基于深度学习与近红外光谱技术对海洋中药马刀的数智评价研究OA

Objective To establish a rapid and intelligent analytical method for evaluating the quality and grade of the marine traditional Chinese medicinal material Madao,from Guangxi,based on its potential pharmacodynamic components.Methods The contents of calcium carbonate,acid-insoluble ash,and extractives in Madao were determined following the methods specified for oysters in the Chinese Pharmacopeia(2025 edition).The total amino acid content was measured using the ninhydrin method.A multi-task one-dimensional convolutional neural network(1D-CNN)model was constructed,using near-infrared spectroscopy(NIRS)data as input to simultaneously predict the contents of multiple components.A spectral attention mechanism was introduced to enhance model interpretability.Furthermore,the criteria importance through intercriteria correlation(CRITIC)objective weighting method and the technique for order preference by similarity to ideal solution(TOPSIS)multi criteria decision making algorithm were integrated to develop an end-to-end AI-NIRS intelligent evaluation platform.This platform enables automatic classification of quality grade based on comprehensive scores.Results A score-grade prediction model was established based on the determination results of 90 batches of Madao samples using the 1D-CNN.Compared with the traditional partial least squares(PLS)NIRS model,the multi-task CNN model demonstrated significantly superior performance.Validation results showed that the comprehensive scores of the Madao samples followed a Gaussian distribution,with clear differentiation among the various quality grades.This indicates that the established evaluation method is effective and reliable.Conclusion Operating within the framework of the current regulatory system,this method achieved rapid,comprehensive quality assessment and grade differentiation for Madao.It provides a novel technological approach for the digital-intelligence evaluation and scientific supervision of shell-based marine traditional Chinese medicinal materials.