alexa The Use of Fourier Transform Infrared (FTIR) Spectrosco
ISSN: 2329-6798

Modern Chemistry & Applications
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Research Article

The Use of Fourier Transform Infrared (FTIR) Spectroscopy and Artificial Neural Networks (ANNs) to Assess Wine Quality

Snezana Agatonovic-Kustrin1*, David W. Morton1 and Ahmad Pauzi Md. Yusof2
1School of Pharmacy and Applied Science, La Trobe University, Australia
2Physiology Department, Medical School, Universiti Teknologi Mara, Selangor, Malaysia
Corresponding Author : Snezana Agatonovic-Kustrin
Associate Professor of Pharmacy
School of Pharmacy and Applied Science, Faculty of Science
Technology and Engineering, La Trobe University
Bendigo, Post Box 199, Bendigo 3552, Australia
Tel: +61-354-447-360
Fax: +61-354-447-878
E-mail: [email protected]
Received August 13, 2013; Accepted September 14, 2013; Published September 23, 2013
Citation: Agatonovic-Kustrin S, Morton DW, Yusof APM (2013) The Use of Fourier Transform Infrared (FTIR) Spectroscopy and Artificial Neural Networks (ANNs) to Assess Wine Quality. Mod Chem appl 1:110. doi:10.4172/2329-6798.1000110
Copyright: © 2013 Agatonovic-Kustrin, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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The aim of this study was to develop a simple method to assess wine quality from its Fourier Transform Infrared Spectroscopy (FTIR) spectrum with minimal or no sample preparation. FTIR spectral data of selected wine samples, grape variety, wine barrel type, wine type and production year were correlated with total phenolic content, total and volatile acidity and alcohol content using Artificial Neural Networks (ANNs). A total of 20 (2 whites and 18 reds) different wines used in this study came from three different states across Australia; New South Wales, Victoria and South Australia. FTIR spectroscopy proved to be a promising technique that provides a rapid and accurate method in the quality assessment of wine. A plot of the values predicted by the validated ANN models showed excellent correlation with the experimentally measured values for acetic acid concentration, alcohol content, total phenols, and total acidity (r=0.898- 0.942).


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