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

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2023-12, Vol.303, p.123214, Article 123214
• Main Authors: Ong, Pauline, Yeh, Ching-Wen, Tsai, I-Lin, Lee, Wei-Ju, Wang, Yu-Jen, Chuang, Yung-Kun
• Format: Article
• Language: English
• Subjects: Chili pepper; Convolutional neural network; Near-infrared spectroscopy; Pesticide residue
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2023.123214

[Display omitted] •Use near-infrared (NIR) spectroscopy to detect pesticide residues in chili pepper.•Neonicotinoid pesticides of acetamiprid and imidacloprid are considered.•NIR spectroscopy coupled with convolutional neural network (CNN) for prediction.•CNN outperforms commonly used partial least squares regression model. 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.