Metodología NIR en la determinación rápida de ácidos grasos en aceites crudos. Área de control de calidad La Fabril S.A.

The present investigation focused on evaluating a new modality with NIR methodology in the determination of fatty acids of some oils involved within the production lines of the FABRIL S.A. industry, which allowed classifying and quantifying the characteristics of crude oils of different nature, in a...

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Detalhes bibliográficos
Autor principal: Almeida Zambrano, Jorge Alfredo (author)
Outros Autores: Macías Moreira, Diego Armando (author)
Formato: masterThesis
Idioma:spa
Publicado em: 2020
Assuntos:
Acesso em linha:http://repositorio.espam.edu.ec/handle/42000/1348
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Descrição
Resumo:The present investigation focused on evaluating a new modality with NIR methodology in the determination of fatty acids of some oils involved within the production lines of the FABRIL S.A. industry, which allowed classifying and quantifying the characteristics of crude oils of different nature, in an easier, faster and non-destructive way. The oils used in the experiment were: extra-virgin olive, canola, raw soy and raw sunflower. Three measurements or scans of 110 were made among the four oils, a total of 330 readings, in the transmittance module and the obtained reference values were measured through gases chromatography. The integration for NIR equipment calibration was performed in the NIRCal’s proprietary calibration wizard, using NIRWare software. After elaborating the matrix, twenty random samples were taken, five from each oil, where the measurement were performed with both methodologies, determining their main fitty acids such as palmitic, stearic, oleic, linoleic and linolenic. In addition, a comparison of dependent samples was performed between the two methodologies, resulting in a general similarity between the two due to the p-values being greater than 0.05, which leads to the conclusion that analyzes can be performed using the NIR methodology in a way more efficient, saving cost and time at an industrial level.