Research on Wine Evaluation based on Principal Component Analysis and Linear Regression

Mengting Liu; Chongrun Wang; Yumeng Yang

doi:10.54097/qtrwc808

Authors

Mengting Liu
Chongrun Wang
Yumeng Yang

DOI:

https://doi.org/10.54097/qtrwc808

Keywords:

Principal Component Analysis, Step-by-Step Regression Model, Linear Regression Model

Abstract

This study integrates computer technology and mathematical modeling methods to deeply explore the grading of wine-making grapes and the internal relationships between physicochemical indicators and wine quality. By using the computer software SPSS, hierarchical clustering, principal component analysis, step-by-step regression models, and linear regression models are applied to process and analyze wine evaluation data, as well as the physicochemical indicator data of grapes and wines. Through model construction, and solution, a reasonable grading of wine-making grapes is achieved. It is found that there is a linear correlation between the physicochemical indicators of grapes and wines, and both have a linear impact on wine quality. This indicates that it is feasible to evaluate wine quality using the physicochemical indicators of grapes and wines, providing a scientific basis for quality control and production decision-making in the wine industry. It also demonstrates the powerful effectiveness of computer-assisted mathematical modeling in solving complex economic management problems.

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