Classification of fermentation methods for white mulberry products using fluorescence spectroscopy combined with deep learning.

In the field of cosmetic raw materials, accurately distinguishing plant fermentation extracts from traditional water extracts is crucial for ensuring product quality, user safety, and the authenticity of claimed effects. However, conventional chemometrics has limited capacity to explore the nonlinear characteristics of complex fluorescence spectra, and machine learning still faces shortcomings in feature representation and generalization capability, necessitating the development of more efficient and accurate identification methods. In this study, white mulberry (Morus alba L.) was used as the raw material to prepare 966 samples, including yeast fermentation extracts, lactic acid bacteria fermentation extracts, and water extracts. After collecting the fluorescence spectra of each sample, six deep learning models-Informer, PatchTST, Transformer, TCN, LSTM, and CNN-were constructed, alongside traditional models such as SVM, PCA-LDA, and PLS-DA, to systematically compare classification performance. The experiments showed that the Informer model performed the best, with an accuracy, precision, recall, and F1 score of 0.981, 0.982, 0.981, and 0.981, respectively, surpassing all other deep learning models and significantly exceeding traditional methods such as SVM, PCA-LDA, and PLS-DA, demonstrating superior feature extraction and generalization capabilities. This study integrates fluorescence spectroscopy with deep learning, providing a novel and effective solution for identifying liquid cosmetic raw materials.