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作 者:王晓彬[1,2,3,4,5] 黄文倩 王庆艳[2,3,4,5] 李江波 王超鹏[2,3,4,5] 赵春江 WANG Xiao-bin;HUANG Wen-qian;WANG Qing-yan;LI Jiang-bo;WANG Chao-peng;ZHAO Chun-jiang(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China;Beijing Research Center of Intelligent Equipment for Agriculture,Beijing 100097,China;National Research Center of Intelligent Equipment for Agriculture,Beijing 100097,China;Key Laboratory of Agri-Informatics,Ministry of Agriculture,Beijing 100097,China;Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture,Beijing 100097,China)
机构地区:[1]沈阳农业大学信息与电气工程学院,辽宁沈阳110866 [2]北京农业智能装备技术研究中心,北京100097 [3]国家农业智能装备工程技术研究中心,北京100097 [4]农业部农业信息技术重点实验室,北京100097 [5]农业智能装备技术北京市重点实验室,北京100097
出 处:《光谱学与光谱分析》2018年第3期805-812,共8页Spectroscopy and Spectral Analysis
基 金:国家自然科学基金项目(31401283);北京市农林科学院青年科研基金项目(QNJJ201528);北京市科技新星计划(Z161100004916076)资助
摘 要:高光谱成像技术不仅可以获得样品的图像信息,每个像素点还包含了光谱信息,因其信息量丰富的特点已在食品安全检测方面得到了应用。该研究应用近红外高光谱成像技术检测面粉中偶氮甲酰胺。分别采集纯偶氮甲酰胺、纯面粉和面粉中10种不同浓度偶氮甲酰胺混合样品的高光谱图像。通过比较纯偶氮甲酰胺和纯面粉的平均漫发射光谱,找到两者区分度较大的4个吸收波段:1 574.38,2 038.55,2 166.88和2 269.91nm。采用二阶导数对样品图像中的像素点光谱进行预处理,通过光谱角制图、光谱相关角和光谱相关性度量三种光谱相似性分析方法对混合样品中的偶氮甲酰胺像素和面粉像素进行检测。结果表明,预处理后的平均光谱不能有效检测面粉中偶氮甲酰胺;单像素点光谱结合光谱相似性分析实现了混合样品中偶氮甲酰胺像素和面粉像素的分类;分类结果的验证显示了偶氮甲酰胺像素和面粉像素的正确分类。研究结果为利用高光谱技术检测面粉中添加剂提供了方法支持,为食品中掺杂物的检测提供参考。Near infrared hyperspectral imaging technology not only can acquire the image information of the sample,but also contain the spectra information about each pixel.Due to the abundant information that the method provides,it has been applied to detect food safety.This study adopted near infrared hyperspectral technology to detect azodicarbonamide in flour.Hyperspectral images of pure azodicarbonamide,pure flour,and azodicarbonamide-flourmixture samples with different concentrations of azodicarbonamide were collected,by comparing the average diffuse reflectance spectra of pure azodicarbonamideand pure flour,the four absorption bands with high difference were found at 1 574.38,2 038.55,2 166.88 and 2 269.91nm.Second derivative was used for each pixel in the mixture sample ROI.Azodicarbonamide pixels and flour pixels weredetected by three spectral similarity analysis methods:spectral angle mapper,spectral correlation angle,and spectral correlation measure.The results showedthat the average spectra after pretreatment cannot effectively detect azodicarbonamide in flour.Spectral similarity analysis of single pixel spectra can be used to classify azodicarbonamide pixels and flour pixels in mixture samples.Thevalidation of the classification results showed the correct classification of azodicarbonamide pixels and flour pixels.This study could provide a method support for the detection of additives in flour based on near infraredhyperspectral imaging technology,and provide a reference for the detection of adulterants in food.
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