Detection of several common adulterants in raw milk by MID-infrared spectroscopy and one-class and multi-class multivariate strategies.

Abstract

A sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074g.L-1), hydrogen peroxide (21.0g.L-1), bicarbonate (4.0g.L-1), carbonate (4.0g.L-1), chloride (5.0g.L-1), citrate (6.5g.L-1), hydroxide (4.0g.L-1), hypochlorite (0.2g.L-1), starch (5.0g.L-1), sucrose (5.4g.L-1) and water (150g.L-1). In the first step, a one-class model was developed with unadulterated samples, providing 93.1% sensitivity. Four poorly assigned adulterants were discarded for the following step (multi-class modelling). Then, in the second step, a multi-class model, which considered unadulterated and formaldehyde-, hydrogen peroxide-, citrate-, hydroxide- and starch-adulterated samples was implemented, providing 82% correct classifications, 17% inconclusive classifications and 1% misclassifications. The proposed strategy was considered efficient as a screening approach since it would reduce the number of samples subjected to confirmatory analysis, time, costs and errors.