Intelligent Fusion of Multi-Source Senses Information for Identifying the Nature of Five Flavors in Chinese Medicine: A Comprehensive Study of Five Classifications.

ABSTRACT

Objective: To develop a classification model for the five flavors of Chinese medicine using advanced multi-source intelligent sensory information fusion technology. The primary aim is to investigate the feasibility of applying this model to classify and identify the flavors of various Chinese medicines effectively. Methods: We selected 122 representative Chinese medicines, each exhibiting a single distinct flavor (sour, pungent, salty, sweet, bitter), along with 14 common foods. Utilizing the nature and flavors of these decoction pieces specified in Chinese Pharmacopeia (ChP)2020 and the inherent attributes of food components, we obtained valuable data from various sensors, including the PEN3 electronic nose, ASTREE electronic tongue, and SA402B electronic tongue. We then collected single-source data matrices from these sample sensors and a multi-source data matrix that combined the data from all sensors. Using discriminant analysis (DA), principal component analysis-discriminant analysis (PCA-DA), and K-nearest neighbor algorithm (KNN) three kinds of chemometric methods were used to establish five flavors and five-category discrimination models. The results were comprehensively evaluated with the highest correct rate of the model of leave-one-out cross-validation as the index. Results: Upon leave-one-out cross-validation, the correct judgment rate of the five flavors, five-category two-source fusion DA discrimination model (83.8%; ASTREE + SA402B) was significantly higher than the correct judgment rate of the single-source optimal DA and KNN model (73.5%; ASTREE). Following full-sample modeling, the correct judgment rate of the five flavors, five-category three-source fusion DA discrimination model (94.9%; PEN3+ASTREE+SA402B) rose substantially. This was higher than the correct judgment rate of the single-source optimal DA model (77.9%; ASTREE) and slightly higher than the two-source optimal correct judgment rate (89.7%; PEN3 + ASTREE). Conclusions: Compared to single-source identification, multi-source intelligent senses information fusion (MISIF) significantly improved accuracy, providing a new outlook for identifying flavor in Chinese medicine.