基于近红外光谱和支持向量机的小麦籽粒不同品质分类

Wenyi Tan, Laijun Sun, Dan Zhang, Dandan Ye, Wenkai Che
{"title":"基于近红外光谱和支持向量机的小麦籽粒不同品质分类","authors":"Wenyi Tan, Laijun Sun, Dan Zhang, Dandan Ye, Wenkai Che","doi":"10.1109/CCIOT.2016.7868317","DOIUrl":null,"url":null,"abstract":"For the purpose of rapid, simple and accurate identification of quality of wheat grains, this study proposed a recognition method which is an integration of near infrared spectroscopy and support vector machine (SVM). The spectral data of wheat samples were analyzed in order to eliminate abnormal data, and then Mahalanobis distance method was used to identify abnormal samples. After deleting those abnormal samples, principal component analysis was done to prove the feasibility of classifying wheat by near infrared technologies. The remaining 111 wheat samples were divided into calibration set and prediction set by sample set partitioning based on joint X-Y distance algorithm, then, the first derivative, second derivative, standard normal variate (SNV) transformation and their combinations were used to preprocess spectra for obtaining the optimal pretreatment method before modeling. Finally, SVM and back propagation neural network classification model were established with the spectral data preprocessed by second derivative plus SNV and first derivative plus SNV, respectively. Prediction results of SVM model showed that the recognition accuracy rate of strong gluten wheat and weak gluten wheat both achieved 100% and the recognition accuracy rate of medium gluten wheat also reached 81.82%, which proved that SVM classification model with the spectra data preprocessed by the second derivative plus SNV achieved the best results and realized rapid and accurate identification and classification of wheat quality.","PeriodicalId":384484,"journal":{"name":"2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Classification of wheat grains in different quality categories by near infrared spectroscopy and support vector machine\",\"authors\":\"Wenyi Tan, Laijun Sun, Dan Zhang, Dandan Ye, Wenkai Che\",\"doi\":\"10.1109/CCIOT.2016.7868317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the purpose of rapid, simple and accurate identification of quality of wheat grains, this study proposed a recognition method which is an integration of near infrared spectroscopy and support vector machine (SVM). The spectral data of wheat samples were analyzed in order to eliminate abnormal data, and then Mahalanobis distance method was used to identify abnormal samples. After deleting those abnormal samples, principal component analysis was done to prove the feasibility of classifying wheat by near infrared technologies. The remaining 111 wheat samples were divided into calibration set and prediction set by sample set partitioning based on joint X-Y distance algorithm, then, the first derivative, second derivative, standard normal variate (SNV) transformation and their combinations were used to preprocess spectra for obtaining the optimal pretreatment method before modeling. Finally, SVM and back propagation neural network classification model were established with the spectral data preprocessed by second derivative plus SNV and first derivative plus SNV, respectively. Prediction results of SVM model showed that the recognition accuracy rate of strong gluten wheat and weak gluten wheat both achieved 100% and the recognition accuracy rate of medium gluten wheat also reached 81.82%, which proved that SVM classification model with the spectra data preprocessed by the second derivative plus SNV achieved the best results and realized rapid and accurate identification and classification of wheat quality.\",\"PeriodicalId\":384484,\"journal\":{\"name\":\"2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT)\",\"volume\":\"213 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIOT.2016.7868317\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIOT.2016.7868317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

为了快速、简便、准确地识别小麦籽粒品质,本研究提出了一种近红外光谱与支持向量机(SVM)相结合的小麦籽粒品质识别方法。对小麦样品的光谱数据进行分析,剔除异常数据,然后利用马氏距离法对异常样本进行识别。在剔除异常样本后,进行主成分分析,验证近红外技术对小麦进行分类的可行性。利用联合X-Y距离算法将剩余的111份小麦样本划分为校准集和预测集,然后利用一阶导数、二阶导数、标准正态变量(SNV)变换及其组合对光谱进行预处理,得到最优的预处理方法,再进行建模。最后,分别对光谱数据进行二阶导数加SNV和一阶导数加SNV预处理,建立支持向量机和反向传播神经网络分类模型。SVM模型的预测结果表明,强筋小麦和弱筋小麦的识别准确率均达到100%,中筋小麦的识别准确率也达到81.82%,证明采用二阶导数加SNV预处理的光谱数据的SVM分类模型效果最好,实现了小麦品质的快速准确识别和分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of wheat grains in different quality categories by near infrared spectroscopy and support vector machine
For the purpose of rapid, simple and accurate identification of quality of wheat grains, this study proposed a recognition method which is an integration of near infrared spectroscopy and support vector machine (SVM). The spectral data of wheat samples were analyzed in order to eliminate abnormal data, and then Mahalanobis distance method was used to identify abnormal samples. After deleting those abnormal samples, principal component analysis was done to prove the feasibility of classifying wheat by near infrared technologies. The remaining 111 wheat samples were divided into calibration set and prediction set by sample set partitioning based on joint X-Y distance algorithm, then, the first derivative, second derivative, standard normal variate (SNV) transformation and their combinations were used to preprocess spectra for obtaining the optimal pretreatment method before modeling. Finally, SVM and back propagation neural network classification model were established with the spectral data preprocessed by second derivative plus SNV and first derivative plus SNV, respectively. Prediction results of SVM model showed that the recognition accuracy rate of strong gluten wheat and weak gluten wheat both achieved 100% and the recognition accuracy rate of medium gluten wheat also reached 81.82%, which proved that SVM classification model with the spectra data preprocessed by the second derivative plus SNV achieved the best results and realized rapid and accurate identification and classification of wheat quality.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信