基于AlexNet卷积神经网络的大米产地高光谱快速判别
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作者单位:

(1.北京工商大学 食品安全大数据技术北京市重点实验室 北京 100048;2.浙江省农业科学院 农业部农产品信息溯源重点实验室 杭州 310021)

作者简介:

吴静珠(1979—),女,博士,教授 E-mail:pubwu@163.com

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基金项目:

农业部农产品信息溯源重点实验室开放课题(2018);国家留学基金委访学项目(2019);国家重点研发计划子课题(2018YFD0101004-03)


Fast Hyperspectral Discrimination of Rice Origin Based on AlexNet Convolutional Neural Network
Author:
Affiliation:

(1.Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing 100048;2.Key Laboratory of Information Traceability of Agricultural Products,Zhejiang Academy of Agricultural Sciences,Hangzhou 310021)

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    摘要:

    采集我国东北和非东北10个产地、4个品种共计1 000份单粒大米样本在波长950~1 700 nm区间的高光谱图像,按照单粒大米轮廓提取感兴趣区域并计算平均光谱,采用主成分分析从样本集光谱矩阵提取累计贡献率大于99%的第一、二主成分,根据载荷矩阵系数最大值筛选与第一、二主成分相关性最强的特征波长1 396.67 nm和1 467.38 nm。针对两组特征波长图像进行主成分分析,分别选取前三维主成分,共计可得2×3组训练样本集。结果表明:基于AlexNet卷积神经网络训练建立6组东北/非东北大米产地高光谱快速判别模型,均有较高的识别准确率,其中基于1 467.38 nm波长的第三主成分图像建立的东北/非东北大米产地判别模型的性能最佳,其识别准确率可达99.5%。

    Abstract:

    There were 1 000 single-grain rice samples collected from 10 origins and 4 varieties in Northeast and non-Northeast China.Near-infrared hyperspectral images were acquired within the wavelength range of 950 nm to 1 700 nm.The region of interest was selected according to the outline of single rice from the images to calculate average spectra.Firstly,principal component analysis was used to extract the first and second principal components with a cumulative contribution rate greater than 99%.According to the maximum value of the weight coefficient in the loading matrix,the characteristic wavelengths of the first and second principal components of 1 396.67 nm and 1 467.38 nm are screened respectively.The principal component analysis was performed on the two sets of characteristic wavelength images,and the first three-dimensional principal components were selected respectively,and a total of 2×3 sets of training sample sets can be obtained.Finally,there were 6 models established to discriminate Northeast/ Non-Northeast rice.Among them,the best performing model was built based on the 1 467.38 nm third principal component image,and its recognition accuracy can reach 99.5%.

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吴静珠,李晓琪,林珑,刘翠玲,刘志,袁玉伟.基于AlexNet卷积神经网络的大米产地高光谱快速判别[J].中国食品学报,2022,22(1):282-288

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  • 收稿日期:2021-01-20
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  • 在线发布日期: 2022-02-11
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