# Wineinformatics: Regression on the Grade and Price of Wines through Their Sensory Attributes

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. The Data

#### 2.2. Evaluation Metrics

_{y}is the standard deviation of the variable.

#### 2.3. Methods

**ϵ**. Allowing for the slack in the margins allows for dealing with the case when not all data fits within the margins around the hyperplane. This value was left at the default of 1. The kernel allows for the SVR to transform the data into a higher dimensional space such that it is easier to construct a hyperplane which follows the data. The linear, Gaussian Radial Basis Function (RBF), and Laplacian RBF kernels are tested in this research.

**ϵ**except it is used when performing classifications, was set to 1. In all cases, both in this work and the prior work, five-fold cross validation was used to train and test the models.

## 3. Results

#### 3.1. Regression

#### 3.2. Classification

## 4. Discussion

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Wine Institute. World Wine Production by Country. Available online: https://www.wineinstitute.org/files/WorldWineProductionbyCountry.pdf (accessed on 28 September 2018).
- Schmidt, D.; Freund, M.; Velten, K. End-User Software for Efficient Sensor Placement in Jacketed Wine Tanks. Fermentation
**2018**, 4, 42. [Google Scholar] [CrossRef] - Sommer, S.; Cohen, S.D. Comparison of Different Extraction Methods to Predict Anthocyanin Concentration and Color Characteristics of Red Wines. Fermentation
**2018**, 4, 39. [Google Scholar] [CrossRef] - Er, Y.; Atasoy, A. The classification of white wine and red wine according to their physicochemical qualities. Int. J. Intell. Syst. Appl. Eng.
**2016**, 4, 23–26. [Google Scholar] [CrossRef] - Cortez, P.; Cerdeira, A.; Almeida, F.; Matos, T.; Reis, J. Modeling wine preferences by data mining from physicochemical properties. Decis. Support Syst.
**2009**, 47, 547–553. [Google Scholar] [CrossRef][Green Version] - Ebeler, S.E. Linking flavor chemistry to sensory analysis of wine. In Flavor Chemistry; Springer: Boston, MA, USA, 1999; pp. 409–421. [Google Scholar]
- Chen, B.; Velchev, V.; Nicholson, B.; Garrison, J.; Iwamura, M.; Battisto, R. (2015, December). Wineinformatics: Uncork Napa’s Cabernet Sauvignon by Association Rule Based Classification. In Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 9–11 December 2015; pp. 565–569. [Google Scholar]
- Chen, B.; Le, H.; Rhodes, C.; Che, D. Understanding the Wine Judges and Evaluating the Consistency through White-Box Classification Algorithms. In Industrial Conference on Data Mining; Springer: Cham, Germany, 2016; pp. 239–252. [Google Scholar]
- Wariishi, N.; Flanagan, B.; Suzuki, T.; Hirokawa, S. Sentiment Analysis of Wine Aroma. In Proceedings of the 2015 IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI), Okayama, Japan, 12–16 July 2015; pp. 207–212. [Google Scholar]
- Flanagan, B.; Wariishi, N.; Suzuki, T.; Hirokawa, S. Predicting and visualizing wine characteristics through analysis of tasting notes from viewpoints. In International Conference on Human-Computer Interaction; Springer: Cham, Germany, 2015; pp. 613–619. [Google Scholar]
- About Our Tastings. Available online: https://www.winespectator.com/display/show/id/scoring-scale (accessed on 28 September 2018).
- Chen, B.; Rhodes, C.; Yu, A.; Velchev, V. The Computational Wine Wheel 2.0 and the TriMax Triclustering in Wineinformatics. In Industrial Conference on Data Mining; Springer: Cham, Germany, 2016; pp. 223–238. [Google Scholar]
- Chen, B.; Rhodes, C.; Crawford, A.; Hambuchen, L. Wineinformatics: Applying data mining on wine sensory reviews processed by the computational wine wheel. In Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, China, 14 December 2014; pp. 142–149. [Google Scholar]
- Wine Spectator’s 100-Point Scale. Available online: http://www.winespectator.com/display/show/id/scoring-scale (accessed on 28 September 2018).
- Palmer, J. Multi-Target Classification and Regression in Wineinformatics. Ph.D. Thesis, University of Central Arkansas, Conway, AR, USA, 2018. [Google Scholar]
- Fradkin, D.; Muchnik, I. Support vector machines for classification. DIMACS Ser. Discret. Math. Theor. Comput. Sci.
**2006**, 70, 13–20. [Google Scholar] - Martin, L. A Simple Introduction to Support Vector Machines; Michigan State University: East Lansing, MI, USA, 2011. [Google Scholar]
- Smits, G.F.; Jordaan, E.M. Improved SVM regression using mixtures of kernels. In Proceedings of the 2002 International Joint Conference on Neural Networks, Honolulu, HI, USA, 12–17 May 2002; Volume 3, pp. 2785–2790. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. Available online: https://www.R-project.org (accessed on 28 September 2018).
- Microsoft and R. C. Team, Microsoft R Open, Microsoft, Redmond, Washington, 2017. Available online: https://mran.microsoft.com/ (accessed on 28 September 2018).
- Karatzoglou, A.; Smola, A.; Hornik, K.; Zeileis, A. Kernlab-an S4 package for kernel methods in R. J. Stat. Softw.
**2004**, 11, 1–20. [Google Scholar] [CrossRef] - Mauricio Zambrano-Bigiarini, hydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series, 2017, r Package Version 0.3-10. Available online: http://hzambran.github.io/hydroGOF/ (accessed on 28 September 2018).
- Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST)
**2011**, 2, 27. [Google Scholar] [CrossRef] - Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. ACM SIGKDD Explor. Newsl.
**2009**, 11, 10–18. [Google Scholar] [CrossRef]

