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傅里叶中红外光谱结合稀疏表示分类方法鉴别小麦赤霉病感染等级
引用本文:梁琨,张夏夏,丁静,徐剑宏,韩东燊,沈明霞.傅里叶中红外光谱结合稀疏表示分类方法鉴别小麦赤霉病感染等级[J].光谱学与光谱分析,2019,39(10):3251-3255.
作者姓名:梁琨  张夏夏  丁静  徐剑宏  韩东燊  沈明霞
作者单位:南京农业大学工学院,江苏 南京 210031;江苏省现代设施农业技术与装备工程实验室,江苏 南京 210031;江苏省食品质量安全重点实验室—省部共建国家重点实验室培育基地/江苏省农业科学院农产品质量安全与营养研究所,江苏 南京,210014
基金项目:国家自然科学青年基金项目(31401610),中央高校基本科研业务费专项资金项目(KJQN201557),江苏省自主创新项目基金项目(CX(17)1003),南京农业大学工学院优秀青年人才科技基金项目(YQ201603)资助
摘    要:旨在探索感染不同等级赤霉病的小麦中主要成分含量变化引起的傅里叶中红外光谱信息响应,并结合模式识别方法实现基于傅里叶变换中红外光谱的小麦赤霉病等级无损检测。以感染不同等级赤霉病小麦为研究对象,在4 000~400 cm-1波数范围内采集95个小麦样本的傅里叶中红外光谱数据,利用载荷系数法(XLW)与随机森林算法(RF)分析选取小麦样本傅里叶中红外光谱中的敏感波长,利用稀疏表示分类(SRC)算法建模识别小麦感染赤霉病等级。结果表明:XLW算法和RF算法选择的特征波长作为定性分析模型的输入时模型鉴别准确率与全波段光谱数据作输入时均达90%以上,特征波长提取算法可以有效简化模型并提高效率。RF-SRC模型鉴别效果最好,建模集鉴别准确率达97%,测试集鉴别准确率达96%。小麦感染赤霉病等级的不同会引起小麦中水分、淀粉、纤维素、可溶性氮素、蛋白质、脂肪等物质含量的变化,采用RF算法选择的特征波长均反映了这些物质所对应的傅里叶中红外光谱透射光谱特征的差异,结合SRC模型进行小麦赤霉病等级鉴别可达到最好的鉴别效果。因此,利用傅里叶中红外光谱技术结合模式识别方法对小麦赤霉病等级鉴别是可行的,解释了傅里叶中红外光谱技术检测小麦赤霉病等级的机理。

关 键 词:傅里叶中红外光谱  小麦  赤霉病  稀疏表示分类
收稿时间:2018-08-18

Discrimination of Wheat Scab Infection Level by Fourier Mid-Infrared Technology Combined with Sparse Representation Based Classification Method
LIANG Kun,ZHANG Xia-xia,DING Jing,XU Jian-hong,HAN Dong-shen,SHEN Ming-xia.Discrimination of Wheat Scab Infection Level by Fourier Mid-Infrared Technology Combined with Sparse Representation Based Classification Method[J].Spectroscopy and Spectral Analysis,2019,39(10):3251-3255.
Authors:LIANG Kun  ZHANG Xia-xia  DING Jing  XU Jian-hong  HAN Dong-shen  SHEN Ming-xia
Institution:1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China 2. Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology and Equipment, Nanjing 210031, China 3. Jiangsu Key Laboratory for Food Quality and Safety-State Key Laboratory Cultivation Base Ministry of Science and Technology/Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
Abstract:This paper aims to explore the response of Fourier transform mid-infrared (FT-MIR) spectra to the changes of the main components in wheat scab with infected different grades and to realize a non-destructive detection of grades of wheat scab based on FT-MIR spectroscopy combined with Sparse Representation based Classification algorithms. The FT-MIR spectra of 95 wheat samples infected with different grades of wheat scab samples were collected in 4 000~400 cm-1. The sensitive wavelengths in the FT-MIR spectra of wheat samples were selected by X-loading Weights and Random Forest algorithms, and Sparse Representation based Classification algorithms were used to build models to predict grades of wheat scab. The results showed that the characteristic wavelengths selected by XLW algorithm and RF algorithm achieved an accuracy of more than 90% for each qualitative analysis model, thus, the characteristic wavelength extraction algorithms could effectively simplify the model and improve efficiency. RF-SRC model had the best results, because the accuracy of the modeling set was 97% and the accuracy of the test data set was 96%. Being infected different grade wheat scab could cause the change of the content of water, starch, cellulose, soluble nitrogen , protein and fat in wheat samples. The characteristic wavelength selected by the RF algorithm could reflect the difference of the spectral characteristics of the FT-MIR spectra of these materials, so the grades discrimination of wheat scab by the RF-SRC model can achieve the best effect. Therefore, it is feasible to distinguish the grades of FHB in Wheat by using FT-MIR spectroscopy and pattern recognition method. This paper explained the mechanism of measuring the grades of FHB in Wheat by FT-MIR.
Keywords:Fourier transform mid-infrared spectra  Wheat  Fusarium head blight  Sparse representation based classification  
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