WINE QUALITY CONTROL

BY

ANSUP BABU

SADASEEB CHOUDHURY

Objective• With data mining techniques classify wine quality

based on properties from table wine analyses.

• This work solves classification problem using decisiontree algorithm.

Agenda Wine types

Quality control

Attributes

Summary of attributes

Data reduction

Classification Algorithm(Decision Tree)

Conclusion

Types of Table Wines

Table Wines

Red Wine

Skin removed immediately

Fermentation 13-15 C

White Wine

Skins not removed immediately

Fermentation 25 -30 C

Rose Wine

Skins removed after a short period

Fermentation 25 – 30 C

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.

Attributes

SUMMARY OF ATTRIBUTES

PRINCIPAL COMPONENTS

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%

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.

References

1. P . Cortez , A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modelling wine preferences bydata mining from physicochemical properties.

2. Wine Classification online.

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