New approaches in the chemometric analysis of infrared spectra of extra-virgin olive oils
The aim of this paper is to apply new chemometric approaches to obtain quantitative information from near and mid infrared spectra of Andalusian extra-virgin olive oils, using gas chromatography as a classical reference analytical technique. Estimations of the content in saturated, monounsaturated a...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/88564 |
| Acceso en línea: | https://hdl.handle.net/2117/88564 |
| Access Level: | acceso abierto |
| Palabra clave: | Extra-virgin olive oil infrared spectroscopy partial least squares regression cross-validation Classificació AMS::62 Statistics::62H Multivariate analysis Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62Q05 Statistical tables Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | The aim of this paper is to apply new chemometric approaches to obtain quantitative information from near and mid infrared spectra of Andalusian extra-virgin olive oils, using gas chromatography as a classical reference analytical technique. Estimations of the content in saturated, monounsaturated and polyunsaturated fatty acids are given using partial least squares regression from the near and mid infrared data matrices as well as their concatenated matrix. The different estimations are evaluated in terms of goodness of fit (calibration) and prediction (validation), as a function of the number of partial least squares factors in the regression model and the used matrix of data. Furthermore, the nature, systematic or random, of the prediction errors is studied by a decomposition of their mean squared error. Finally, procedures of cross-validation are implemented in order to generalize the previous results. |
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