常洪娟,蒙庆华,吴哲锋,邱邹全,倪淳宇,马煜雯,桑丽婷,姚嘉炜,黄玉清,李 钰.基于反向传播神经网络和高光谱成像的芒果可溶性固形物含量检测[J].食品安全质量检测学报,2024,15(2):141-148
基于反向传播神经网络和高光谱成像的芒果可溶性固形物含量检测
Detection of soluble solids content in mango based on backpropagation algorithm neural network and hyperspectral imaging
投稿时间:2023-11-13  修订日期:2024-01-10
DOI:
中文关键词:  可见光-近红外高光谱成像  芒果  无损检测  可溶性固形物含量  反向传播神经网络
英文关键词:visible-near-infrared hyperspectral imaging  mango  non-destructive testing  soluble solids content  backpropagation algorithm neural network
基金项目:广西科技基地和人才专项(桂科AD20238059)、广西学位与研究生教育改革项目(JGY2022220)、广西普通本科高校示范性现代产业学院-南宁师范大学智慧物流产业学院建设项目示范性现代产业学院(6020303891823)
作者单位
常洪娟 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
蒙庆华 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
吴哲锋 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
邱邹全 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
倪淳宇 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
马煜雯 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
桑丽婷 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
姚嘉炜 南宁师范大学物理与电子学院;南宁师范大学, 广西信息功能材料与智能信息处理重点实验室 
黄玉清 南宁师范大学, 北部湾环境演变与资源利用教育部重点实验室, 广西地表过程与智能 模拟重点实验室 
李 钰 广西水果技术指导办公室 
AuthorInstitution
CHANG Hong-Juan College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
MENG Qing-Hua College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
WU Zhe-Feng College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
QIU Zou-Quan College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
NI Chun-Yu College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
MA Yu-Wen College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
SANG Li-Ting College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
YAO Jia-Wei College of Physics and Electronics, Nanning Normal University;Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University 
HUANG Yu-Qing Key Laboratory of Environmental Evolution and Resource Utilization of Beibu Gulf, Ministry of Education, and Key Laboratory of Ground Surface Processes and Intelligent Simulation of Guangxi, Nanning Normal University 
LI Yu Guangxi Fruit Technical Guidance Office 
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中文摘要:
      目的 比较反向传播神经网络(backpropagation algorithm neural network, BPNN)模型与偏最小二乘回归(partial least squares regression, PLSR)模型在预测芒果可溶性固形物含量(soluble solids content, SSC)方面的性能。方法 使用高光谱成像仪和全自动折光仪采集芒果的近红外高光谱及SSC数据, 建立两种预测模型,采用多元散射校正(multiplicative scatter correction, MSC)进行光谱预处理, 利用遗传算法(genetic algorithm, GA)、区间变量迭代空间收缩算法(interval variable iterative space shrinkage algorithms, IVISSA)和变量组合群体分析算法(variable combination population analysis, VCPA)提取特征波长变量, 通过比较不同特征波长提取方法进一步优化对比预测模型。结果 与PLSR模型相比, BPNN模型在预测SSC方面更为有效。而在IVISSA特征波长变量提取后优化的BPNN模型预测能力最佳, 预测集判定系数、均方根误差(root mean square error of prediction, RMSEP)、残差预测偏差(residual prediction deviation, RPD)分别为0.8641、0.3924和2.7127。结论 该模型可快速、准确地检测芒果的SSC, 并证明可见光-近红外高光谱成像与反向传播神经网络模型相结合有望预测芒果的SSC, 为开发在线芒果SSC无损检测系统奠定基础。
英文摘要:
      Objective To compare the performance of backpropagation algorithm neural network (BPNN) model and partial least squares regression (PLSR) model in predicting soluble solids content (SSC) of mango. Methods The near-infrared hyperspectral and SSC data of mango were collected using a hyperspectral imager and a fully automated refractometer to establish 2 kinds of prediction models, and the spectral was preprocessed by using multiplicative scatter correction (MSC). Genetic algorithm (GA), interval variable iterative space shrinkage algorithms (IVISSA) and variable combination population analysis (VCPA) were used to extract the characteristic wavelength variables. The comparative prediction model was further optimized by comparing different feature wavelength extraction methods. Results Compared with the PLSR model, the BPNN model was more effective in predicting SSC. The BPNN model optimized after IVISSA feature wavelength variable extraction has the best prediction ability, with prediction set determination coefficients , root mean square error of prediction (RMSEP), residual prediction deviation (RPD) of 0.8641, 0.3924 and 2.7127, respectively. Conclusion The model can detect the SSC of mango quickly and accurately, which demonstrates that the combination of visible-near-infrared hyperspectral imaging and backpropagation algorithm neural network modeling is expected to predict the SSC of mango, which lays the foundation for the development of an on-line mango SSC nondestructive testing system.
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