Abstract:Storage time has a crucial effect on the quality of cabbage. Identifying the optimal storage time mainly relies on manual experience, which is not accurate and reliable. This study aimed to establish a stoichiometric recognition model based on Raman spectrum to calculate the storage time of cabbage. The Raman spectra corresponding to different storage times of cabbage heart were evaluated. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were established by testing the Raman spectra of cabbage heart every 48 h after harvest. The results showed that soluble sugars, cellulose, soluble proteins, and carotenoids were the main components affecting the storage quality of cabbage. The classification accuracy of linear and polynomial functions of the SVM model and verification set were 97.75 and 97.34%, respectively, and the classification accuracy of the SVM model and verification set were 97.50% and 96.00%, respectively. The classification accuracy of the LDA quadratic function model and the verification set were 99.00% and 97.00%, respectively. Both SVM and LDA models based on Raman spectrum effectively identified different optimal storage times of cabbage. Thus, this study provides reference and technical support for the identification of cabbage freshness based on storage time.
