基于BP神经网络快速无损检测开阳枇杷糖度  

Nondestructive Detection of the Sugar Content of Kaiyang Loquat Based on BP Neural Network

作　　者：孟庆龙 冯树南 谭涛 尚静 黄人帅 曹森 MENG Qing-long;FENG Shu-nan;TAN Tao;SHANG Jing;HUANG Ren-shuai;CAO Sen(Food and Pharmaceutical Engineering Institute,Guiyang University,Guiyang 550005,Guizhou,China;Research Center of Nondestructive Testing for Agricultural Products,Guiyang University,Guiyang 550005,Guizhou,China)

机构地区：[1]贵阳学院食品与制药工程学院,贵州贵阳550005 [2]贵阳学院农产品无损检测工程研究中心,贵州贵阳550005

出　　处：《食品研究与开发》2022年第13期135-140,共6页Food Research and Development

基　　金：国家自然科学基金项目(62141501);贵阳市科技计划项目(筑科合同[2021]43-15号);贵阳市科技局贵阳学院专项资金(GYU-KY-[2022]);贵阳学院硕士研究生科研基金项目(GYU-YJS[2021]-45);贵州省大学生创新创业训练计划(202110976044)。

摘　　要：为实现开阳枇杷糖度的快速无损检测,采用紫外/可见光纤光谱仪采集开阳枇杷的反射光谱,探究比较标准正态变换以及多元散射校正预处理原始光谱的效果;应用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)、连续投影算法以及组合两种方法分别筛选特征变量,基于筛选的特征变量构建预测开阳枇杷糖度的反向传播(back propagation,BP)神经网络检测模型。结果表明:标准正态变换预处理效果相对较好;基于CARS从835个全变量中筛选出49个特征变量,使模型的运算效率明显提高;构建的枇杷糖度预测模型中,CARS-BP的性能最好,预测集相关系数为0.91,均方根误差为0.56%,剩余预测偏差为2.42。表明采用紫外/可见光谱结合BP神经网络适用于开阳枇杷糖度的快速无损检测,为后期在线无损检测设备的研发提供参考。The study aimed to realize the rapid nondestructive detection of the sugar content of Kaiyang loquat.The UV/Vis fiber-optic spectrometer was used to obtain the reflectance spectra of Kaiyang loquat the effects of standard normal variation(SNV)and multi-scatter calibration(MSC)on preprocessing the original spectra were explored and compared.The competitive adaptive reweighted sampling(CARS),successive projection algorithm(SPA)and combination of the two methods were employed to select characteristic variables separately.Then the back propagation(BP)neural network model was built up to detect the sugar content of Kaiyang loquat based on the characteristic variables.The results showed that SNV was a desirable spectral preprocessing method.A total of 49 characteristic variables were chosen by CARS from 835 full variables,which evidently improved the working efficiency of the model.Among the established models for predicting the sugar content of Kaiyang loquat,CARS-BP neural network model had the highest detection ability(r_(p)=0.91,RMSEP=0.56%,RPD=2.42).Therefore,it was possible to detect the sugar content of Kaiyang loquat by UV/Vis spectroscopy and BP neural network in a rapid and nondestructive way,which provided theoretical guidance for the research and development of online nondestructive detection equipment.

关 键 词：光谱技术 开阳枇杷 糖度 人工神经网络 无损检测 

分 类 号：TS255.7[轻工技术与工程—农产品加工及贮藏工程] O657.32[轻工技术与工程—食品科学与工程] TP183[理学—分析化学]