ATR‐FTIR Spectroscopy and Deep Learning for Chemometric Analysis and Geographical Classification of Red Wines

This study leverages attenuated total reflection Fourier‐transform infrared (ATR‐FTIR) spectroscopy combined with a novel residual composite convolutional neural network (Res‐MCNN) to address the critical challenge of geographical origin authentication in red wines. By analyzing 200 samples from distinct Chinese regions (Xinjiang and Hebei), we demonstrate the efficacy of ATR‐FTIR in capturing region‐specific spectral fingerprints, particularly highlighting the role of titratable acids and phenolic compounds as discriminative markers. The proposed Res‐MCNN model integrates advanced preprocessing techniques, including competitive adaptive reweighted sampling (CARS) for wavelength selection and decision tree pooling for feature optimization, achieving an exceptional classification accuracy of 97.975%. Our findings reveal that while alcohol and sugar content variations introduce sensitivity in model performance, the robust extraction of high‐dimensional nonlinear spectral features ensures reliable origin traceability when key chemical parameters are controlled. The proposed Res‐MCNN model achieved a classification accuracy of 97.975%, a sensitivity of 98.2%, a specificity of 97.6%, and an AUC of 0.989, demonstrating highly reliable geographical discrimination performance. Furthermore, controlled experiments showed that variations in alcohol (10%–15%) and sugar content (1–20 g/L) altered accuracy by no more than 0.6%, confirming that the model maintains stable traceability performance when key chemical parameters remain within typical ranges.