**Figure 1.**Review of the Kosta Browne Pinot Noir Sonoma Coast 2009 (scores 95 pts) on both chemical and sensory analysis.

**Figure 2.**A word cloud of all of the keywords that occured in at least 2000 different wine reviews. The size of the word corresponds to its frequency.

**Table 1.**Response variables and their class categories for the four-class dataset and the two-class dataset.

Category | Grade | Price | |
---|---|---|---|

Four-class dataset | 1 | ≤84 | ≤$18 |

2 | 85~89 | $19~$29 | |

3 | 90~94 | $29~$50 | |

4 | 95~100 | >50 | |

Two-class dataset | 1 | <90 | ≤$29 |

1 | ≥90 | >$29 |

**Table 2.**Table of results for the regression model on Grade. Normalized error is calculated using the minimax method.

Kernel | ME | MAE | MSE | RMSE | NMAE | NRMSE | r | r^{2} |
---|---|---|---|---|---|---|---|---|

Linear | −0.07 | 1.64 | 4.27 | 2.07 | 8.2% | 10.4% | 0.73 | 0.54 |

RBF/Laplace | 0.00 | 4.59 | 4.02 | 2.00 | 8.0% | 10.0% | 0.75 | 0.56 |

**Table 3.**Table of results for the regression model on price. The normalized error is calculated using the standard deviation method.

Kernel | ME | MAE | MSE | RMSE | NMAE | NRMSE | r | r^{2} |
---|---|---|---|---|---|---|---|---|

Linear | −9.56 | 20.53 | 2082.15 | 45.63 | 41.8% | 93.0% | 0.44 | 0.19 |

RBF/Laplace | −9.88 | 20.52 | 2133.56 | 46.19 | 41.8% | 94.2% | 0.43 | 0.18 |

**Table 4.**Table of results for the regression model on price, without outliers. The normalized error is calculated using the standard deviation method.

Kernel | ME | MAE | MSE | RMSE | NMAE | NRMSE | r | r^{2} |
---|---|---|---|---|---|---|---|---|

Linear | −3.80 | 13.07 | 325.25 | 18.03 | 64.3% | 88.7% | 0.50 | 0.25 |

RBF/Laplace | −3.75 | 12.94 | 318.64 | 17.85 | 63.6% | 87.8% | 0.51 | 0.27 |

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**MDPI and ACS Style**

Palmer, J.; Chen, B. Wineinformatics: Regression on the Grade and Price of Wines through Their Sensory Attributes. *Fermentation* **2018**, *4*, 84.
https://doi.org/10.3390/fermentation4040084

**AMA Style**

Palmer J, Chen B. Wineinformatics: Regression on the Grade and Price of Wines through Their Sensory Attributes. *Fermentation*. 2018; 4(4):84.
https://doi.org/10.3390/fermentation4040084

**Chicago/Turabian Style**

Palmer, James, and Bernard Chen. 2018. "Wineinformatics: Regression on the Grade and Price of Wines through Their Sensory Attributes" *Fermentation* 4, no. 4: 84.
https://doi.org/10.3390/fermentation4040084