Research on quality evaluation system and grade classification of Angelica dahurica based on artificial intelligence and multispectral technology

Abstract

Angelica dahurica (AD) is both a widely used spice and a precious traditional Chinese medicine. Currently, its quality evaluation predominantly depends on traditional identification methods and physicochemical assessments, which are often subjective or time-consuming, thus limiting their suitability for rapid, non-destructive, and accurate quality evaluation. Therefore, this study constructed a quality evaluation system based on key criteria: shape, color, odour and texture. Experienced traditional medicine experts scored 611 samples according to this system, categorizing them into three quality grades. Imperatorin and isoimperatorin in 30 randomly selected batches were quantified by HPLC, revealing a positive correlation with quality grade and confirming the system's accuracy and reliability. Moreover, quality grading models were established by integrating multispectral imaging technology with artificial intelligence technologies such as CNN and Transformer. The Transformer model achieved the highest accuracy of 88.71 %. Overall, this study improves the objectivity and reproducibility of traditional identification methods. It also demonstrates that integrating artificial intelligence with multispectral imaging enables non-destructive, rapid, and precise classification of AD, offering a novel approach for quality control of medicinal materials.