Machine-learning-facilitated prediction of heavy metal contamination in distiller's dried grains with solubles

Abstract

Excessive heavy metal contamination often occurs in feed due to natural or anthropogenic activity, leading to poisoning and other health problems in animals. In this study, a visible/near-infrared hyperspectral imaging system (Vis/NIR HIS) was used to reveal the different characteristics of spectral reflectance of Distillers Dried Grains with Solubles (DDGS) doped with various heavy metals and to effectively predict metal concentrations. Two types of sample treatment were used, namely tablet and bulk. Three quantitative analysis models were constructed based on the full wavelength, and through comparison the support vector regression (SVR) model was found to show the best performance. As typical heavy metal contaminants, copper (Cu) and zinc (Zn) were used for modeling and prediction. The prediction set accuracy of the tablet samples doped with Cu and Zn was 94.9% and 86.2%, respectively. In addition, a novel characteristic wavelength selection model based on SVR (SVR-CWS) was proposed to filter characteristic wavelengths, which improved the detection performance. The regression accuracy of the SVR model on the prediction set of tableted samples with different Cu and Zn concentrations was 94.7% and 85.9%, respectively. The accuracy of bulk samples with different Cu and Zn concentrations was 81.3% and 80.3%, respectively, which indicated that the detection method can reduce the pretreatment steps and verify its practicability. The overall results suggested the potential of Vis/NIR-HIS in the detection of feed safety and quality.

Keywords: DDGS; Heavy metals; Hyperspectral imaging; SVR.