Prediction of Chemical Composition and Sensory Information of Codonopsis Radix Based on Electronic Nose

Codonopsis Radix (CR), an important species of "medicine-food homology", exhibits broad market prospects, underscoring the urgency and importance of research on its quality. This study specifically measured the alcohol-soluble extract and polysaccharide extract of 77 samples from mainstream producing areas of CR, which serve as key fractions for assessing its quality. Additionally, to gain a comprehensive understanding of the sensory characteristics of samples, the study employed electronic tongue technology to obtain sweetness values, used a colorimeter to determine yellowness values, and captured odor fingerprint information through an electronic nose (E-nose). In the data analysis phase, the study compared the accuracy of various regression prediction models, including Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). After comprehensive evaluation, an SVM algorithm was selected due to its superior prediction performance. To further enhance prediction accuracy, the study utilized a Particle Swarm Optimization (PSO) algorithm to optimize the SVM, resulting in a significant improvement in the prediction accuracy of sweetness values. In conclusion, regression prediction models for chemical composition and sensory information of CR based on an E-nose were established. It represents an enhancement of traditional morphological identification methods for Chinese medicinal herbs and provides new ideas and means for quality evaluation of CR. Furthermore, it offers a reference for quality evaluation of other similar Chinese medicinal herbs.