Examining Craft Beer Quality Control with FT-MIR Spectroscopy


A recent study published in Foods explored using Fourier transform mid-infrared (FT-MIR) spectroscopy combined with multivariate analysis to predict various quality parameters in craft beer.

Prediction models based on Fourier-transform mid-infrared spectroscopy (FT-MIR) can help quantify key quality parameters in craft beer, according to a study published in Foods (1).

Quality control is essential in the alcohol industry. For example, for Irish whiskey, analytical techniques have been used to analyze and evaluate congeners, which is a group of chemical compounds that are naturally produced during the fermentation and aging process in whiskey production and influence the flavor and aroma of whiskeys (2). However, ensuring the quality of craft beer is a little more complicated. Traditional methods for quality control can be time-consuming and costly, often requiring the use of chemicals and extensive laboratory procedures (1).

happy male friends drinking beer at bar or pub | Image Credit: © Syda Productions - stock.adobe.com

happy male friends drinking beer at bar or pub | Image Credit: © Syda Productions - stock.adobe.com

A recent study led by Ofelia Gabriela Meza-Marquez from the National Polytechnic Institute's National School of Biological Sciences-Zacatenco in Ciudad de Mexico, Mexico, explored an alternative solution that could be used to ensure the quality of craft beer. The study explored the use of Fourier-transform mid-infrared (FT-MIR) spectroscopy combined with multivariate analysis to predict various quality parameters in craft beer (1).

Beer enthusiasts and brewers alike understand the importance of maintaining specific organoleptic characteristics—attributes that are perceived by the senses, such as taste, color, and aroma. Ensuring these qualities are consistently met is crucial for consumer satisfaction and brand loyalty (1). Meza-Marquez's research aimed to streamline this process by developing prediction models based on FT-MIR spectroscopy to quantify key quality parameters, including color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity (1).

By employing FT-MIR spectroscopy, the researchers were able to gather spectral data from craft beer samples, which were then analyzed using principal component analysis (PCA) and partial least squares regression (PLS1) (1). PCA allowed for the classification of the beer samples according to their style, grouping 60 craft beer samples based on their unique spectral fingerprints (1). This classification is crucial for brewers who wish to maintain the distinctiveness of their beer varieties.

The PLS1 model proved to be effective. The PLS1 demonstrated high predictive accuracy, and the model's R²c values reached an impressive 0.9999 with standard error of calibration (SEC) values ranging from 0.01 to 0.11 and standard error of prediction (SEP) values from 0.01 to 0.14 (1). These results demonstrate the model's reliability in predicting multiple quality parameters simultaneously. However, the study noted that specific gravity could not be accurately predicted because of low variability in the reference values.

One of the most significant advantages of using FT-MIR spectroscopy in this context is its efficiency. Traditional methods for quality control in brewing can be labor-intensive and involve the use of solvents and reagents. In contrast, FT-MIR spectroscopy is rapid (approximately six minutes per analysis), cost-effective, and eco-friendly, as it eliminates the need for chemicals (1). This makes it an ideal solution for small-scale breweries that may not have extensive laboratory resources but still require stringent quality control measures (1).

The validation and prediction stages of the study confirmed the robustness of the developed models. External samples were used to test the model's predictive capacity, reinforcing its potential as a reliable tool for brewers (1). The researchers suggest that further work should be conducted using commercial samples to fully confirm the model's applicability in a real-world setting (1).

Meza-Marquez and her team conclude in their study that predictive models need to include additional parameters relevant to quality control. These additional parameters could encompass metals, microbiological counts, and health-benefiting attributes such as polyphenols and flavonoids (1).

With the construction of more all-encompassing models, the quality of craft beer could be further assured. With FT-MIR spectroscopy coupled with chemometrics, brewers can achieve a higher level of quality control, ensuring that their products meet consumer expectations consistently (1). This methodology not only enhances efficiency and reduces costs but also supports sustainable practices by minimizing the use of harmful chemicals.


(1) Meza-Marquez, O. G.; Rodriguez-Hijar, A. R.; Gallardo-Vasquez, T.; et al. The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics. Foods 2024, 13 (8), 1157. DOI: 10.3390/foods13081157

(2) Kelly, T. J.; O’Connor, C.; Kilcawley, K. N. Sources of Volatile Aromatic Congeners in Whiskey. Beverages 2023, 9 (3), 64. DOI: 10.3390/beverages9030064

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