[Discriminant analysis of near infrared diffuse reflectance spectra to detect adulteration of non-ruminant meat and bone meal].
In order to study the feasibility of using near infrared (NIR) diffuse reflectance spectroscopy to discriminate adultera tion of non-ruminant meat and bone meal (MBM) with ruminant MBM, a total of 39 MBM samples made up of 15 from pig, 15 from poultry, 5 from cattle and 4 from sheep produced in different areas in China were chosen. The MBM samples were ground with 0. 5 mm sieve. 252 specimens were prepared by non-ruminant MBM deliberately adulterated with different proportion of ruminant MBM. The specimens were scanned by FOSS NIRSystem 6500. A calibration set of 180 specimens and an independent validation set of 72 specimens were randomly selected by the WINISI software. Discriminant analysis model was developed by partial least squares (PLS) on the calibration set and validated with independent validation set. The best discriminant model was obtained using standard normal variate and detrend (SNVD) and second derivative for spectrum pretreatment; this model had a coefficient of determination (R2(CV)) of 0.83 and a standard error of cross-validation (SECV) of 0. 147 1. For the independent validation set, the correct classification rate is 90%. There were a false negative specimen (0.5%) and two uncertain specimens (1%, 1.5%) in validation set. Results showed that it is feasible to use NIR diffuse reflectance spectroscopy to discriminate adulteration of non-ruminant MBM with ruminant MBM, but for specimens adulterated with ruminant MBM at less than 2%, the accuracy of calibration model needs to be improved. NIR was a rapid and non-destructive approach to discriminating adulteration of non-ruminant MBM with ruminant MIBM.
China Agricultural University, Key Laboratory of Modern Precision Agriculture System Integration Ministry of Education, Beijing 100083, China.
Qiong-Fei
QF
Li
Zeng-Ling
ZL
Yang
Lu-Jia
LJ
Han
D000818:Animals; D001688:Biological Products / Q000032:analysis; D002138:Calibration; D002417:Cattle; D016002:Discriminant Analysis; D008460:Meat / Q000032:analysis*; D008903:Minerals / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
D001688:Biological Products; D008903:Minerals; C027762:bone meal