Honeysuckle flower is a common edible-medicinal food with significant anti-inflammatory efficacy. Process quality control of its ethanol precipitation is a topical issue in the pharmaceutical field. Near infrared (NIR) spectroscopy is commonly used for process quality analysis. However, establishing a robust and reliable quantitative model of complex process remains a challenge in industrial applications of NIR. In this paper, modeling design based on quality by design concept (QbD) was implemented for the ethanol precipitation process quality control of Honeysuckle flower. According to the 56 models' performances and 25 contour plots, quadratic model was the best with R adj 2 increasing from 0.1395 to 0.9085, indicating the strong interaction among spectral pre-processing methods, variable selection methods, and latent factors. SG9 and CARS was an appropriate combination for modeling. Furthermore, spectral assignment method was creatively introduced for variable selection. Another 56 models' performances and 25 contour plots were established. Compared with the chemometric variable selection method, spectral assignment combined with QbD concept made a higher R pre 2 and a lower RMSEP. When the latent factors of PLS was small, R pre 2 of the model by spectral assignment increased from 0.9605 to 0.9916 and RMSEP decreased from 0.1555 mg/mL to 0.07134 mg/mL. This result suggests that the variable selected by spectral assignment is more representative and precise. This provided a novel modeling guideline for process quality control in PAT.

Color is a characteristic that has long been used to evaluate the quality of Chinese herbal medicine (CHM). In this study, safflower (Carthamus tinctorius L.) was taken as a representative example to examine the application of color characteristics to evaluate quality. A computer vision system was established for the objective and nondestructive assessment of color using image processing algorithms.Color parameters were investigated based on the RGB, L * a * b * and HSV color spaces.The content of hydroxysafflor yellow A (HSYA), a major bioactive constituent of safflower, was determined by high-performance liquid chromatography. The relationship between HSYA content and color values was investigated by Pearson correlation analysis. A multiple linear regression model was established to predict the HSYA content from color values. The red color and lightness of safflower were found to be significantly related to HSYA content. The prediction equation obtained by multiple regression was reliable with an R 2 value of 0.805 (P < .01). Together, the results suggest that the computer vision technique could be used as a promising and non-destructive technology for color measurement and quality evaluation of CHM.

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