Rapid Screening of Pesticide Residues Using Vibrational Spectroscopy, Theoretical Calculations, and Machine Learning: a Case Study on Chlorothalonil
Abstract
Chlorothalonil, a fungicide commonly used in agriculture to combat fungal infections, is a
major concern due to its persistent residues in the food supply, especially in countries like
Kenya where vegetable consumption is high. This thesis develops a rapid, non-destructive
methodology to detect chlorothalonil residues on vegetables, integrating vibrational spectroscopy
(Raman and Near-Infrared Spectroscopy - NIR), computational spectroscopy
(Density Functional Theory - DFT), and machine learning tools. The study focuses on
four key vegetables: bell peppers (Capsicum annuum), kale (Brassica oleracea var. sabellica),
tomatoes (Solanum lycopersicum), and pigweed (Amaranthus spp.) due to their
widespread consumption and vulnerability to fungal diseases, for which chlorothalonil is
widely applied. Through Raman and NIR spectroscopy, chlorothalonil residues were identified
by exploiting their unique molecular vibrational fingerprints. These were further
enhanced using DFT simulations, incorporating anharmonic corrections through Generalized
Vibrational Perturbation Theory (GVPT2), along with the B3LYP and B2PLYP
functionals paired with advanced basis sets like N07D and SNSD. This computational
approach significantly improved the accuracy of vibrational spectra simulations, particularly
in resolving overlapping spectral bands often encountered in complex agricultural
matrices. Two-Dimensional Correlation Spectroscopy (2D-COS) was employed alongside
chemometric techniques such as Principal Component Analysis (PCA) and Support Vector
Machines (SVM) to improve the interpretability and classification of spectral data.
The combined Raman and NIR methodologies demonstrated remarkable efficiency, with
spectral acquisition completed in just 30 seconds per sample and the developed machine
learning pipeline delivering results within 10 seconds. These rapid, non-destructive
processes, coupled with high accuracy, underscore the potential of integrating advanced
spectroscopy techniques with computational and machine learning methods for real-time
pesticide residue detection in agricultural matrices.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
- Faculty of Arts [979]
The following license files are associated with this item: