Wine Evaluation Modeling Based on Lasso and Support Vector Regression
Yanyun Yao*,**, Bing Xu*, and Jinghui He**
*Research Institute of Economic Statistics and Quantitative Economics, Zhejiang Gongshang University
18 Xuezheng Road, Xiasha University Town, Hangzhou 310018, China
**Department of Mathematics, Shaoxing University
900 Chengnan Avenue, Yuecheng District, Shaoxing 312000, China
Wine consumption is gaining popularity, and significant attention has been given to its quality. In the present paper, an objective evaluation model along with a reliability test via Lasso and nonlinear effect test via support vector regression (SVR) is proposed. The digital simulation is finished with the experimental data obtained from the A problem of CUMCM-2012 (China Undergraduate Mathematical Contest in Modeling in 2012). The results of Lasso regression show that the wine quality mainly depends upon eight physicochemical indicators. Further research results of SVR imply that with several training samples, a good evaluation can be realized, denoting that our model based on Lasso SVR can significantly reduce the costs of measurement and appraisal. Compared to other relevant articles, this paper builds an objective and credible wine evaluation system where the physicochemical indicators and the latent nonlinear effect are considered. Moreover, the evaluation costs are taken into account.
-  S. Somogyi et al., “The Underlying Motivations of Chinese Wine Consumer Behaviour,” Asia Pacific J. of Marketing and Logistics, Vol.23, No.4, pp. 473-485, 2011.
-  Y. Zhou, “Answer to CUMCM-2012 A Question,” Mathematical Modeling and Its Applications, No.1, pp. 60-66, 2012.
-  X. Gong et al., “Model for Wine Sensory Evaluation Based on Electronic Nose,” China Brewing, Vol.33, No.5, pp. 67-71, 2014.
-  Y. Li, J. Li, and Z. Jiang, “Application of Statistical Analysis in the Evaluation of Grape Wine Quality,” Liquor-Making Science & Technology, No.4, pp. 79-82, 2009.
-  H. Li et al., “Effects of Different Factors on Tasting Results of Dry Red Wine,” J. of Biomathematics, No.2, pp. 223-228, 2005.
-  Y. Song, Y. Ta, and J. Feng, “Application of the Fuzzy Synthetic Judgement in the Taste of Wine,” Sino-overseas Grapevine & Wine, No.2, pp. 35-36, 2002.
-  B. Wang and J. Feng, “Application of Fuzzy Synthetic Evaluation in the Sense Estimation of Dry Red Wine,” Food and Nutrition in China, No.8, pp. 33-37, 2011.
-  X. Chen et al., “Diversity Analysis of Grape Wine Evaluation and the Classification Method of Wine-making Grapes – Data Analysis of 2012 National College Mathematical Modeling Contest,” Liquor-Making Science & Technology, No.7, pp. 28-32, 2013.
-  X. Pan, Q. Wang, and X. Huang, “The Evaluation of the Wine,” J. of MUC (Natural Science Edition), No.S1, pp. 126-132, 2013.
-  C. Qi and Q. Fang, “Partial Least Squares Modelling with R Software and Empirical Analysis,” Mathematical Theory and Applications, No.2, pp. 103-111, 2013.
-  X. Cheng, J. Chen, and W. Wu, “Application of Multivariate Statistics to Analyze the Correlations of Physicochemical indicators of Grape and Wine With the Quality of Wine,” Sino-overseas Grapevine & Wine, No.4, pp. 43-47, 2013.
-  J. Che et al., “Study of Wine Evaluation Based on Lasso Regression,” J. of Management Science & Statistical Decision, Vol.10, No.4, pp. 53-61, 2013.
-  B. Liu, “Organoleptic Investigation of Wine,” Liquor-making Science & Technology, No.8, pp. 89-91, 2005.
-  Y. Ding and H. Shi, “Effects of Phenols on the Quality of Wine,” Liquor-Making Science & Technology, No.4, pp. 55-59, 2011.
-  J. Li, P. He, and L. Liu, “Aroma Components of Wines from Quality Grape Varieties,” The J. of Northewest Agricultural University, No.6, pp. 6-9, 1998.
-  B. Hu et al., “EPR Spectroscopy Studies on DPPH Free Radical Scavenging Activity of Dry Red Wines and Their Relationship with Total Polyphenols Content,” Scientia Agricultura Sinica, No.1, pp. 135-141, 2012.
-  G. V. Jones and K. Storchmann, “Wine Market Prices and Investment Under Uncertainty: An Econometric Model for Bordeaux Crus Classes,” Agricultural Economics, No.26, pp. 115-133, 2001.
-  X. Wu, “Complex Data Statistical Method: Based on the Application of R,” Beijing: China Renmin University Press, pp. 28-29, 2012.
-  R. Tibshirani, “Regression Shrinkage and Selection Via the Lasso,” J. Royal. Statist. Soc. (B), No.58, pp. 267-288, 1996.
-  J. Man and W. Yang, “Based on Multiple Collinearity Processing Method,” Mathematical Theory and Applications, No.2, pp. 105-109, 2010.
-  B. Effron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least Angle Regression,” The Annals of Statistics, No.2, pp. 407-499, 2004.
-  C. L. Mallows, “Some Comments on Cp,” Technometrics, Vol.15, No.4, pp. 661-675, 1973.
-  V. Vapnik, S. Golowich, and A. Smola, “Support Vector Machine for Function Approximation Regression Estimation and Signal Processing,” Advances in Neural Information Processing Systems, No.9, pp. 281-287, 1996.
-  B. Sheng and P. Ye, “Error Analysis for Support Vector Machine Classifiers on Unite Sphere of Euclidean Space,” J. of Computational Information Systems, No.9, pp. 3023-3030, 2011.
-  B. Sheng and P. Ye, “Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces,” J. of Computers, No.1, pp. 252-257, 2012.
-  B. Sheng, L. Duan, and P. Ye, “Strong Convex Loss Can Increase the Learning Rates of Online Learning,” J. of Computers, No.7, pp. 1606-1611, 2014.
-  S. Cai, R. Zhang, L. Liu, and D. Zhou, “A Method of Salt-affected Soil Information Extraction Based On a Support Vector Machine With Texture Features,” Mathematical and Computer Modelling, No.51, pp. 1319-1325, 2010.
-  R. Ji, D. Li, L. Chen, and W. Yang, “Classification and Identification of Foreign Fibers in Cotton on the Basis of a Support Vector Machine,” Mathematical and Computer Modelling, No.51, pp. 1433-1437, 2010.
-  F. J. de Cos Juez, P. J. García Nieto, J. Martínez Torres, and J. Taboada Castro, “Analysis of Lead Times of Metallic Components in the Aerospace Industry Through a Supported Vector Machine Model,” Mathematical and Computer Modelling, No.52, pp. 1177-1184, 2010.
-  Y. Yuan, “Forecasting the Movement Direction of Exchange Rate With Polynomial Smooth Support Vector Machine,” Mathematical and Computer Modelling, No.57, pp. 932-944, 2013.
-  S. Liu, H. Tai, Q. Ding, D. Li, L. Xu, and Y. Wei, “A Hybrid Approach of Support Vector Regression With Genetic Algorithm Optimization For Aquaculture Water Quality Prediction,” Mathematical and Computer Modelling, No.58, pp. 458-465, 2013.
-  S. B. Rinaldo, D. F. Duhan, B. Trela, T. Dodd, and N. Velikova, “Evaluating Tastes and Aromas of Wine: A Peek Inside the ‘Black Box,’” Int. J. of Wine Business Research, No.3, pp. 1-26, 2014.