Quantitative detection of binary and ternary adulteration of minced beef meat with pork and duck meat by NIR combined with chemometrics

By Tuo Leng, Feng Li, Luo Xiong, Qian Xiong, Mengting Zhu, Yi Chen. 2020

98 citations recorded by the source corpus.

Abstract This study described a rapid and non-destructive method by near-infrared (NIR) technology (12,500-5400 cm−1) for the detection of pork and duck meat in minced beef. Chemometric techniques were used for adulteration detection and adulterant level prediction. Discriminant Analysis (DA) and Partial Least Squares (PLS) models were optimized by selecting appropriate spectral wavelengths and using different spectral pretreatments. The DA model with selected wavelength and with (none preprocess methods) achieved the best results with classification rates at 100% and 91.5% for binary and ternary system, respectively. The optimal PLS models with full-wavelength for predicting adulterant levels gained correlation coefficient Rp of 95.80% and 95.69%, and the root-mean-square error of prediction (RMSEP) of 7.27 and 9.27 for the binary and tenary samples respectively. The results of this paper allowed that NIR technology is not only suitable for the binary adulteration system of minced beef, but also the ternary system.

Subfields: Analytical Chemistry, Animal Science and Zoology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3906bd165bd52eddf0da7bf70b58203ec4c3ead6