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基于BP神经网络的太赫兹时域光谱对面粉中苯甲酸的定量检测研究

Quantitative Determination of Benzoic Acid in Flour Based on Terahertz Time-Domain Spectroscopy and BPNN Model

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摘要

为建立面粉中添加剂苯甲酸的定量检测模型,首先,探索不同光谱预处理方法对太赫兹光谱的影响,采用平滑、多元散射校正、基线校正和归一化等方法对原始光谱进行校正处理,并建立相应的偏最小二乘(PLS)模型以选择最优预处理方法,发现归一化后建立的PLS模型较优,预测集的相关系数(rp)为0.9790,预测集的均方根误差(RMSEP)为1.28%。其次,分别建立面粉中苯甲酸含量检测的PLS、最小二乘支持向量机(LS-SVM)和反向传播神经网络(BPNN)模型,各模型的对比结果表明:基于太赫兹吸收系数建立的BPNN模型的预测相关系数(rp)为0.9945,预测均方根误差(RMSEP)为0.66%。本研究为面粉中苯甲酸添加剂的无损检测提供了新的解决方案,对促进面粉行业的健康发展具有较为重要的意义。

Abstract

To establish a quantitative detection model of benzoic acid additive in flour, terahertz time-domain spectra of benzoic acid doped at different percentages (mass fraction) in flour are collected, and the absorption coefficient spectra are obtained through calculation. It is found that the absorption peak amplitude is positively correlated with benzoic acid content. As for the detection method, first, we explore the effects of different spectral pretreatment methods on THz spectroscopy, and then adopt methods like smoothing correction, multiple scatter correction (MSC), baseline correction, and normalization correction to perform the appropriate processing. Subsequent to correction, PLS model is established to select the optimal pretreatment method. Experimental results verify that PLS model established subsequent to normalization is more optimal, with the correlation coefficient of prediction (rp) observed to be 0.9790 and root-mean-square error of prediction (RMSEP) observed to be 1.28%. We establish PLS, least squares support vector machine (LS-SVM), and back propagation neural network (BPNN) regression models for the determination of benzoic acid content in flour. It is proved that the most optimal quantitative determination model of benzoic acid content in flour is BPNN model with correlation coefficient of prediction (rp) of 0.9945 and root-mean-square error of prediction (RMSEP) of 0.66% subsequent to the normalization of terahertz absorption coefficient. It is concluded that a new solution for the nondestructive detection of benzoic acid additives in flour has been developed, and provide guidance for the detection of other types of additives, all of which are essential for the healthy development of the flour industry.

Newport宣传-MKS新实验室计划
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中图分类号:O439; O433.4

DOI:10.3788/LOP57.073002

所属栏目:光谱学

基金项目:“十二五”国家863计划、江西省优势科技创新团队建设计划、南方山地果园智能化管理技术与装备协同创新中心项目、江西省教育厅科学技术研究青年项目、江西省博士研究生创新资金项目;

收稿日期:2019-06-26

修改稿日期:2019-09-26

网络出版日期:2020-04-01

作者单位    点击查看

胡军:华东交通大学机电与车辆工程学院, 江西 南昌 330013
刘燕德:华东交通大学机电与车辆工程学院, 江西 南昌 330013
孙旭东:华东交通大学机电与车辆工程学院, 江西 南昌 330013
李斌:华东交通大学机电与车辆工程学院, 江西 南昌 330013
徐佳:华东交通大学机电与车辆工程学院, 江西 南昌 330013
欧阳爱国:华东交通大学机电与车辆工程学院, 江西 南昌 330013

联系人作者:刘燕德(jxliuyd@163.com)

备注:“十二五”国家863计划、江西省优势科技创新团队建设计划、南方山地果园智能化管理技术与装备协同创新中心项目、江西省教育厅科学技术研究青年项目、江西省博士研究生创新资金项目;

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引用该论文

Hu Jun,Liu Yande,Sun Xudong,Li Bin,Xu Jia,Ouyang Aiguo. Quantitative Determination of Benzoic Acid in Flour Based on Terahertz Time-Domain Spectroscopy and BPNN Model[J]. Laser & Optoelectronics Progress, 2020, 57(7): 073002

胡军,刘燕德,孙旭东,李斌,徐佳,欧阳爱国. 基于BP神经网络的太赫兹时域光谱对面粉中苯甲酸的定量检测研究[J]. 激光与光电子学进展, 2020, 57(7): 073002

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