Quantitative analysis of key components in Qingke beer brewing process by multispectral analysis combined with chemometrics

In order to monitor the Qingke beer brewing process in real time, this paper presents an analytical method for predicting the content of key components in the wort during the mashing and boiling stages using multi-spectroscopy combined with chemometrics. The results showed that the Neural Networks (NN) model based on Raman spectroscopy (RPD = 3.9727) and the NN model based on NIR spectroscopy (RPD = 5.1952) had the best prediction performance for the reducing sugar content in the mashing and boiling stages; The partial least Squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) predicted the content of free amino nitrogen best; The PLS model based on UV-Vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) are most suitable for the quantitative analysis of total phenols. The results can be used as a guide for real-time control of wort quality in industrial production.