Work

Quantitative analysis of genetically modified maize based on terahertz spectroscopy and DeepSpectra models

Year: 2026

Type: article

Abstract: With the widespread cultivation of genetically modified (GM) maize, accurate detection of GM components has become critical. This study developed three end-to-end DeepSpectra models (V1-V3) to classify GA21 maize at six GM concentrations (blank, levels 1-5). Spectral data were acquired using a terahertz time-domain spectroscopy system. Outliers were removed using an isolation forest, and the spectral data were preprocessed with Savitzky-Golay smoothing, standard normal variate transformation, baseline correction, first derivative (FD), and second derivative. Three comparison models were constructed, a support vector machine (SPA-GS-SVM) and a random forest (SPA-GS-RF) based on successive projection algorithms (SPA) and grid search (GS), as well as a one-dimensional convolutional neural network. Experimental results showed that the DeepSpectraV2 model with FD preprocessing achieved the best classification accuracy of 96.56%, outperforming the best comparative models by 1.52 to 15.52% across other preprocessing methods. This study presents a novel approach for the rapid non-destructive detection of GM crops.

Source: npj Science of Food

DOI: 10.1038/s41538-026-00866-9