Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm−1 (C–H stretching in lipids), ~1650 cm−1 (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm−1 range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products.

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Publication Details

Journal
Applied Sciences
Published
2025-07-25
DOI
https://doi.org/10.3390/app15158272
Citations
3
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
2.42

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article

Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy

Michalis Koureas, A. Manouras, Dimitrios Kantas, Eleni Malissiova et al.
3 citations
Applied Sciences
Identification and Quantification in Food
2.42
article

Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy

Michalis Koureas, A. Manouras, Dimitrios Kantas, Eleni Malissiova, Christos J. Pappas, Lamprini Dimitriou
article en
3 citations

Abstract

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm−1 (C–H stretching in lipids), ~1650 cm−1 (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm−1 range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products.

Applied SciencesVol. 15(15)
Agricultural University of Athens (GR), University of Thessaly (GR)
University of Thessaly
Openalex Percentile: Top 11%
Identification and Quantification in Food
2.42
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