# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 5 Conclusions  
This study demonstrates the effectiveness of ATR-FTIR spectroscopy combined with supervised chemometric models (PCA-LDA and PLS-DA) as a robust, rapid, and non-destructive approach for verifying the authenticity of PDO Feta cheese. The results highlight the importance of both spectral preprocessing and sample preparation, with freeze-dried samples, particularly those analyzed using second derivative transformations, yielding the highest classification accuracy (95.8%), perfect sensitivity, and strong specificity.  
Key discriminatory spectral regions included bands associated with lipids (-2920 and -1745 cm\({}^{-1}\)), proteins (-1650 and 1540 cm\({}^{-1}\)), and carbohydrates (-1040-1060 cm\({}^{-1}\)), confirming the method's ability to resolve subtle molecular differences between authentic and non-authentic cheese types.  
Beyond its analytical performance, the approach offers practical advantages: it is rapid (requiring less than 6 min per sample), reagent-free, and uses minimal sample quantities, features that make it well-suited for routine quality control and authenticity verification in regulatory or industrial settings.  
Future research should aim to validate the method on broader datasets, including seasonal, geographic, and adulterated samples, as well as mixed-milk cheees. In parallel, the integration of advanced machine learning techniques and portable FTIR systems could facilitate real-time, in-field authentication, expanding the method's applicability across the dairy supply chain.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15158272/s1](https://www.mdpi.com/article/10.3390/app15158272/s1)]([https://www.mdpi.com/article/10.3390/app15158272/s1](https://www.mdpi.com/article/10.3390/app15158272/s1)), Figure S1: Mean FT-IR spectra and derivatives for raw cheees; Figure S2: PCA score and scree plots for raw samples under raw, d1, and d2 preprocessing; Table S1: PCA-LDA metrics for raw samples; Table S2: PLS-DA metrics for raw samples.  
Conceptualization, E.M.; methodology, C.S.P., E.M., A.M. and D.K.; software, M.K.; validation, L.D. and M.K.; formal analysis, L.D.; investigation, E.M., M.K. and L.D.; resources, D.K. and E.M.; data curation, M.K. and L.D.; writing--original draft preparation, E.M., M.K. and L.D.; writing--review and editing, D.K., C.S.P., A.M. and E.M.; visualization, M.K.; supervision, E.M., C.S.P. and A.M.; project administration, E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We would like to thank [PERSON] from the Laboratory of Food Chemistry, Biochemistry and Technology, Department of Nutrition and Dietetics, for her valuable support in the freeze-drying of the samples. Special thanks also go to [PERSON] from the Laboratory of Foods of Animal Origin, Department of Animal Science, University of Thessaly, for her contribution to the ELISA analysis.  
The authors declare no conflicts of interest.