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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Yuying Jiang,
  • Xixi Wen,
  • Hongyi Ge,
  • Hao Chen,
  • Mengdie Jiang,
  • Heng Wang,
  • Shilei Wei,
  • Jiahui Wang

摘要

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.