A study on wine sensory evaluation by the statistical analysis method

https://doi.org/10.17221/438/2017-CJFSCitation:Gang-Ling H., Bin G., Liang-Liang S., Kai-Xin X. (2020): A study on wine sensory evaluation by the statistical analysis method. Czech J. Food Sci., 38: 1-10.
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In this paper, we construct a rating credibility model of red wine by the Analytic Hierarchy Process, achieve the classification of red grapes through the evaluation results of red wine and cluster analysis method and analyze the correlation of the physical and chemical indicators between red grapes and red wine. Thus, the paper demonstrates that aromatic substances play an important role in the quality of red wine, so we cannot evaluate the quality of wine only by the physical and chemical indicators of wine grapes and wine.

Baker A.K., Ross C.F. (2014): Wine finish in red wine: The effect of ethanol and tannin concentration. Food Quality and Preference, 38: 65–74. https://doi.org/10.1016/j.foodqual.2014.05.014
Cai Z.C., He L.M. (2006): The application of correlation analysis principle on library and information science. Modern Information, 5: 151–156. (in Chinese)
Caldas J., Rebelo J. (2013): Portuguese wine ratings: An old product a new assessment. Wine Economics and Policy, 2: 102–110. https://doi.org/10.1016/j.wep.2013.11.004
D’Alessandro S., Pecotich A. (2013): Evaluation of wine by expert and novice consumers in the presence of variations in quality, brand and country of origin cues. Food Quality and Preference, 28: 287–303. https://doi.org/10.1016/j.foodqual.2012.10.002
Cortez P., Cerdeira A., Almeida F., Matos T., Reis J. (2009): Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems, 47: 547–553. https://doi.org/10.1016/j.dss.2009.05.016
Cozzolino D., Holdstock M., Dambergs R.G., Cynkar W.U., Smith P.A. (2009): Mid infrared spectroscopy and multivariate analysis: A tool to discriminate between organic and non-organic wines grown in Australia. Food Chemistry, 116: 761–765. https://doi.org/10.1016/j.foodchem.2009.03.022
Goodstein E.S., Bohlscheid J.C., Evans M., Ross C.F. (2014): Perception of flavor finish in model white wine: A time-intensity study. Food Quality and Preference, 36: 50–60. https://doi.org/10.1016/j.foodqual.2014.02.012
He J., Zhang H.N. (2011): Study on optimization of fermentation conditions of wild kiwifruit wine. International Conference on Agricultural and Biosystems Engineering, Phuket, Thailand: 320–324.
Hui X.J. (2013): The classification and evaluation of wine by fuzzy mathematics. Mathematics in Practice and Theory, 43: 40–45. (in Chinese)
Chen T., Jin Y.Y., Qiu X.P., Chen X. (2014): A hybrid fuzzy evaluation method for safety assessment of food-waste feed based on entropy and the analytic hierarchy process methods. Expert Systems with Applications, 41: 7328–7337. https://doi.org/10.1016/j.eswa.2014.06.006
Chira K., Pacella N., Jourdes M., Teissedre P.L. (2011): Chemical and sensory evaluation of bordeaux wines (Cabernet-Sauvignon and Merlot) and correlation with wine age. Food Chemistry, 126: 1971–1977. https://doi.org/10.1016/j.foodchem.2010.12.056
Juega M., Gonzalez-Ramos D., Bartolome B., Carrascosa A.V., Martinez-Rodriguez A.J. (2014): Chemical evaluation of white wines elaborated with a recombinant saccharomyces cerevisiae strain overproducing mannoproteins. Food Chemistry, 147: 84–91. https://doi.org/10.1016/j.foodchem.2013.09.126
Li H., Liu S.D., Wang H., Zhang Y.L. (2006): Studies on the statistical analyses methods for sensory evaluation results of wine. Journal of Chinese Institute of Food Science and Technology, 6: 126–131. (in Chinese)
Li N., Wu D.D. (2010): Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48: 354–368. https://doi.org/10.1016/j.dss.2009.09.003
Li Y., Li J.M., Jiang Z.J. (2009): Application of statistical analysis in the evaluation of grape wine quality. Liquor-Making Science and Technology, 4: 79–82. (in Chinese)
Liu Y.L. (2012): Application of new hopfield neural network classifier in the quality evaluation of grape wine.Value Engineering, 31: 181–182. (in Chinese)
Peng B.Z., Yue T.L., Yuan Y.H. (2008): A fuzzy comprehensive evaluation for selecting yeast for cider making. International Journal of Food Science and Technology, 43: 140–144. https://doi.org/10.1111/j.1365-2621.2006.01404.x
Song T.S. (2008): Discussions on the functions of the statistics of correlation coefficient and its applications: Taking SPSS as analysis tool. Statistical Thinktank, 11: 27–31. (in Chinese)
Sugintiene A. (2010): Quality evaluation of dandelion wine. Annals: Food Science and Technology, 11: 16–20.
Xu S.B. (1998). Practical decision-making method – principle of analytic hierarchy process (AHP). Tianjin University Press: Tianjin: 17.
Yu H.B., Qi N., ZhaoY.J. (2013): Application of gray correlation neural network to evaluation of wine quality. Microprocessors, 34: 49–51. (in Chinese)
Zhu B., Xu Z.S. (2014): Analytic hierarchy process-hesitant group decision making. European Journal of Operational Research, 239: 794–801. https://doi.org/10.1016/j.ejor.2014.06.019
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