Rapid detection of milk adulteration using Raman spectroscopy and statistical modelling

Abstract

Food adulteration has become a concern for consumers and food safety authorities. Milk is a commune adulterated food product, like melamine adulteration, which resulted in devastating effects, especially on young children. Because of the current fast paste economy, it is essential to develop equally fast analysis methods to ensure reliable and sensitive results quickly with little to no sample preparation. For that purpose, a Raman method was developed and Partial least squares regression (PLS) was applied in order to develop a model for adulterated goat milk detection. Minitab 17 software was used for the statistical modeling of data. Validation matrices were constructed using unadulterated goat milk and goat milk adulterated with cow milk in different proportions (0-50%). The prediction model had a correlation coefficient of 99.8 %.