Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

Autor: Villar A; Surface Chemistry Unit, IK4-Tekniker, Iñaki Goenaga 5, 20600 Eibar, Spain. Electronic address: alberto.villar@tekniker.es., Vadillo J; Surface Chemistry Unit, IK4-Tekniker, Iñaki Goenaga 5, 20600 Eibar, Spain., Santos JI; SGIKER-UPV, University of the Basque Country, Tolosa Hiribidea 7, 20018 Donosti, Spain., Gorritxategi E; Atten2 Advanced Monitoring Technologies, Iñaki Goenaga 5, 20600 Eibar, Spain., Mabe J; Electronics and Comms. Unit, IK4-Tekniker, Iñaki Goenaga 5, 20600 Eibar, Spain., Arnaiz A; Intelligent Information Systems Unit, IK4-Tekniker, Iñaki Goenaga 5, 20600 Eibar, Spain., Fernández LA; Department of Analytical Chemistry, University of the Basque Country, 48080 Bilbao, Spain.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2017 Apr 15; Vol. 221, pp. 100-106. Date of Electronic Publication: 2016 Oct 14.
DOI: 10.1016/j.foodchem.2016.10.045
Abstrakt: Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring.
(Copyright © 2016 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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