食品工业科技2024，Vol.45Issue(10)：282-291,10.DOI:10.13386/j.issn1002-0306.2023090074
基于高光谱成像技术的山楂产地判别研究
Identification of Geographical Origin for Hawthorn Based on Hyperspectral Imaging Technology
摘要
Abstract
The geographical origin was one of the important factors affecting the quality of hawthorn.To discriminate the geographical origin of hawthorn rapidly and nondestructively,hawthorns from five different provincial production areas were used as samples,and visible-shortwave infrared(410～2500 nm)band hyperspectral data were obtained for the pedicel face(G),side(C),and bottom(D)of each sample by using a near-infrared hyperspectral imaging system.Partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and random forests(RF)classification models were built by multivariate scattering correction(MSC),first derivative(D1),SG smoothing(Savitzky-Golay,SG),and standard normal transform(SNV)four preprocessing methods.The results showed that the D-D1-SVM model discriminated optimally,with 100%accuracy in both the training and prediction sets.To simplify the model,successive projections algorithm(SPA)and competitive adaptive reweighted sampling algorithm(CARS)were applied to select feature wavelength.The multivariate data analysis revealed that the D-SPA-SVM model had the best performance,with an accuracy of 95.2%and 93%for the training and prediction sets,respectively.This study could provide technical support for rapid and non-destructive identification of hawthorn origin.关键词
高光谱成像技术/山楂/产地识别/无损检测/机器学习Key words
hyperspectral imaging technology/hawthorn/origin identification/nondestructive testing/machine learning分类
数理科学引用本文复制引用
刘子健,顾佳盛,周聪,王游游,杨健,黄俊,王宏鹏,白瑞斌..基于高光谱成像技术的山楂产地判别研究[J].食品工业科技,2024,45(10):282-291,10.基金项目
浙江省"领雁"攻关计划项目(2022C02023) （2022C02023）
中央本级重大增减支项目(2060302) （2060302）
浙江科技大学科研业务费专项资金项目(2023QN024,2023JLZD007) （2023QN024,2023JLZD007）
中药全产业链质量技术服务平台(2022-230-221). （2022-230-221）
