| 基于近红外光谱的山核桃的品种鉴别 |
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| 引用本文： | 黄宇,项建凯,汤文涛,商玉乾,毕海文,关世昊,赵超.基于近红外光谱的山核桃的品种鉴别[J].农业工程,2022,12(12):56-60. |
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| 作者姓名： | 黄宇 项建凯 汤文涛 商玉乾 毕海文 关世昊 赵超 |
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| 作者单位： | 浙江农林大学光机电工程学院,浙江 杭州 310000 |
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| 基金项目： | 国家级大学生创新创业训练计划项目（202210341043） |
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| 摘 要： | 为实现对山核桃品种的快速鉴别,采集浙江临安山核桃、安徽宁国山核桃、美国山核桃和四川核桃共4种100个核桃样品的近红外光谱,对光谱数据进行了标准正态变量变换（standard normal variate transformation,SNV）预处理后,采用主成分分析（principal component analysis,PCA）法实现了光谱的差异可视化,基本可实现4种山核桃的鉴别。为提高模型准确率,采用主成分分析降维后数据结合线性判别（PCA-LDA）的分类方法,该法对4种山核桃品种鉴别的校正集和验证集的分类准确度都达到了100%。结果表明,近红外光谱技术可实现对山核桃品种的快速鉴别。
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| 关 键 词： | 山核桃 近红外光谱 品种鉴别 主成分分析 线性判别 |
| 收稿时间： | 2022/8/1 0:00:00 |
| 修稿时间： | 2022/10/14 0:00:00 |
Varieties identification of the pecan (Carya cathayensis Sarg.) using near infrared spectroscopy |
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| Huang Yu,Xiang jiankai,Tang Wentao,Shang Yuqian,Bi Haiwen,Guan Shihao and Zhao Chao.Varieties identification of the pecan (Carya cathayensis Sarg.) using near infrared spectroscopy[J].Agricultural Engineering,2022,12(12):56-60. |
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| Authors: | Huang Yu Xiang jiankai Tang Wentao Shang Yuqian Bi Haiwen Guan Shihao and Zhao Chao |
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| Affiliation: | College of Optical,Mechanical and Electrical Engineering,Zhejiang A & F University,Hangzhou Zhejiang 310000,China |
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| Abstract: | To realize the varieties identification of pecan, the near infrared spectroscopy of 100 samples from four kinds of pecans, including Zhejiang Lin"an pecan, Anhui Ningguo pecan, American pecan and Sichuan pecan, were collected. In order to achieve the visualization of spectral differences, principal component analysis (PCA) was carried out on the experimental data. It was found that the spectral principal component analysis after SNV processing could basically identify four different kinds of pecans. In order to improve the accuracy of the model, linear discriminant analysis after principal component analysis (PCA-LDA) was applied to the near infrared spectroscopy. It was found that the varieties identification accuracy of the correction set and verification set reached 100%. The results showed that near infrared spectroscopy could effectively identify pecan varieties. |
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| Keywords: | Pecan Near infrared spectroscopy Variety identification Principal component analysis Linear discriminant analysis |
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