Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400–2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.

Original languageEnglish
Article number123214
JournalSpectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
Publication statusPublished - Dec 15 2023


  • Chili pepper
  • Convolutional neural network
  • Near-infrared spectroscopy
  • Pesticide residue

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Spectroscopy


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