Optimization of diffuse reflectance spectroscopy measurements for direct and rapid screening of pesticides: A case study of spinach.

Although diffuse reflectance spectroscopy (DRS) measurements can be collected rapidly and simultaneously, the resulting datasets are imbalanced and redundant due to the highly correlated spectral features collected on relatively few samples. Consequently, modelling these datasets using machine learning (ML) techniques is challenging and necessitates longer training times and more computational resources. Furthermore, models developed with such data are frequently prone to overfitting, resulting in promising but often non-reproducible results. We demonstrate the advantage of using an eigenvector decomposition principal component analysis (PCA) in reducing the dimensionality and data mining of DRS measurements in the short near-infrared region (750-900 nm). A total of 547 DRS measurements consisting of 151 wavelengths were acquired from spinach samples sprayed with two different pesticides and control samples. The measurements were later preprocessed with a Savitzky-Golay filter and multiplicative scatter analysis. After performing PCA on the preprocessed data, two principal components (PCs) that explained 77% of the cumulative variance and maximized the interclass variation were extracted and used as inputs to three ML models namely; artificial neural networks, support vector machine and random forest, to classify the samples. Re-sampling was used to tune the models and avoid overfitting. The performance of the models was compared using raw DRS data, pre-processed (PP) DRS data, and PCs data. The results show that pesticide classification using PCs data requires the least amount of training time (average 2.4 s) for all the models, and achieves 100% classification accuracy. In addition, it was observed that spectral data pre-processing improves accuracy and training time when compared to using raw spectral data. These findings are particularly encouraging since they demonstrate the possibility of developing rapid and accurate classification models for screening pesticide residues in fresh produce based on DRS measurements with minimal computational resources.

Clinical efficacy and plasma concentrations of two formulations of buparvaquone in cattle infected with East Coast fever (Theileria parva infection).

East Coast fever, caused by the protozoan parasite Theileria parva, kills about 600,000 cattle annually in Africa. The hydroxynaphthoquinone compound buparvaquone (BPQ) is curative. Sixteen calves were infected with T. parva. On manifestation of disease symptoms, eight were injected with the original (pioneer) BPQ product and eight with a test product containing BPQ. All 16 calves were cured by one injection of 2.5 mg BPQ/kg bodyweight. The concentration of BPQ in blood plasma was monitored by HPLC. The mean observed C(max) of BPQ was 0.229 and 0.253 microg/mL of plasma, the mean observed time to reach this concentration (T(max)) was 2.62 and 2.12 h and the AUC (area under curve) was 4.785 and 4.156 microg h/mL, respectively, for the pioneer and test product. Considerable variations occurred in the plasma concentration of BPQ within each group. They showed no relationship with either clinical or parasitological parameters following treatment.

Immunization of cattle against East Coast fever using Theileria parva (Marikebuni) and relaxation of tick control in North Rift, Kenya.

A total of 90 animals was immunized against East Coast fever (ECF) using Theileria parva (Marikebuni) stock on three large-scale farms in Kiminini Division, Trans-Nzoia District, North Rift, Kenya. Another 90 cattle served as non-immunized controls. Following immunization the number of cattle with significant indirect fluorescent antibody (IFA) titres increased from 43.9% to 84.4% and 6.7% of the cattle developed clinical ECF reactions. Two months after immunization, the immunized and non-immunized cattle were divided into two groups one of which was dipped every 3 weeks and the other dipped when total full body tick counts reached 100. All the animals were monitored for 51 weeks for incidences of ECF and other tick-borne diseases. Twenty-four cases of ECF were diagnosed among the non-immunized cattle compared to four cases among the immunized cattle; a difference that was significant (P > 0.05). There was no significant difference in the incidences of babesiosis and anaplasmosis between the immunized and non-immunized cattle.