A rapid and reliable method for the determination of 10 pesticide residues in coconut (Cocos nucifera L.) tree trunks after endotherapy treatments has been established. A modified QuEChERS (quick, easy, cheap, effective, rugged and safe) method, using an homogeneous sample slurry and acetate buffer, followed by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was developed and validated. Under the best extraction conditions, the average recoveries for all pesticides spiked at 40, 80 and 200 μg kg⁻¹ ranged from 70 to 93%, with RSD <10%. Intermediate precision expressed as RSD, ranged between 3 and 6% for all compounds. Calibration curves showed a wide linear range between 10.0 and 1000.0 μg kg⁻¹ for all compounds studied. Limit of quantification was established as 40.0 μg kg⁻¹. The developed procedure was employed in the analysis of real coconut tree trunk samples obtained 45 h after pesticides application using endotherapy treatment. Concentrations of pesticides were between 44.7 ± 5 and 938.3 ± 20 μg kg⁻¹. These results prove the translocation of pesticides in different heights, in the coconut tree trunk, from the application point. Imidacloprid presented the highest acropetal translocation and was found near the leaves at 61 ± 6 μg kg⁻¹.

This paper describes different E-Senses systems, such as Electronic Nose, Electronic Tongue, and Electronic Eyes, which were used to build several machine learning models and assess their performance in classifying a variety of Colombian herbal tea brands such as Albahaca, Frutos Verdes, Jaibel, Toronjil, and Toute. To do this, a set of Colombian herbal tea samples were previously acquired from the instruments and processed through multivariate data analysis techniques (principal component analysis and linear discriminant analysis) to feed the support vector machine, K-nearest neighbors, decision trees, naive Bayes, and random forests algorithms. The results of the E-Senses were validated using HS-SPME-GC-MS analysis. The best machine learning models from the different classification methods reached a 100% success rate in classifying the samples. The proposal of this study was to enhance the classification of Colombian herbal teas using three sensory perception systems. This was achieved by consolidating the data obtained from the collected samples.

A method was developed using matrix solid-phase dispersion, together with liquid chromatography with ultraviolet diode array detector for determination of carbofuran, difenoconazole, β-cyfluthrin, spirodiclofen and thiophanate-methyl in stem of coconut palm. The best results were obtained using 2.0 g of stem, 1.6 g of Florisil as sorbent and cyclohexane:acetone mixture (4:1). The method was validated using stem samples spiked with pesticides at four concentration levels (0.05–2.0 μg/g). Average recoveries ranged from 70 % to 114.3 %, with relative standard deviations between 1.2 % and 19.2 %. Detection and quantification limits were in the ranges 0.02–0.03 and 0.05–0.1 μg/g, respectively.