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当前位置: 首页 > 文章 > 基于高光谱图像技术的配合饲料主要营养成分的检测方法 华中农业大学学报 2017,36 (2) 123-129
Position: Home > Articles > Inspection methods of feed main nutritional components by NIRS and hyperspectral imaging Journal of Huazhong Agricultural University 2017,36 (2) 123-129

基于高光谱图像技术的配合饲料主要营养成分的检测方法

作  者：
付苗苗;刘梅英;牛智有
单  位：
华中农业大学工学院
关键词：
配合饲料;营养成分;高光谱图像技术;快速检测;偏最小二乘法
摘  要：
收集403个配合饲料样本,利用高光谱成像仪对样本进行图像采集,获取配合饲料样本的可见/近红外光谱信息。采用光谱杠杆值和学生残差法剔除异常样本,利用CG法、SPXY法和K-S法按3∶1的比例进行样本集划分,采用均值中心化、标准化、一阶导数、二阶导数、正交信号校正、多元散射校正和标准正态变量变换、去趋势变换,以及其组合方法对光谱进行预处理;采用相关系数法获取特征波段,建立基于高光谱图像技术的配合饲料中粗蛋白、粗灰分、水分、总磷、钙含量的偏最小二乘法(PLS)定量分析模型。通过验证,粗蛋白验证集决定系数R~2V为0.777 8,均方根误差RMSEP为2.6155%,相对分析误差RPDV为2.114 3;粗灰分验证集R~2V为0.775 8,RMSEP为1.0611%,RPDV为2.120 4;水分验证集R~2V为0.631 4,RMSEP为1.6003%,RPDV为1.937 1,总磷验证集R~2V、RMSEP、RPDV分别为0.467 2、0.1916%、1.357 0;钙验证集R~2V仅为0.440 6,RMSEP为0.1755%,RPDV,为1.310 5。结果表明,所建立的粗蛋白、粗灰分最优定量分析模型预测性能较好,水分最优定量分析模型预测精度不够理想,总磷和钙定量分析模型的预测性能很差。
译  名：
Inspection methods of feed main nutritional components by NIRS and hyperspectral imaging
作  者：
FU Miaomiao;LIU Meiying;NIU Zhiyou;College of Engineering,Huazhong Agricultural University;
关键词：
compound feed;;nutrition components;;hyperspectral imaging technology;;rapid detection;;partial least squares
摘  要：
403samples of compound feed were collected to study the rapid detection methods of hyperspectral imaging used to detect the nutrition components of the compound feed.Visible/infrared reflectance spectroscopy information of samples was collected by hyperspectral imager and leveragestudents residuals were used to eliminate outliers.Sample set was divided by the method of CG,SPXY and K-S according to the proportion of 3∶1.Combined with different spectral pretreatment methods of MC,AS,FD,SD,OSC,MSC,SNV,Detrend and their combinations,the optimal optical wave length was selected by correlation index.Partial least squares(PLS)stoichiometric methods were used to establish the quantitative analysis model of crude protein,crude ash,moisture,total phosphorus,calcium content in compound feed based on hyperspectral image technology.Through validation,the validation set decision coefficient R~2 Vof crude protein,root mean square error RMSEP,and relative analysis error RPDVwas0.777 8,2.6155%,and 2.114 3,respectively.When the R~2 Vof crude ash was 0.775 8,RMSEP and RPDV was 1.0611% and 2.120 4.When the R~2 Vof water was 0.631 4,RMSEP and RPDVwas 1.6003% and1.937 1.When the R~2 Vof total phosphorus was 0.467 2,RMSEP and RPDVwas 0.1916% and 1.357 0.When the R~2 Vof calcium was 0.440 6,RMSEP and RPDVwas 0.1755% and 1.310 5.Comparing those models,the effect of the optimal model of crude protein and crude ash established by the hyperspectral image technology was found to estimate performance better.Both of them can be used in the actual quantitative analysis.The quantitative analysis model of water prediction accuracy is still not ideal and needs to be further optimized.The quantitative analysis model of calcium and total phosphorus prediction ability is poor and cannot be used for quantitative analysis.

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