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

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.

Automated summary

In this study, the adulteration of goat milk powder by urea, melamine, and starch was quantified using near infrared (NIR) spectroscopy and chemometrics. For single adulterants, models were built with good predictive ability. For multiple adulterants, different methods were used to build the models, with PLS2 showing better results. MCR-ALS models were able to detect adulteration with new and unknown substitutes.