Research on Prediction Chemical Composition of Beef by Near Infrared Reflectance Spectroscopy

文献类型： 外文期刊

第一作者： Sun Xiao-ming

作者： Sun Xiao-ming;Lu Ling;Zhang Song-shan;Sun Bao-zhong;Sun Xiao-ming;Zhang Jia-cheng

作者机构：

关键词： Near infrared reflectance;Artificial neural network;Related coefficient;Chemical composition

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2011 年 31 卷 2 期

页码：

收录情况： SCI

摘要： This study established a near infrared reflectance spectroscopy models for exactly predicting the fat, protein and moisture of the ground and mince beef on line. Using our country' SupNIR-1000 near infrared spectrometer, the models were set up by artificial neural network (ANN). Related coefficient of calibration (r(c)) of fat model of mince was 0.971 and related coefficient of prediction (r(p)) was 0.972. The protein' r(c) and RP were 0.952 and 0.949, respectively. The moisture' r(c) and r(p) were 0.938 and 0.927, respectively. Using ground beef established models, the fat' r(c) and r(p) were 0.935 and 0.810;the protein' r(c) and r(p) were 0.954 and 0.868; the moisture' r(c) and r(p) were 0.930 and 0.913, respectively. So near infrared reflectance spectroscopy can better detect the fat, protein and moisture of mince than ground beef. But basically the ground beef model also can be used to quickly predict the chemical composition on line.

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