Target variable: Quality Parameters associated: Alcohol, pH, Acidity, Volatility The following Quality can be achieved Pricing based on the chemical and physiometric properties. Segmentation: Defining new markets. DFV Wines is steadfastly committed in crafting and representing wines of the highest quality produced in accordance with sustainable wine growing practice using data mining.

1. WINE QUALITY CONTROL
BY
ANSUP BABU
SADASEEB CHOUDHURY

2. Objective
• With data mining techniques classify wine quality
based on properties from table wine analyses.
• This work solves classification problem using decision
tree algorithm.

3. Agenda
 Wine types
 Quality control
 Attributes
 Summary of attributes
 Data reduction
 Classification Algorithm(Decision Tree)
 Conclusion

4. Types of Table Wines
Table Wines
Red Wine
White Wine
Skin removed immediately
Skins not removed immediately
Fermentation 13-15 C
Fermentation 25 -30 C
Rose Wine
Skins removed after a short
period
Fermentation 25 – 30 C

5. pH value
 pH is a fundamental element of the wine-making





industry. pH strongly influences wine properties such
as colour, oxidation, biological and chemical stability
pH measures the quantity of acids present, the
strength of the acids, and the effects of minerals and
other ingredients in the wine
pH values range from 2.9 to 4.2 in wine
The lower the pH, the more acidic the wine is, the
higher the pH, the less acidic the wine.
Red wines typically have a higher pH than white wines.
The lower the pH, the better the wine will retain its
color.

6. Attributes

7. SUMMARY OF ATTRIBUTES

8. PRINCIPAL COMPONENTS

10. CONCLUSION
 Target variable: Quality
 Parameters associated: Alcohol, pH, Acidity, Volatility
 The following Quality can be achieved
Rules No.
Q (1 - 10)
Volatile. Acidity
Alcohol
Cover
1
6
< 0.2675
A < 10.85
34%
2
5
--------
> 10.85
37%
3
4
>= 0.2675
A< 10.85
29%

11. Business Implications
 Pricing based on the chemical and physiometric properties.
 Segmentation: Defining new markets.
 DFV Wines is steadfastly committed in crafting and representing wines
of the highest quality produced in accordance with sustainable
winegrowing practice using data mining.

12. References
P . Cortez , A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modelling wine preferences by
data mining from physicochemical properties.
2. Wine Classification online.
1.

13. THANK YOU