摘要
目的建立了一种基于高光谱成像(hyperspectral imaging,HSI)技术的板栗产地溯源模型。方法采集怀柔、迁西和沂蒙短枝3种不同产地板栗的高光谱图像,提取感兴趣区域,建立支持向量机(support vector machine,SVM)板栗产地溯源模型,通过比较分析不同预处理方法对建模结果的影响,选出最佳的预处理组合方法,并使用遗传算法(geneticalgorithm,GA)对模型进一步优化。结果实验结果表明,经多元散射校正(multiplicative scatter correction,MSC)和移动窗口平滑法(moving window smoothing,MWS)组合预处理后的数据所建立的溯源模型预测性能最好,分类的预测精确率达到了95%以上,模型整体的预测准确率为96.61%。经GA对SVM的参数C进行优化,优化后的模型对怀柔板栗和沂蒙短枝板栗的预测精确率达到了100%,模型整体的准确率提高到了98.31%。结论本研究基于高光谱成像技术建立了一种板栗产地溯源模型,经预处理和参数优化后,所建立的模型具有较好的预测性能,为板栗的产地溯源提供了一种新方法。
Objective To establish a model of Castanea mollissima origin tracing based on hyperspectral imaging(HSI)technology.Methods s Hyperspectral images of 3 kinds of different origin Castanea mollissima from Huairou,Qianxi and Yimeng were collected,region of interest was extracted,and support vector machine(SVM)tracing model of Castanea mollissima origin was established.By comparing and analyzing the influence of different pretreatment methods on the modeling results,the best pretreatment combination method was selected,and the genetic algorithm(GA)was used to further optimize the model.Results The experimental results showed that the traceability model established by the combination of multiplicative scatter correction(MSC)and moving window smoothing(MWS)had the best prediction performance.The prediction accuracy of classification reached more than 95%,and the overall accuracy of the model was 96.61%.The GA was used to optimize the parameter C of SVM,and the prediction accuracy of the optimized model reached 100%,and the overall accuracy of the model increased to 98.31%.Conclusion In this study,based on hyperspectral imaging technology,a Castanea mollissima origin tracing model is established.After preprocessing and parameter optimization,the established model has better prediction performance,which provides a new method for the origin tracing of Castanea mollissima.
作者
孙晓荣
张晨光
刘翠玲
吴静珠
张善哲
SUN Xiao-Rong;ZHANG Chen-Guang;LIU Cui-Ling;WU Jing-Zhu;ZHANG Shan-Zhe(School of Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,China;Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing100048,China)
出处
《食品安全质量检测学报》
CAS
北大核心
2023年第18期59-65,共7页
Journal of Food Safety and Quality
基金
北京市自然科学基金项目(4222043)
2021年教育部高教司产学合作协同育人项目(202102341023)
2022年北京工商大学研究生教育教学改革专项(20220613)。
关键词
高光谱成像
支持向量机
遗传算法
板栗
产地溯源
hyperspectral imaging
support vector machine
genetic algorithm
Castanea mollissima
origin tracing
作者简介
通信作者:张晨光,硕土研究生,主要研究方向为食品安全检测技术。E-mail:1102622879@qq.com;孙晓荣,硕士,教授,主要研究方向为智能测量技术与数据处理、系统建模与仿真方法、智能控制方法研究。E-mail:sxrchy@sohu.com。
