Rapid monitoring of beer-quality attributes based on UV-Vis spectral data

Abstract

This work aimed to determinate eight beer properties using UV-Vis spectra in combination with principal component regression (PCR) or artificial neural network (ANN) models. A statistical experimental design was performed to generate the calibration data. First, principal component analysis (PCA) was applied to the original spectral data, and the scores in significant PCs were utilized to calibrate both models. PCR showed poor correlation for beer parameters (R2 < 0.61). The ANNs showed satisfactory correlations (R2 = 0.74–0.92) and low relative error considering a variable range (Er < 9%) for most of the beer-quality attributes, but vicinal diketones (R2 = 0.56, Er = 16.69%). Once implemented, this method would be fast and low cost.