Generic models for rapid detection of vanillin and melamine adulterated in infant formulas from diverse brands based on near-infrared hyperspectral imaging

Research has shown that near-infrared hyperspectral imaging (NIR HSI) is an effective rapid-detection tool for milk powder adulteration, but model generality under different adulteration conditions requires further study. Therefore, this study focused on developing generic models for the detection of vanillin and melamine in infant formulas from diverse brands. Three pretreatment algorithms were applied successively to spectrum of each pixel in hyperspectral images. Minimum noise fraction was applied to eliminate interference from brand diversity and extract adulterant information. Partial least squares discriminant analysis (PLSDA) was used to develop a classification model to identify vanillin-rich pixels. The PLSDA model, developed with three optimal wavelengths selected by the successive projections algorithm (SPA), detected vanillin at concentrations as low as 0.01%. Partial least squares regression (PLSR) was applied to establish a quantitative model for melamine. The PLSR model, established with six optimal wavelengths selected by the competitive adaptive reweighting algorithm (CARS), showed excellent predictive capabilities, with a limit of detection of 0.5%. A visual prediction map clearly showed the location of vanillin-rich pixels and melamine content variations spatially. The proposed generic practical method would greatly facilitate the application and promotion of NIR HSI technology in quality inspection for the milk powder market and manufacturers.

Automated summary

Research has shown that near-infrared hyperspectral imaging is effective for detecting milk powder adulteration. This study focused on developing generic models to detect vanillin and melamine in infant formulas from diverse brands. Various pretreatment algorithms and statistical analysis methods were applied to establish the detection models.