Short-wavelength infrared hyperspectral imaging and spectral unmixing techniques for detection and distribution of pesticide residues on edible perilla leaves

Authors: SEMYALO, DENNIS - Chungnam National University; JOSH, RAHUL - Chungnam National University; KIM, YENA - Chungnam National University; OMIA, EMMANUEL - Chungnam National University; ALAL, LORNA - Chungnam National University; Kim, Moon; Baek, Insuck; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Foods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/2/2025
Publication Date: 8/18/2025
Citation: Semyalo, D., Josh, R., Kim, Y., Omia, E., Alal, L.B., Kim, M.S., Baek, I., Cho, B. 2025. Short-wavelength infrared hyperspectral imaging and spectral unmixing techniques for detection and distribution of pesticide residues on edible perilla leaves. Foods. 14(16), 2864. https://doi.org/10.3390/foods14162864.
DOI: https://doi.org/10.3390/foods14162864

Interpretive Summary: Chemical pesticides are commonly used in leafy green cultivation to minimize the crop loss caused by pests and diseases. It is important to detect pesticide residues on leafy greens to mitigate health risks. This study evaluated an infrared imaging technique to non-destructively detect two types of pesticides, chlorfenapyr and azoxystrobin, on perilla leaves. The researchers used a special camera that captures images at multiple wavelengths in the infrared region. A total of 66 leaves were tested, each treated with different amounts of pesticides ranging from 0 to 0.06 percent. To determine whether the pesticide was present on the leaf samples, a numerical analysis method called multivariate curve resolution-alternating least squares was applied to analyze the infrared spectral image data. The method produced highly accurate results, with a detection accuracy rate of 99 percent. In addition, this study developed a quantitative model designed to measure the amount of chemical residues. It showed excellent performance, with a very low error rate of 0.0012 percent. The techniques presented in this investigation enable rapid and accurate assessments of pesticide residues on leafy greens. Leafy green producers and processors can use the techniques to ensure fresh leafy greens are free of pesticides and safe for public consumption.

Technical 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.