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基于高光谱技术和IRIV-FOA-ELM算法的花椒挥发油无损检测

Nondestructive Testing of Volatile Oil of Zanthoxylum Bungeanum Based on Hyperspectral Technique and IRIV-FOA-ELM Algorithm

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摘要

为了对花椒挥发油的含量进行快速、无损、低成本的检测,以汉源县花椒为实验对象,采集其在400~1000 nm波长范围内的光谱数据,然后采用标准正态变量变换(SNVT)方法对光谱数据进行预处理,利用迭代保留信息变量算法(IRIV)进行特征变量的提取,并建立极限学习机(ELM)回归模型,模型结果如下:校正集的决定系数 RC2为0.8522,均方根误差RMSEC为0.3475;预测集的决定系数 RP2为0.8365,均方根误差RMSEP为0.5737。为了进一步提高模型的预测性能,利用果蝇优化算法(FOA)对极限学习机的输入权值进行自适应优化。最终,优化后模型(IRIV-FOA-ELM)的决定系数 RC2为0.8792,RMSEC为0.3323, RP2为0.8659,RMSEP为0.3621。结果表明,高光谱成像技术可以对花椒挥发油进行快速无损检测,同时为其他农产品挥发油检测提供一种新的方法和思路。

Abstract

For quick, nondestructive, and cheap testing of the volatile oil of Zanthoxylum bungeanum, Chinese prickly ash samples were selected as the experimental object and collected from Hanyuan County for hyperspectral analysis in the 400-1000-nm wavelength. Standard normal variable transformation (SNVT)was used to preprocess the spectral data and the method of iteratively retains informative variables (IRIV) was used to extract the feature variables. The regression model of extreme learning machine (ELM) was established. The following results were obtained using the model: the coefficient of determination ( RC2) and root-mean-square error of the calibration set (RMSEC) were 0.8522 and 0.3475 and the coefficient of determination ( RP2) and root-mean-square error of the prediction set (RMSEP) were 0.8365 and 0.5737. To improve the prediction performance of the model, the fruit fly optimization (FOA) algorithm was used to optimize the input weights of ELM. Finally, RC2 and RMSEC of the optimized model (IRIV-FOA-ELM) were 0.8792 and 0.3323, respectively, and RP2 and RMSEP were 0.8659 and 0.3621, respectively. The results show that the hyperspectral imaging technique can be used for the rapid nondestructive testing of the volatile oil of Z. bungeanum, providing a new method and concept for the testing of volatile oil of other agricultural products.

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中图分类号:S24

DOI:10.3788/LOP57.203002

所属栏目:光谱学

基金项目:四川省教育厅自然科学项目、四川省2018—2020年高等教育人才培养质量和教学改革项目;

收稿日期:2020-01-21

修改稿日期:2020-02-19

网络出版日期:2020-10-01

作者单位    点击查看

纪然仕:四川农业大学机电学院, 四川 雅安 625014
陈晓燕:四川农业大学信息工程学院, 四川 雅安 625014
刘素珍:四川农业大学机电学院, 四川 雅安 625014
饶利波:四川农业大学机电学院, 四川 雅安 625014
汪震:四川农业大学信息工程学院, 四川 雅安 625014

联系人作者:陈晓燕(chenxy@sicau.edu.cn)

备注:四川省教育厅自然科学项目、四川省2018—2020年高等教育人才培养质量和教学改革项目;

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引用该论文

Ji Ranshi,Chen Xiaoyan,Liu Suzhen,Rao Libo,Wang Zhen. Nondestructive Testing of Volatile Oil of Zanthoxylum Bungeanum Based on Hyperspectral Technique and IRIV-FOA-ELM Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(20): 203002

纪然仕,陈晓燕,刘素珍,饶利波,汪震. 基于高光谱技术和IRIV-FOA-ELM算法的花椒挥发油无损检测[J]. 激光与光电子学进展, 2020, 57(20): 203002

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