Work

Short-Wavelength Infrared Hyperspectral Imaging and Spectral Unmixing Techniques for Detection and Distribution of Pesticide Residues on Edible Perilla Leaves

Year: 2025

Type: article

Abstract: Pesticide residue analysis of agricultural produce is vital because of associated health concerns, highlighting the need for effective non-destructive techniques. This study introduces a method that combines short-wavelength infrared hyperspectral imaging with spectral unmixing to detect chlorfenapyr and azoxystrobin residues on perilla leaves. Sixty-six leaves were treated with pesticides at concentrations between 0 and 0.06%. The study utilized multicurve resolution-alternating least squares (MCR-ALS), a spectral unmixing method, to identify and visualize the distribution of pesticide residues. This technique achieved lack-of-fit values of 1.03% and 1.78%, with an explained variance of 99% for both pesticides. Furthermore, a quantitative model was developed that integrates insights from MCR-ALS with Gaussian process regression to estimate chlorfenapyr residue concentrations, resulting in a root mean square error of double cross-validation (RMSEV) of 0.0012% and a double cross-validation coefficient of determination (R2v) of 0.99. Compared to other chemometric approaches, such as partial least squares regression and support vector regression, the proposed integrated method decreased RMSEV by 67.57% and improved R2v by 2.06%. The combination of hyperspectral imaging with spectral unmixing offers advancements in the real-time monitoring of agricultural product safety, supporting the delivery of high-quality fresh vegetables to consumers.

Source: Foods

DOI: 10.3390/foods14162864