The Application of Fourier Transform Infrared Spectroscopy and Chemometrics in Identifying Signatures for Sheep’s Milk Authentication

English. This study explores the application of Fourier transform infrared (FTIR) spectroscopy combined with chemometric and machine learning techniques for authenticating sheep’s milk and distinguishing it from cow’s milk. The demand for accurate authentication methods is driven by the high production costs of sheep’s milk and the prevalent issue of adulteration with cow’s milk, which can have economic, health, and ethical implications. Our research utilizes exploratory analysis, regression, and classification tasks on spectral data to identify characteristic spectral signatures and physicochemical parameters for sheep’s milk. Key methods included the application of decision trees, random forests, and k-nearest neighbors (KNN), with the random forest model showing the highest predictive accuracy (R2 of 0.9801). Principal Component Analysis (PCA) and analysis of variance (ANOVA) revealed significant spectral and compositional differences, particularly in fat content and wavelengths responsible for amide I and II bands (1454 nm and 1550 nm) correlated with the conformational characteristics of the proteins, with sheep’s milk exhibiting higher values than cow’s milk. These findings indicate the potential of FTIR spectroscopy as a reliable tool for milk authentication. Currently, digitalization within the milk production chain is limited, particularly in the case of regional dairy products. The introduction of integrated photonics, machine learning, and, in the future, telemetry systems would enable dairy farmers to optimize their operations and ensure the origin and quality of the milk supplied to milk producers.

English. sheep milk; authentication; FTIR spectroscopy; chemometrics; machine learning

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