Application of FT-IR and machine learning in the characterization of organic and biodynamic wines vs conventional wines

• Combine FT-IR spectroscopy with machine learning to authenticate wine production methods. • Principal Component Analysis (PCA) reveals separation between white and red wines, but limited discrimination based on production method. • Achieve 100% classification accuracy of white wines (conventional, organic, and biodynamic) using SVM and RF models. • Suggest the potential of FT-IR and ML as supportive tools for certifying organic and biodynamic viticulture practices. In this study, Fourier-transform infrared spectroscopy (FT-IR) combined with machine learning (ML) algorithms was employed to characterize wines according to the specific viticulture and winemaking practices (conventional, organic, and biodynamic). A total of 83 wine samples, including both red and white varieties, were analyzed. FT-IR spectral data were processed using unsupervised methods, such as principal component analysis (PCA), and supervised classification algorithms, including random forest (RF), support vector machines (SVM), and linear discriminant analysis (LDA). PCA showed a clear distribution between white and red wines, although no evident clustering was observed based on specific vineyard and winemaking practices. Conversely, the application of supervised algorithms (SVM and RF) to the FT-IR data enabled accurate classification of white wines according to their production method, achieving 100% accuracy on the test set for both models. In the case of red wines, the SVM model yielded the best performance, with an accuracy of 83.33% in the test set. These results highlight the potential of FT-IR spectroscopy combined with ML as a reliable approach for determining the production method of wine samples. Furthermore, a web-based platform has been developed to facilitate access to the implemented classification algorithms.