Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra
- 20 September 2022
- journal article
- research article
- Published by Elsevier in Food Chemistry
- Vol. 403, 134321
Abstract
No abstract availableKeywords
This publication has 36 references indexed in Scilit:
- The distance correlation-test of independence in high dimensionJournal of Multivariate Analysis, 2013
- Estimation of the age of a weathered mixture of volatile organic compoundsAnalytica Chimica Acta, 2011
- Optimization of a Method for the Extraction and Quantification of Carotenoids and Chlorophylls during Ripening in Grape Berries (Vitis vinifera cv. Merlot)Journal of Agricultural and Food Chemistry, 2010
- Usefulness of Fluorescence Excitation−Emission Matrices in Combination with PARAFAC, as Fingerprints of Red WinesJournal of Agricultural and Food Chemistry, 2009
- Front face fluorescence spectroscopy and visible spectroscopy coupled with chemometrics have the potential to characterise ripening of Cabernet Franc grapesAnalytica Chimica Acta, 2008
- High Throughput Analysis of Red Wine and Grape PhenolicsAdaptation and Validation of Methyl Cellulose Precipitable Tannin Assay and Modified Somers Color Assay to a Rapid 96 Well Plate FormatJournal of Agricultural and Food Chemistry, 2007
- Influence of Plant Water Status on the Production of C13-Norisoprenoid Precursors in Vitis vinifera L. Cv. Cabernet Sauvignon Grape BerriesJournal of Agricultural and Food Chemistry, 2007
- Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolationJournal of Chemometrics, 2006
- Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintagesAustralian Journal of Grape and Wine Research, 2005
- THE FLUORESCENCE OF CAROTENOIDSPhotochemistry and Photobiology, 1967