Multivariate modeling of aging in bottled lager beer by principal component analysis and multiple regression methods

Area of Science:

• Food Science
• Chemistry
• Sensory Analysis

Background:

• Beer aging affects sensory qualities, necessitating objective evaluation methods.
• Chemical changes during aging impact flavor and aroma profiles.
• Existing methods for assessing beer staling may lack precision.

Purpose of the Study:

• To develop a predictive model for bottled lager beer aging using chemical and sensory data.
• To identify key chemical components and indices correlated with beer staling.
• To establish a robust regression model for organoleptic test score prediction.

Main Methods:

• Collected sensory data and chemical components (e.g., carbonyls, higher alcohols, DPPH, TBA).
• Employed multiple linear regression and principal component analysis for data analysis.
• Utilized varimax rotated principal components for variable selection to mitigate multicollinearity.

Main Results:

• Higher alcohols, TBA value, and DPPH scavenging amount were significant predictors of beer staling.
• These three factors explained 62% of the variance in the organoleptic test score (adjusted R² = 0.62).
• The developed regression model demonstrated weak multicollinearity.

Conclusions:

• Higher alcohols, TBA value, and DPPH scavenging amount are major contributors to the sensory perception of beer aging.
• A reliable mathematical model for predicting beer aging based on these key indicators was established.
• This research provides a scientific basis for quality control and shelf-life assessment of bottled lager beer.