A Rapid identification of stir-frying temperature and time and prediction of three chemical quality indexes for seeds of fructus tsaoko based on FT-NIR spectroscopies and chemometrics

Seeds of Fructus tsaoko (SFT), which have long been utilized in traditional Chinese medicine, exhibit high sensitivity to processing (paozhi) parameters. These parameters significantly affect the efficacy and safety of the resulting herbal products. This study investigated the feasibility of identifying the stir-frying temperature and duration of SFT by integrating Fourier transform near-infrared spectroscopy (FT-NIR) with chemometric methods, and further aimed to predict the contents of volatile oils, flavonoids, and polyphenols. Qualitative and quantitative models were developed using partial least squares discriminant analysis (PLS-DA) and partial least squares regression analysis (PLSR), respectively. The results demonstrate that by selecting the most suitable spectral preprocessing techniques—such as mean centering, autoscaling, normalization, smoothing, derivative transformation, SNV, MSC, FT and WT—the accuracy rate of the optimized model reached up to 99.02%, with all quantitative models achieving correlation coefficients above 0.90. Overall, this study offers valuable insights into quality control during the processing of Chinese medicinal materials.