Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11407
Title: Prediction of vinegar processing parameters with chemometric modelling of spectroscopic data
Authors: Çavdaroğlu, Çağrı
Özen, Banu
Keywords: Vinegar
Prediction
UV-visible spectroscopy
FTIR
Chemometrics
Issue Date: 2021
Publisher: Elsevier
Abstract: Spectroscopic methods have the advantages of being rapid and environmentally friendly and can be used in measurement and control of processing parameters during food production. It was aimed to predict several quality and chemical parameters of vinegar processing from UV-visible and mid-infrared spectroscopic profiles. Two processing lines of both traditional and submerged vinegar production from 2 separate grape varieties (green and red grapes) were monitored. Some of the important markers of the fermentation processes; pH, brix, total acidity, total flavonoid content, total and individual phenolic contents, organic acid, sugar, ethanol concentrations as well as UV-visible and mid-infrared spectra were obtained during both types of vinegar processing and quality and chemical parameters were predicted from spectroscopic data using chemometric methods. Individual UV-visible and mid-infrared spectral profiles along with low level of data fusion were used in building of chemometric prediction models. Accurate, reliable and robust prediction models (R(2)cal and R(2)val >0.9) were obtained for quality parameters mostly with combination of two spectroscopic datasets. Predictive models used for phenolic components were below average except for p-coumaric and syringic acids. Citric and acetic acids were the most accurately estimated ones among organic acids along with ethanol. Close agreements between reference and predicted values were obtained during the monitoring of changes of some quality parameters for vinegar fermentation process through rapid and simultaneous spectroscopic measurements.
URI: https://doi.org/10.1016/j.microc.2021.106886
https://hdl.handle.net/11147/11407
ISSN: 0026-265X
1095-9149
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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