Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models.

ABSTRACT: 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 applic

SUBMITTER: Nogales-Bueno J 

PROVIDER: S-EPMC7912666 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature