Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models
- Others:
- Universidad de Sevilla. Departamento de Nutrición y Bromatología, Toxicología y Medicina Legal
- Universidad de Sevilla. AGR225: Color y Calidad de Alimentos
- Universidad de Sevilla
- Ministerio de Ciencia e Innovación (MICIN). España
- Ministerio de Economía y Competitividad (MINECO). España
- Junta de Andalucía
Description
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.
Abstract
Universidad de Sevilla- becas VPPI-II.2 y VPPIII.4
Abstract
Ministerio de Ciencia e Innovación del gobierno español-Contrato Juan de la Cierva FJC2018 037967 I
Abstract
Ministerio de Economía y Competitividad del gobierno español-AGL2017-84793-C2
Abstract
Consejería de Economía y Conocimiento de la Junta de Andalucía-US-1261752
Additional details
- URL
- https://idus.us.es/handle//11441/104911
- URN
- urn:oai:idus.us.es:11441/104911
- Origin repository
- USE