Argentina is a beekeeping country and one of the drawbacks is the exposure of the hives to pollutants. Therefore, the aim of this Thesis is the development of an electroanalytical methods to evaluate the hygienic-sanitary quality of bee products of Buenos Aires province. The following works were performed: ➢ A "lab-made" solid bismuth electrode was designed for cadmium determination in corbicular pollen and raw propolis samples. For this proposed, Square Wave Anodic Stripping Voltammetry was used as electroanalytical technique. ➢ An "in-situ" antimony film electrode was developed for tetracyclines residues determination in honey samples, using Square Wave Cathodic Stripping Voltammetry. ➢ The use of an electrochemically activated glassy carbon electrode and Square Wave Adsorptive Stripping Voltammetry was implemented for the amitraz determination from its final metabolite, 2,4 dimethylaniline, in honey samples. The proposed methods are simple, fast, low cost and may be a good alternative for the determination of these analytes in bee products. Furthermore, the results obtained were satisfactorily validated using recovery study. Certifico que fueron incluidos los cambios y correcciones sugeridas por los jurados
Classification or screening analysis of natural unprocessed teas using simple digital images and a variable selection algorithm is described. The proposed methodology uses color histograms generated on free downloadable software ImageJ 1.44p as a source of analytical information. Two chemometric methods were compared for classification of the resulting images, namely Soft Independent Modeling of Class Analogy (SIMCA), and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors found in a sample set separate from the modeling process. The choice of more informative photometric color attributes (red-greenblue (RGB), hue (H), saturation (S), brightness (B), and grayscale) for screening the tea samples was made during the color modeling because SIMCA failed to give good results. Therefore the data treatment used SPA-LDA, which correctly classified all samples according to their geographical regions, whether from Brazilian, Argentinian or foreign soils.
This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.