基于近红外显微成像的豆粕和抗生素菌渣鉴别分析
发布时间：2018-07-21 19:06
【摘要】：以豆粕和3种抗生素菌渣为研究对象,通过傅里叶变换近红外显微成像系统采集样品近红外显微图像;对采集到的近红外显微图像进行光谱重构,并对所有样品光谱进行预处理,利用Duplex算法分别从不同的样品预处理光谱中筛选具有代表性的光谱建立豆粕和抗生素菌渣的特征光谱库。使用偏最小二乘判别分析(PLS-DA)与支持向量机判别分析(SVM-DA)结合不同的光谱预处理方法,构建豆粕与不同种类抗生素菌渣的近红外显微成像定性判别模型。结果表明:构建的2种模型均能有效对试验中所用豆粕和抗生素菌渣样品进行鉴别分析,正确率均在99.4%以上。进一步比较研究发现,一阶导数+SNV的预处理方式优于无预处理、一阶导数、二阶导数;SVM-DA的模型效果优于PLS-DA,SVM-DA中特征提取方法 PLS优于PCA。
[Abstract]:Taking soybean meal and three kinds of antibiotic bacteria dregs as research objects, the near infrared microscopic images of samples were collected by Fourier transform near infrared microscopic imaging system, and the acquired near infrared microscopic images were reconstructed by spectral analysis. All the sample spectra were pretreated and the characteristic spectrum library of soybean meal and antibiotic residue was established by Duplex algorithm from different sample pretreatment spectra. Using partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) combined with different spectral pretreatment methods, a qualitative model of near-infrared microscopic imaging of soybean meal and antibiotic residue was established. The results showed that the two models could be used to identify and analyze soybean meal and antibiotic bacteria residue effectively, and the accuracy was over 99.4%. Furthermore, it is found that the pretreatment of first derivative SNV is better than that of no pretreatment, and the model effect of first derivative and second derivative SVM-DA is better than that of PLS-DA-SVM-DA.
【作者单位】： 中国农业大学工学院;中国农业科学院农业质量标准与检测技术研究所;
【基金】：中国农业科学院基本科研业务费专项(1610072017001)和中国农业科学院“饲料质量安全检测与评价”创新团队经费项目
【分类号】：O657.3;S816
[Abstract]:Taking soybean meal and three kinds of antibiotic bacteria dregs as research objects, the near infrared microscopic images of samples were collected by Fourier transform near infrared microscopic imaging system, and the acquired near infrared microscopic images were reconstructed by spectral analysis. All the sample spectra were pretreated and the characteristic spectrum library of soybean meal and antibiotic residue was established by Duplex algorithm from different sample pretreatment spectra. Using partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) combined with different spectral pretreatment methods, a qualitative model of near-infrared microscopic imaging of soybean meal and antibiotic residue was established. The results showed that the two models could be used to identify and analyze soybean meal and antibiotic bacteria residue effectively, and the accuracy was over 99.4%. Furthermore, it is found that the pretreatment of first derivative SNV is better than that of no pretreatment, and the model effect of first derivative and second derivative SVM-DA is better than that of PLS-DA-SVM-DA.
【作者单位】： 中国农业大学工学院;中国农业科学院农业质量标准与检测技术研究所;
【基金】：中国农业科学院基本科研业务费专项(1610072017001)和中国农业科学院“饲料质量安全检测与评价”创新团队经费项目
【分类号】：O657.3;S816
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