食品工业科技2025，Vol.46Issue(7)：227-234,8.DOI:10.13386/j.issn1002-0306.2023100154
基于高光谱技术的金线莲多糖与黄酮含量的无损检测
Non-destructive Detection of Polysaccharide and Flavonoid Contents in Anoectochilus roxburghii Using Hyperspectral Technology
摘要
Abstract
This study aimed to rapidly and non-destructively evaluate the levels of polysaccharides and flavonoids in A.roxburghii leaves under various photoperiods(10,12,14,16,18,and 20 h/d).Hyperspectral imaging was employed to acquire pixel-level spectral data from the leaves,and discriminant models for content levels were developed using traditional machine learning methods(PCA-LDA and PCA-SVM)and deep learning approaches(1D CNN and its optimized version).The findings revealed that the 1D CNN model outperformed the PCA-LDA and PCA-SVM models in terms of discrimination accuracy on the training,validation,and independent test sets,achieving 99.99％and 99.89％,99.98％and 99.78％,and 91.62％and 87.92％,respectively.The introduction of a Dropout layer in the lD CNN model enhanced its generalization capability,increasing the discrimination accuracy for polysaccharide and flavonoid content levels on the independent test set to 98.92％and 95.67％,respectively.Additionally,visualization images depicting the discrimination results for different compound levels were constructed,providing an intuitive representation.This study validates the feasibility of hyperspectral imaging in evaluating polysaccharide and flavonoid levels in A.roxburghii leaves cultivated under various photoperiods,and the research results can provide technical support for the quality control of A.roxburghii.关键词
无损检测/深度学习/机器学习/金线莲/多糖与黄酮含量Key words
non-destructive testing/deep learning/machine learning/Anoectochilus roxburghii/polysaccharides and flavonoids contents分类
数理科学引用本文复制引用
谷传凯,褚璇,刘洪利,韦鸿钰,牟英辉,马稚昱..基于高光谱技术的金线莲多糖与黄酮含量的无损检测[J].食品工业科技,2025,46(7):227-234,8.基金项目
国家自然科学基金青年科学基金项目(32102087) （32102087）
广州市科技计划项目(2023A04J1667). （2023A04J1667）
