Artículo
Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models
Autor/es | Nogales Bueno, Julio
Rodríguez Pulido, Francisco José Baca Bocanegra, Berta Pérez Marín, Dolores Heredia Mira, Francisco José Garrido Varo, Ana Mª Hernández Hierro, José Miguel |
Departamento | Universidad de Sevilla. Departamento de Nutrición y Bromatología, Toxicología y Medicina Legal |
Fecha de publicación | 2021-01 |
Fecha de depósito | 2021-02-12 |
Publicado en |
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Resumen | Developing chemometric models from near-infrared (NIR) spectra requires the use of a
representative calibration set of the entire population. Therefore, generally, the calibration procedure
requires a large number of ... Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results. |
Agencias financiadoras | Universidad de Sevilla Ministerio de Ciencia e Innovación (MICIN). España Ministerio de Economía y Competitividad (MINECO). España Junta de Andalucía |
Identificador del proyecto | beca VPPI-II.2
beca VPPIII.4 FJC2018 037967 I AGL2017-84793-C2 US-1261752 |
Cita | Nogales Bueno, J., Rodríguez Pulido, F.J., Baca Bocanegra, B., Pérez Marín, D., Heredia Mira, F.J., Garrido Varo, A.M. y Hernández Hierro, J.M. (2021). Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models. Foods, 10 (2), 233. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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foods-10-00233-v2.pdf | 2.573Mb | [PDF] | Ver/ | |