Colorimetric Sensor Array for White Wine Tasting
by
Soo Chung 1,†, Tu San Park 2,†, Soo Hyun Park 1,3, Joon Yong Kim 1, Seongmin Park 1, Daesik Son 1, Young Min Bae 4,* and Seong In Cho 1,3,*
Soo Chung 1,†, Tu San Park 2,†, Soo Hyun Park 1,3, Joon Yong Kim 1, Seongmin Park 1, Daesik Son 1, Young Min Bae 4,* and Seong In Cho 1,3,*
1
Department of Biosystems & Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Korea
2
Department of Agricultural and Biosystems Engineering, University of Arizona, 1177 E. 4th St., Tucson, AZ 85721, USA
3
Research Institute for Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-921, Korea
4
Korea Electrotechnology Research Institute, Ansan-si, Gyeonggi-do 426-910, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editor: W. Rudolf Seitz
Sensors 2015, 15(8), 18197-18208; https://doi.org/10.3390/s150818197
Received: 17 June 2015 / Revised: 17 July 2015 / Accepted: 22 July 2015 / Published: 24 July 2015
(This article belongs to the Section Chemical Sensors)
A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.
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MDPI and ACS Style
Chung, S.; Park, T.S.; Park, S.H.; Kim, J.Y.; Park, S.; Son, D.; Bae, Y.M.; Cho, S.I. Colorimetric Sensor Array for White Wine Tasting. Sensors 2015, 15, 18197-18208.
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