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

作　　者：谷传凯 褚璇 刘洪利 韦鸿钰[1] 牟英辉 马稚昱[1] GU Chuankai;CHU Xuan;LIU Hongli;WEI Hongyu;MU Yinghui;MA Zhiyu(College of Mechanical and Electrical Engineering,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;College of Engineering,South China Agricultural University,Guangzhou 510642,China)

机构地区：[1]仲恺农业工程学院机电工程学院,广东广州510225 [2]华南农业大学工程学院,广东广州510642

出　　处：《食品工业科技》2025年第7期227-234,共8页Science and Technology of Food Industry

基　　金：国家自然科学基金青年科学基金项目(32102087);广州市科技计划项目(2023A04J1667)。

摘　　要：为了无损、快速地实现金线莲叶片中多糖与黄酮含量水平的判别,本研究以在不同光周期下(10、12、14、16、18、20 h/d)栽培的金线莲为样本,通过高光谱技术获取叶片像素光谱数据,运用传统机器学习(PCALDA与PCA-SVM)与深度学习方法(1D CNN及其优化方法)构建了相应的判别模型。结果表明,1D CNN模型在训练集、验证集与独立测试集上的判别准确率优于PCA-LDA与PCA-SVM模型,分别为99.99%与99.89%、99.98%与99.78%、91.62%与87.92%。通过在1D CNN模型中引入Dropout层,模型的泛化能力得到增强,多糖与黄酮含量水平在独立验证集上的判别准确率分别提升至98.92%与95.67%。基于此,本研究进一步构建了多糖与黄酮含量水平的可视化图像,通过颜色直观展示了不同含量水平的判别结果。研究证实了高光谱技术可用于评估金线莲叶片中多糖与黄酮的含量水平,研究结果可为金线莲的品质控制提供技术支撑。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 1D 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 本文网刊: 

分 类 号：O433.4[机械工程—光学工程] TQ936.2[理学—光学]