete matches between experimental and theoretical masses suggest additional unknown modifications or cleavage patterns. Since whey proteins are largely lost during curd draining, the identified truncated caseins—being retained in the curd—serve as effective biomarkers. These truncated forms were > 10-fold more abundant in raw-milk cheese but largely absent or present at low levels in heat-treated samples, likely due to further enzymatic hydrolysis or PTMs. Additionally, heat-induced interactions between β-lactoglobulin and κ-casein may inhibit casein hydrolysis, explaining the absence of truncated κ-casein in heat-treated cheeses (Barbu et al., 2021). Fig. 9b shows cheese consumption is steadily increasing in South Korea, with mozzarella gaining popularity due to its versatility in foodservice and processing. In this context, MALDI-TOF mass spectrometry has emerged as a promising tool for analyzing milk and cheese protein profiles, including the detection of adulteration in products like buffalo mozzarella and Pecorino. However, no prior studies have examined the protein profiles of domestically produced cheeses in South Korea. This study aimed to develop a direct MALDI-TOF MS method to characterize and differentiate cow milk-based mozzarella cheeses based on geographical origin. Samples from 24 local farms, 9 domestic companies, and 10 imports were analyzed, with representative spectra showing ion intensities across the m/z range of 1000–5000. Distinct peaks—such as m/z 1869.6, 2754.8, 3470.6, 4478.6, and up to 12,254.5—were consistently found across all mozzarella samples, aligning with previous studies and confirming their association with cow's milk. These protein profiles reflect the complex proteolysis driven by endogenous enzymes and processing conditions, including pH shifts during ripening. Peaks at m/z 1869.6 and 2754.8 correspond to αs1-casein fragments (as1-CNf(1–16) and as1-CNf(1−23)), the latter known for immunomodulatory and antimicrobial properties, further validating the role of MALDI-TOF MS in cheese authentication and adulteration detection (Kandasamy et al., 2021). Fig. 9c and d present the sensor responses to volatile organic compounds (VOCs) in Fresh Mexican Cheese samples produced using milk from Holstein (CTF) and Jersey (STF) cows. Sensors S5, S7, S8, S19, and S31 detected VOCs in both sample types, with sensor S31 demonstrating notably higher sensitivity compared to the others. These sensorgrams highlight differences in aroma profiles based on milk origin, which can aid in identifying potential cheese adulteration (Lee-Rangel et al., 2022). MS-based methods are particularly valuable for detecting species substitution and the addition of whey proteins or synthetic compounds. Despite their superior analytical capabilities, their application is generally confined to specialized laboratories due to high cost, time-consuming sample preparation, and the need for expert interpretation (Barbu et al., 2021).
Fig. 9.
(a) Top-down MS spectrum of m/z 1551.9423 (z = 7) with annotated fragment ions; inset shows the parent ion spectrum (adapted from Barbu et al., 2021). (b) Representative MALDI-TOF mass spectra (1100–18,000 m/z) of mozzarella from Korean farmsteads (adapted from Kandasamy et al., 2021). Sensorgram of sensor responses to F-MC produced with milk from (c) Holstein (CTF) and (d) Jersey cows (SMF) (adapted from Lee-Rangel et al., 2022).
4.2.9. Electronic nose as a complementary technique
Electronic nose (E-nose) systems—comprising sensor arrays that respond to volatile organic compounds (VOCs)—have emerged as effective tools for monitoring the flavor, freshness, and adulteration of dairy products (Yakubu et al., 2022). Their advantages include rapid, non-destructive detection, low-cost operation, and compatibility with real-time or in-line quality assessment (Shi et al., 2018). In dairy authentication, E-noses have been used to detect spoilage, oxidation, and species-specific aroma profiles, complementing chemical markers obtained from spectroscopic platforms. Studies have demonstrated that sensor-based odor fingerprints can discriminate between milk sources, detect adulteration with non-native fats or proteins, and evaluate shelf-life changes (Yakubu et al., 2022).
Compared to molecular-resolution methods like LC-MS/MS or NMR, the E-nose lacks specificity for identifying individual compounds, but it excels in overall aroma profiling. Unlike FTIR or NIR, which analyze chemical bonds in bulk matrices, E-noses focus exclusively on VOC patterns and thus provide orthogonal information. Integration of E-nose data with spectroscopic or chromatographic techniques, especially via multivariate analysis, has shown synergistic potential in quality monitoring frameworks. However, sensor drift, sensitivity to humidity, and calibration variability remain technical challenges that limit their standalone use in regulatory applications.
Despite spectroscopic techniques analytical power, interpreting spectral data from cheese remains inherently challenging due to the complexity of the matrix. These products contain overlapping signals from fats, proteins, carbohydrates, and water, which often complicate spectral interpretation. For example, in IR spectroscopy, the strong absorbance of water can obscure key vibrational bands of minor adulterants (Vlasiou, 2023), while in NMR spectroscopy, the presence of dense lipid and aqueous phase components may result in overlapping chemical shifts (Ray et al., 2023). Similarly, Raman spectroscopy can suffer from fluorescence interference or low signal-to-noise ratios when targeting minor constituents in aged or high-fat cheeses (Ostovar pour et al., 2021). These limitations necessitate rigorous sample preprocessing and the application of advanced chemometric models to deconvolute overlapping signals and extract meaningful analytical information. Addressing matrix effects remains crucial to improving the robustness and reliability of spectral analysis in real-world cheese authentication scenarios.
5. Conclusion
This systematic review highlights the pivotal role of spectroscopic techniques in detecting and authenticating cheese products. Various forms of adulteration—including species substitution, fat and protein replacement, addition of non-dairy fillers, and misrepresentation of geographical origin—pose significant challenges to food authenticity, public health, and the protection of PDO-labeled products. Among the evaluated methods, techniques like 1H NMR, LC-MS/MS, FTIR, NIR, and HSI have demonstrated strong performance in identifying a wide range of adulterants with high sensitivity and minimal sample preparation. Methods such as IRMS, ICP-MS, and LIBS further support the authentication of geographical origin and elemental composition. Despite individual limitations, the combined use of multiple spectroscopic modalities, often enhanced by chemometric analysis, provides a powerful approach for comprehensive cheese authentication. As the dairy industry advances toward greater transparency and traceability, the integration of these techniques into routine quality control and regulatory frameworks will be essential. Continued development of rapid, non-invasive, and high-throughput spectroscopic platforms will further strengthen efforts to safeguard cheese quality and consumer trust on a global scale.
CRediT authorship contribution statement
Parham Joolaei Ahranjani: Writing – original draft, Formal analysis, Data curation, Conceptualization. Parsa Joolaei Ahranjani: Writing – original draft, Data curation, Conceptualization. Kamine Dehghan: Writing – original draft, Formal analysis. Zahra Esfandiari: Writing – review & editing, Conceptualization. Giovanna Ferrentino: Writing – review & editing, Conceptualization.
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.
Acknowledgement
This study was supported by the Nutrition and Food Security Research Center of Vice Chancellery for Research and Technology in Isfahan University of Medical Sciences in Iran with tracing No. 64685.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2025.102685.
Contributor Information
Zahra Esfandiari, Email: zesfandiary24@yahoo.com.
Giovanna Ferrentino, Email: giovanna.ferrentino@unibz.it.
Appendix A. Supplementary data
Supplementary material
Data availability
Data will be made available on request.
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