title: [Research on prediction chemical composition of beef by near infrared reflectance spectroscopy].
abstract: 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.
authors.affiliation: Institute of Animal Husbandry and Veterinary, Chinese Academy of Agricultural Sciences, Beijing 100193, China. sxm007008@163.com
authors.forename: Xiao-Ming
authors.initials: XM
authors.lastname: Sun
authors.forename: Ling
authors.initials: L
authors.lastname: Lu
authors.forename: Jia-Cheng
authors.initials: JC
authors.lastname: Zhang
authors.forename: Song-Shan
authors.initials: SS
authors.lastname: Zhang
authors.forename: Bao-Zhong
authors.initials: BZ
authors.lastname: Sun
mesh_terms: D000818:Animals; D002417:Cattle; D005223:Fats / Q000032:analysis; D008460:Meat / Q000032:analysis*; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D014867:Water / Q000032:analysis
chemical_list: D005223:Fats; D011506:Proteins; D014867:Water