Melamine Analysis in Liquid Milk by Simple and Robust Neural Network Based Method

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine’s presence are essential. We propose the use of spectroscopy data – produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular – for melamine detection in complex dairy matrixes. It was found that infrared spectroscopy is an effective tool to detect melamine in liquid milk. The limit of detection (LOD) below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum pre-processing (pre-treatment) technique and a correct multivariate (MDA) algorithm: partial least squares regression (PLS), polynomial PLS (Poly-PLS), or artificial neural network (ANN) – is used for spectrum analysis. The relationship between MIR/NIR spectrum of milk product and melamine content is non-linear. So, non-linear regression methods are needed to correctly predict the triazine-derivative content. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk analysis. The technique can be applied for the automation of milk analysis.