Single- and Multiple-Adulterants Determinations of Goat Milk Powder by NIR Spectroscopy Combined with Chemometric Algorithms

Xin Zhao, Yunpeng Wang, Xin Liu, Hongzhe Jiang, Zhilei Zhao, Xiaoying Niu, Chunhua Li, Bin Pang and Yanlei Li
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Xin Zhao: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Yunpeng Wang: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Xin Liu: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Hongzhe Jiang: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Zhilei Zhao: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Xiaoying Niu: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Chunhua Li: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Bin Pang: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
Yanlei Li: College of Quality and Technical Supervision, Hebei University, Baoding 071002, China

Agriculture, 2022, vol. 12, issue 3, 1-15

Abstract: In this work, we quantified goat milk powder adulteration by adding urea, melamine, and starch individually and simultaneously, with the utilization of near infrared (NIR) spectroscopy coupled with chemometrics. For single-adulterant samples, the successive projections algorithm (SPA) selected three, three, and four optimal wavelengths for urea, melamine, and starch, respectively. Models were built based on partial least squares regression (PLS) and the selected wavelengths, exhibiting good predictive ability with an R p 2 above 0.987 and an RMSEP below 0.403%. For multiple-adulterants samples, PLS2 and multivariate curve resolution alternating least squares (MCR-ALS) were adopted to build the models to quantify the three adulterants simultaneously. The PLS2 results showed adequate precision and results better than those of MCR-ALS. Except for urea, MCR-ALS models presented good predictive results for milk, melamine, and starch concentrations. MCR-ALS allowed detection of adulteration with new and unknown substitutes as well as the development of models without the need for the usage of a large data set.

Keywords: goat milk powder; adulteration; near infrared spectroscopy; partial least squares regression; multivariate curve resolution alternating least squares (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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