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dc.contributor.authorNdung’u, Ndegwa C
dc.description.abstractThere is an urgent need for accurate, non-destructive, rapid, and affordable techniques for screening pesticide residues in fresh fruits to assess whether regulatory standards are met by not surpassing the allowed maximum residue limits (MRL). Although conventional methods such as chromatography and mass spectroscopy are accurate, they are expensive, destructive, and require tedious wet lab sample preparations. This study aimed to assess the utility of machine learning techniques for rapid and non-destructive assessment of residues in fruits based on diffuse reflectance spectroscopy (DRS) measurements in the near-infrared region. Towards this goal, tree tomatoes fruits were spiked with Mancozeb and thiocyclam hydrogen oxalate (THO) in varying concentrations, and near-infrared spectra data (900-2500nm) were collected in DRS geometry. Another dataset was collected from the field in the 200 nm to 1050 nm range on control and treated tree tomatoes for 11 consecutive days for qualitative analysis. All the measurements were transformed into absorbance using log10(1/R) before preprocessing using the smoothing, normalization, and multiplicative scatter correction techniques. Principal component analysis of the field data showed distinct clusters for the control and treated fruits in the 800 nm to 850 nm third overtone region with PC1 and PC2 accounting for 82% and 5.9% of the variance, respectively. The combination region (1900-2500 nm) was optimal in discriminating the samples with varying pesticide concentrations for the laboratory data, with PC1 and PC2 accounting for 93% and 3.8% of the variance, respectively. The first four PCs, which explained 98% of the cumulative variation of the data, were extracted from the laboratory data and used as inputs to the support vector machine (SVM), artificial neural networks (ANN), and Random forest (RF) machine learning models. All the developed models had R2 values greater than 92% and RMSEP values of less than 0.06 ppm for Mancozeb models and not more than 0.08 ppm for THO models. Limits of detection and quantification were also determined using a pseudo-univariate approach. The models were tested on a new dataset of samples collected from four local markets. From the results obtained, all predicted values were below the acceptable MRL values (0.5 ppm and 0.3 ppm for Mancozeb and THO, respectively). One-way Tukey ANOVA analysis of the predictions of the market samples showed that ANN and SVR models were more reliable than the RF model. Therefore, it was concluded that the combination of diffuse reflectance spectroscopy with machine learning techniques has potential for rapid, non-destructive, in-situ assessment of pesticide residues in fruits and vegetables.en_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectPesticide Residues in Fruits and Vegetablesen_US
dc.titleRapid Assessment of Pesticide Residues in Fruits and Vegetables Using Machine Learning Assisted Diffuse Reflectance Spectroscopyen_US

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