# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 3 Results
### Exploratory Analysis  
The initial assessment of the FTIR spectra revealed consistent patterns across cheese categories, with notable absorbance features characteristic of protein, lipid, and carbohydrate functional groups. Figure 1 presents the FT-IR spectra of freeze-dried samples of Feta and non-Feta white primed cheeses for the different expressions. Figure 1A displays the mean raw spectra for all samples, with strong absorption bands observed near 3300 cm\({}^{-1}\) (O-H and N-H stretching), 2920 cm\({}^{-1}\) (C-H asymmetric stretching in lipids), and in the fingerprint region between 1800 and 600 cm\({}^{-1}\), where Amide I, Amide II, ester carbonyl, and C-O/C-N stretching are apparent. The raw absorbance curves for Feta and non-Feta white primed cheeses are largely similar, reflecting the shared major functional groups, and visual inspection cannot reliably distinguish the two classes. The most intense bands and the broad fingerprint region appear nearly identical between groups, with minor differences in the carbonyl (\(\sim\)1745 cm\({}^{-1}\)) and CH\({}_{2}\) bending (\(\sim\)1460 cm\({}^{-1}\)) regions. The first derivative (Figure 1B) and the second derivative (Figure 1C) reveal minor differences. However, the derivatives helped sharpen peak shapes and uncover less prominent features--particularly within the complex fingerprint region (1800-600 cm\({}^{-1}\)). For instance, improved definition is visible in the Amide I-II region (\(\sim\)1700-1500 cm\({}^{-1}\)) and in parts of the spectrum between 1300 and 1000 cm\({}^{-1}\). A comparable spectral overview was also performed for fresh (non-freeze-dried) samples, which also exhibited similar absorbance patterns across the same wavenumber regions. Although slight baseline fluctuations were observed--likely due to higher moisture content--the overall spectral structure remained consistent. A summary figure is included in the Supplementary Materials (Supplementary Figure S1). The higher classification accuracy obtained with freeze-dried samples (e.g., PLS-DA accuracy = 95.8% vs. 85% for fresh samples) is most likely due to the removal of water, which reduces the broad O-H absorption around 3300 cm\({}^{-1}\) and lowers baseline noise. Moisture elimination also improves sample homogeneity and baseline stability, thereby enhancing the contrast of key lipid, protein, and carbohydrate bands in the fingerprint region. These factors collectively give clearer, more reproducible spectra and facilitate better model discrimination.  
Principal component analysis was conducted to explore potential clustering patterns among the cheese samples using raw, first derivative, and second derivative FTIR spectra. As shown in Figure 2A,C,E, there was no distinct separation between Feta and non-Feta white brined cheese samples under any preprocessing condition. Although PC1 captured a substantial proportion of the total variance in the raw spectra (62.0%), and less so in the derivative transformations (42.1% and 12.6%, respectively), this variance did not translate into clear group separation in the PC score plots. The substantial overlap between classes across all preprocessing modes was observed, indicating that unsupervised PCA alone is insufficient to discriminate cheese types based on FTIR data. These findings justify the application of supervised classification methods for more effective group differentiation. A similar lack of class separation was observed in the PCA of non-freeze-dried samples (Supplementary Figure S2).  
Figure 1: Mean FT-IR spectra and their derivatives for Feta vs. non-Feta white brined cheeses.