# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 4 Discussion  
This study confirms the effectiveness of ATR-FTIR spectroscopy combined with chemometric modeling (PCA-LDA, PLS-DA) for differentiating authentic PDO Feta from other white printed cheeses. Spectral preprocessing--especially the use of derivatives--enhanced the resolution of critical features, particularly in the fingerprint region, improving the models' discriminatory power. Freeze-dried samples yielded higher classification performance, compared to fresh samples. In freeze-dried samples, models based on second derivative spectra achieved AUC values above 0.95 and test-set accuracies exceeding 95%. Similar studies have reported comparable success using derivative-enhanced FTIR data for cheese authentication. For example, [PERSON] et al. (2021) and [PERSON] et al. (2018) demonstrated high discrimination accuracy in PDO mozzarella and kasseri cheese, respectively, highlighting the generalizability of the method to various protected dairy products [6, 17]. Regarding milk origin, [PERSON] et al. (2024) [31] reported perfect classification between cow versus goat/sheep milk cheees (49 samples, Orthogonal Partial Least Squares Discrimi  
Figure 3: PLS-DA score plots (**A**,**C**,**E**) in the train set and corresponding VIP (**B**,**D**,**F**) for freeze-dried cheese samples using raw (**A**,**B**), first derivative (**C**,**D**), and second derivative (**E**,**F**) FTIR spectra. Features with a VIP score > 2 are highlighted with a yellow point. nant Analysis [OPLS-DA] 100% accuracy) and noted the remaining issue of discriminating goat from sheep cheese. Similarly, a study on Ricotta whey cheese applied FTIR and SPORT-LDA to classify samples by milk source, demonstrating robust separation between cow versus small ruminant milk [32]. Our results support previous findings and further extend them by specifically focusing on brined cheeses, with particular emphasis on Feta cheese. Furthermore, studies on pure milk samples identified routine band differences, such as enhanced CH\({}_{2}\), Amide I/II, and lactose bands in sheep, cow, and goat milk, forming spectral foundations consistent with cheese differentiation [33]. PLS-DA models generally outperformed PCA-LDA, achieving higher classification accuracy on the test set (0.958 vs. 0.917, respectively). This aligns with the strengths of PLS-DA in handling collinear and high-dimensional spectral data. In addition, PLS-DA models maintained excellent performance even with a small number of latent variables, indicating that the models were not overly complex. PCA-LDA, while simpler and computationally less intensive, still achieved acceptable performance, particularly when second derivative spectra were used. PLS-DA also offered better interpretability through variable importance in projection (VIP) scores, which provided direct insight into key discriminatory features. Overall, while both approaches were effective, PLS-DA demonstrated greater robustness and adaptability for spectral classification in this context. Also, it was observed that freeze-drying of samples had a substantial effect on the model's performance. In PLS-DA models applied to second derivative spectra, freeze-dried samples achieved 95.8% test accuracy and 90.9% specificity, compared to 89.0% accuracy in raw samples (see Supplementary Table S2). A similar trend was observed in PCA-LDA results (91.7% vs. 81.5% accuracy), supporting the claim that freeze-drying improves spectral definition and classification performance. The improved classification performance observed in freeze-dried samples is likely due to enhanced spectral clarity resulting from the removal of water content. Water strongly absorbs in the mid-infrared region, especially around \(\sim\)3300 cm\({}^{-1}\) (O-H stretching) and \(\sim\)1600 cm\({}^{-1}\) (H-O-H bending), which can obscure weaker but informative signals. Lyophilization minimizes this interference, leading to better-resolved bands for proteins, lipids, and carbohydrates.