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.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 61975053 and 62271191), the Natural Science Foundation of Henan Province (No. 222300420040), the Program for Science and Technology Innovation Talents in Universities of Henan Province (Nos. 22HASTIT017 and 23HASTIT024), the Open Fund Project of Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology (KFJJ2025006). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Jiang, Y., Wen, X., Ge, H. et al. Quantitative analysis of genetically modified maize based on terahertz spectroscopy and DeepSpectra models. npj Sci Food (2026). https://doi.org/10.1038/s41538-026-00866-9
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DOI: https://doi.org/10.1038/s41538-026-00866-9
