A portable 3D-printed near-infrared spectrometer to screen maize for deoxynivalenol and zearalenone contamination

Abstract

Mycotoxins are toxic metabolites found in grains and cereals, posing a severe potential risk to human and animal health and significantly disrupting animal production, particularly in industries like pig farming. Current laboratory methods for mycotoxin analysis are expensive and time-intensive, creating a need for faster, more accessible solutions. This study set out to address this challenge by developing a portable spectrometer prototype based on the near-infrared (NIR) region and designing a chemometric model to classify ground maize as either non-compliant (NC) or compliant (C) for zearalenone (ZON) and deoxynivalenol (DON). A total of 259 naturally contaminated maize samples, collected over two years from diverse European regions, were analyzed using both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and the prototype device. Spectral data were preprocessed using the first derivative, and a partial least squares discriminant analysis (PLS-DA) model was developed to classify ZON and DON levels into NC and C categories. Thresholds of 100 {micro}g/kg for ZON and 500 {micro}g/kg for DON were used to define compliance. The PLS-DA model showed good performance for ZON, achieving a classification accuracy of 86.3%. However, for DON classification a rather limited accuracy of 66.75% was achieved. While the DON model could identify NC samples it struggled with C samples. Despite these challenges, the results highlight the potential of a portable NIR spectrometer, combined with straightforward preprocessing and PLS-DA modeling, as a rapid, cost-effective screening tool for detecting mycotoxins in maize. The presented approach could significantly simplify mycotoxin monitoring, offering a practical solution to safeguard public health and enhance agricultural productivity.