Evaluation of convolutional neural network for non-destructive detection of imidacloprid and acetamiprid residues in chili pepper (Capsicum frutescens L.) based on visible near-infrared spectroscopy

Pauline Ong, Ching Wen Yeh, I. Lin Tsai, Wei Ju Lee, Yu Jen Wang, Yung Kun Chuang

研究成果: 雜誌貢獻文章同行評審

摘要

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.
原文英語
文章編號123214
期刊Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
303
DOIs
出版狀態已發佈 - 12月 15 2023

ASJC Scopus subject areas

  • 分析化學
  • 原子與分子物理與光學
  • 儀器
  • 光譜

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