Using Multispectral Analysis Combined with Chemometrics to Improve Beer Production

Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, Raman spectroscopy, and ultraviolet-visible (UV-vis) spectroscopy, can be used alongside chemometrics to improve the efficiency of beer production, according to a recent study published in Food Chemistry (1).

NIR, Raman, and UV-vis spectroscopy are spectral analysis techniques, which can interpret the spectra of objects under study (2). The benefits of using these techniques are that they offer a broader wavelength range compared to other techniques (1). As a result, these techniques are better suited for identifying characteristic absorption peaks of test subjects and improving quantitative analysis (1). In particular, NIR and Raman spectroscopy provide complementary information on the characteristics of substances at specific wavelengths, facilitating a more detailed molecular structure characterization (1,3).

Glass of cold craft light beer on dark background. | Image Credit: © gornist - stock.adobe.com

Researchers from Sichuan University of Science and Engineering and Wuliangye Group Co., Ltd. explored using multispectral analysis techniques with chemometrics to improve the brewing process. To this end, they designed and tested a new analytical method to monitor the brewing process of Qingke beer in real time (1). This advancement leverages the combined power of NIR spectroscopy, Raman spectroscopy, and UV-vis spectroscopy, alongside sophisticated chemometric techniques, to enhance the precision and efficiency of beer production (1).

The method focuses on real-time monitoring of key components in the wort during the critical mashing and boiling stages of Qingke beer brewing. Utilizing a multi-spectroscopy approach combined with chemometrics, the researchers aimed to develop predictive models for the content of reducing two sugars, free amino nitrogen (FAN), and total phenols (TP), in the brewing process (1).

One of the highlights of this study were the performance of the neural network (NN) models. These models were based on Raman and NIR spectroscopy, and both were effective in reducing the sugar content in beer (1). The NN model based on Raman spectroscopy achieved a relative prediction deviation (RPD) of 3.9727, whereas the NIR spectroscopy-based NN model attained an impressive RPD of 5.1952 (1).

For predicting the content of free amino nitrogen, the partial least squares (PLS) model based on Raman spectroscopy (RPD = 2.7301) and the NN model based on Raman spectroscopy (RPD = 4.3892) also demonstrated their effectiveness (1).

Quantitative analysis of total phenols was best achieved using the PLS model based on UV-vis spectroscopy (RPD = 4.0412) and the NN model based on Raman spectroscopy (RPD = 4.0540) (1). These models enable precise monitoring of phenolic compounds, ensuring that the beer ends up having the desired taste and quality that consumers expect.

The researchers demonstrated a detailed analysis of changes in reducing sugars, free amino nitrogen, and total phenols in Qingke malt at different mashing and boiling times. By applying multispectral techniques combined with chemometric modeling, the researchers identified the optimal models for predicting the content of these key substances during the primary stages of beer brewing (1).

The ability to predict and control the quality of wort in real time allows for better consistency and quality in the final beer product, aligning with modern production demands and consumer expectations. By applying advanced spectroscopic techniques with chemometrics, the researchers from Sichuan University of Science and Engineering and Wuliangye Group Co., Ltd. contributed to finding new ways to improve beer production.

References

(1) Zhou, X.; Li, L.; Zheng, J.; et al. Quantitative Analysis of Key Components in Qingke Beer Brewing Process by Multispectral Analysis Combined with Chemometrics. Food Chem. 2024, 436, 137739. DOI: 10.1016/j.foodchem.2023.137739

(2) Chambers, B. A. Utilizing Modern Analytical Instrumentation in Brewing Science to Further Quality and Innovation in Beer. Spectroscopy Suppl. 2021, 36 (S9A), 12–14.

(3) Chapman, J.; Gangadoo, S.; Truong, V. K.; Cozzolino, D. Spectroscopic Approaches for Rapid Beer and Wine Analysis. Curr. Opinion Food Sci. 2019, 28, 67–73. DOI: 10.1016/j.cofs.2019.09.001