A miniaturized NIR-based approach for quantifying fat content and cow milk adulteration in goat milk.
Hellen Jainne do Nascimento Pereira, Elainy Virgínia Dos Santos Pereira, José Leonardo Alves Ferreira, Raissa Tavares Estavam Ramalho, David Douglas de Sousa Fernandes, Paulo Henrique Gonçalves Dias Diniz
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Abstract
This study introduces a novel approach for determining the fat content and cow milk adulteration in goat milk using a miniaturized NIR spectrometer coupled with multivariate calibration frameworks based on the Successive Projections Algorithm for variable and interval selection in Multiple Linear Regression (SPA-MLR) and Partial Least Squares (iSPA-PLS). An 11-point Savitzky-Golay smoothing (SGS) demonstrated the best predictive performance among the preprocessing techniques. The SGS/iSPA-PLS model achieved correlation coefficients (rpred) of 0.97 and 0.99, root mean square errors of prediction (RMSEP) of 0.12 g/100 g and 2.15 g/100 g, ratios of performance to deviation (RPD) of 4.32 and 8.96, and relative errors of prediction (REP) of 2.70 % and 8.04 % for the fat content estimation and cow milk adulteration detection, respectively. This methodology addresses key challenges in compositional variability and adulteration, offering a robust tool for advancing goat milk quality control in both research and industrial settings.