Assessment of infant formula quality and composition using Vis-NIR, MIR and Raman process analytical technologies

Abstract

In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytical technologies were investigated for assessment of infant formula quality and compositional parameters namely preheat temperature, storage temperature, storage time, fluorescence of advanced Maillard products and soluble tryptophan (FAST) index, soluble protein, fat and surface free fat (SFF) content. PLS-DA models developed using spectral data with appropriate data pre-treatment and significant variables selected using Martens’ uncertainty test had good accuracy for the discrimination of preheat temperature (92.3–100%) and storage temperature (91.7–100%). The best PLS regression models developed yielded values for the ratio of prediction error to deviation (RPD) of 3.6–6.1, 2.1–2.7, 1.7–2.9, 1.6–2.6 and 2.5–3.0 for storage time, FAST index, soluble protein, fat and SFF content prediction respectively. Vis-NIR, MIR and Raman were demonstrated to be potential PAT tools for process control and quality assurance applications in infant formula and dairy ingredient manufacture.