Identification of different bran-fried Atractylodis Rhizoma and prediction of atractylodin content based on multivariate data mining combined with intelligent color recognition and near-infrared spectroscopy.

Unclear established standard of bran-fried Atractylodis Rhizoma (BFAR), a commonly used drug in Traditional Chinese Medicine (TCM), compromised its clinical efficacy. In this study, we explored the correlation between color and near-infrared spectroscopy (NIR) feature with content of atractylodin, then established a rapid recognition model for the optimal degree of processing for BFAR preparation. The results of the Pearson analysis indicated that the color values were significantly and positively correlated with atractylodin content. The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.79-127.25)], G[green value(75.84-89.64)], B[blue value(33.33-42.73)], L[Lightness (81.26-95.09)].Using NIR, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and cluster analysis, three types of BFAR were accurately identified. The prediction model of atractylodin content was established using partial least squares regression analysis. The R2 of the validation set was 0.9717 and the root mean square error was 0.026. In the color judgment model, the processing degree of 8 batches of BFAR from the market is inferior. According to the NIR judgment model, the processing degree of all samples from the market is inferior. In conclusion, the best fire degree of BFAR can be identified quickly and accurately based on our established model. It is a potential method for quality evaluation of Chinese Materia Medica processing.