摘要
为构建基于近红外光谱数据的定量预测模型,实现对覆盆子中鞣花酸和山柰酚-3-O-芸香糖苷含量的快速预测,本研究收集了不同来源的覆盆子样本,采集其近红外光谱(NIR),通过高效液相(HPLC)检测其鞣花酸和山柰酚-3-O-芸香糖苷的含量,运用MATLAB R2020b软件对光谱数据进行预处理,运用竞争自适应重加权抽样CARS筛选特征波长,建立偏最小二乘(PLS)和随机森林(RF)模型筛选出最佳预处理方法,进而筛选出最佳的预测模型。结果表明,应用SNV+FD+CARS+PLS模型可获得鞣花酸的最优预测结果,定量模型测试集的相关系数(R^(2)_(p))为0.9038,应用SG+FD+CARS+PLS模型可获得山柰酚-3-O-芸香糖苷的最优预测结果,R^(2)_(p)为0.7586。经归一化和一阶导数组合处理的近红外数据在正交偏最小二乘判别分析(OPLS-DA)模型中能够判别区分覆盆子合格品,累积方差值R^(2)Y为0.728,预测率Q^(2)为0.681。本研究结果表明,应用近红外光谱技术可以快速预测覆盆子中鞣花酸和山柰酚-3-O-芸香糖苷的含量,对覆盆子样品进行品质初步判别。
To establish a quantitative prediction model based on near-infrared spectroscopy data,and to realize the rapid prediction of ellagic acid and kaempferol-3-O-rutinoside content in Rubus chingii Hu.,the samples from different sources were collected,the near-infrared spectroscopy was collected,and the contents of ellagic acid and kaempferol-3-O-rutinoside were detected by HPLC.Spectral data were preprocessed by MATLAB R2020b software.The competitive adaptive reweighted sampled CARS were used to screen characteristic wavelengths,and partial least squares(PLS)and random forest(RF)models were established to screen the best pretreatment methods,and then the best prediction models were selected.Results showed that the optimal prediction results of ellagic acid were obtained by SNV+FD+CARS+PLS model,and the correlation coefficient(R^(2)_(p))of the quantitative model test set was 0.9038.The optimal prediction results of kaempferol-3-O-rutinoside were obtained by SG+FD+CARS+PLS model,and the R^(2)_(p) of the quantitative model test set was 0.7586.The NIR data treated by the combination of normalization and first derivative can distinguish Rubus chingii Hu.qualified products in OPLS-DA model,the cumulative variance value R^(2)Y was 0.728,and the prediction rate Q^(2) was 0.681.The results of this study indicated that the content of ellagic acid and kaempferol-3-O-rutinoside in Rubus chingii Hu.can be quickly predicted by NIR technique,and the quality of raspberry samples can be preliminarily judged.
作者
汪传宝
胡淳莉
占建勇
李海申
姜玲
郑平汉
王志安
孙健
WANG Chuanbao;HU Chunli;ZHAN Jianyong;LI Haishen;JIANG Ling;ZHENG Pinghan;WANG Zhian;SUN Jian(Zhejiang Research Institute of Traditional Chinese Medicine Co.,Ltd.,Hangzhou 310023,Zhejiang;Chun′an County Food and Drug Testing Center,Chun′an 311700,Zhejiang;Zhejiang Chun′an County Agricultural and Rural Bureau,Chun′an 311700,Zhejiang;Chun′an County Agricultural and Rural Development Service Center,Chun′an 311700,Zhejiang;Chun′an County Linqi Town Chinese Herbal Medicine Office,Chun′an 311703,Zhejiang;State Key Laboratory for Quality Ensurance and Sustainable Use of Genuine Herbs,Beijing 100700)
出处
《浙江农业科学》
2025年第9期2126-2136,共11页
Journal of Zhejiang Agricultural Sciences
基金
中央本级重大增减支项目“名贵中药资源可持续利用能力建设项目”(2060302-2202-09)
浙江省农业(中药材新品种选育)新品种选育重大科技专项(2021C02074)
浙江省科技计划(省属科研院所扶持专项)。
