Fast pattern recognition of malted and unmalted beer: An investigation using FTIR, UV-VIS, fluorescence spectroscopy and chemometrics



Palabras clave:

malted beer, unmalted beer, spectroscopy, Principal Component Analysis, self organizing maps


Beer production and consumption has increased worldwide during the past years. In this growing market, consumers have opted for products endowed with greater quality and diversity. In this respect, malted beers offer a more pleasant sensory experience. From a practical point of view, the high cost of production, when compared to the unmalted beer, passed on to the added value of the final product, then is common to encounter unmalted beers labeled as malted in the market. So, the characterization of beers into groups of malted and unmalted beers is of great importance for food control agencies. The present work reports a good alternative classification procedure that is fast, efficient, with no sample preparation using fluorescence spectroscopy associated with SOM (Self Organizing Map) and it is compared with the negative results (no pattern recognition) obtained with a FTIR and UV-VIS spectroscopy associated with PCA also performed with no sample preparation process.


Ballabio, D. (2015). A MATLAB toolbox for Principal Component Analysis and unsupervised exploration of data structure. Chemometrics and Intelligent Laboratory Systems, 149, Part B, 1-9.

Blandino, A., Al-Aseeri, M. E., Pandiella, S. S., Cantero, D., & Webb, C. (2003). Cereal-based fermented foods and beverages. Food Research International, 36(6), 527-543

Colen, L., & Swinnen, J. (2016). Economic Growth, Globalisation and Beer Consumption. Journal of Agricultural Economics, 67(1), 186-207.

Coman, E., Brewster, M. W., Popuri, S. K., Raim, A. M., & Gobbert, M. K. (2012). A comparative evaluation of matlab, octave, freemat, scilab, r, and idl on tara. UMBC Faculty Collection.

Cortese, M., Gigliobianco, M. R., Peregrina, D. V., Sagratini, G., Censi, R., & Di Martino, P. (2020). Quantification of phenolic compounds in different types of crafts beers, worts, starting and spent ingredients by liquid chromatography-tandem mass spectrometry. Journal of Chromatography A, 1612, 460622.

Cuadros-Rodríguez, L., Ruiz-Samblás, C., Valverde-Som, L., Pérez-Castaño, E., & González-Casado, A. (2016). Chromatographic fingerprinting: An innovative approach for food ‘identitation’ and food authentication - A tutorial. Analytica Chimica Acta, 909, 9-23.

Davé, R. N., & Krishnapuram, R. (1997). Robust clustering methods: A unified view. IEEE Transactions on Fuzzy Systems, 5(2), 270 - 293.

de Gaetano, G., Costanzo, S., Di Castelnuovo, A., Badimon, L., Bejko, D., et al. (2016). Effects of moderate beer consumption on health and disease: A consensus document. Nutrition, Metabolism and Cardiovascular Diseases, 26(6), 443-467

Duarte, I. F., Barros, A., Almeida, C., Spraul, M., & Gil, A. M. (2004). Multivariate Analysis of NMR and FTIR Data as a Potential Tool for the Quality Control of Beer. Journal of Agricultural and Food Chemistry, 52(5), 1031-1038.

Eaton, J. W. (n.d.). GNU octave. Free Software Foundation. Retrieved from //

Guido, L. F., Curto, A. F., Boivin, P., Benismail, N., Gonçalves, C. R., & Barros, A. A. (2007). Correlation of malt quality parameters and beer flavor stability: Multivariate analysis. Journal of Agricultural and Food Chemistry, 55(3), 728-733.

Inc, T. M. (n.d.). MATLab. Natick, Massachusetts: The MathWorks Inc.

Kaplan, D. T., Levy, S. D., & Lambert, K. A. (2016). Introduction to Scientific Computation and Programming in Python (1st edition). United States: Project Mosaic Books.

Krakowska, B., Custers, D., Deconinck, E., & Daszykowski, M. (2016). Chemometrics and the identification of counterfeit medicines—A review. Journal of Pharmaceutical and Biomedical Analysis, 127, 112-122.

Lachenmeier, D. W. (2007). Rapid quality control of spirit drinks and beer using multivariate data analysis of Fourier transform infrared spectra. Food Chemistry, 101(2), 825-832.

Lakowicz, J. R. (2006). Principles of Fluorescence Spectroscopy (Third Edit). Baltimore/USA: Springer.

Odibo, F. J. C., Nwankwo, L. N., & Agu, R. C. (2002). Production of malt extract and beer from Nigerian sorghum varieties. Process Biochemistry, 37(8), 851-855.

Petrov, N., Georgieva, A., & Jordanov, I. (2013). Self-organizing maps for texture classification. Neural Computing and Applications, 22(7), 1499–1508.

Pierce, K. M., Hoggard, J. C., Hope, J. L., Rainey, P. M., Hoofnagle, A. N., et al. (2006). Fisher Ratio Method Applied to Third-Order Separation Data To Identify Significant Chemical Components of Metabolite Extracts. Analytical Chemistry, 78(14), 5068–5075.

Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A., & Jent, N. (2007). A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. Journal of Pharmaceutical and Biomedical Analysis, 44(3), 683-700.

Sádecká, J., & Tóthová, J. (2007). Fluorescence spectroscopy and chemometrics in the food classification - A review. Czech Journal of Food Sciences, 25, 159-174.

Sleiman, M., Venturini Filho, W. G., Ducatti, C., & Nojimoto, T. (2010). Determinação do percentual de malte e adjuntos em cervejas comerciais brasileiras através de análise isotópica. Ciência e Agrotecnologia, 34(1), 163-172.

StatSoft, I. (n.d.). Statistica. Tulsa: StatSoft.

Steiner, E., Auer, A., Becker, T., & Gastl, M. (2012). Comparison of beer quality attributes between beers brewed with 100% barley malt and 100% barley raw material. Journal of the Science of Food and Agriculture, 92(4), 803-813.

Thompson, T. (2015). The gluten-free labeling rule: What registered dietitian nutritionists need to know to help clients with gluten-related disorders. Journal of the Academy of Nutrition and Dietetics, 115(1), 13-16.

Wolfram Research, I. (n.d.). Mathematica. Champaign, Illinois: Wolfram Research, Inc. Retrieved from

Yano, M., Back, W., & Krottenthaler, M. (2008). The impact of low heat load and activated carbon treatment of second wort on beer taste and flavour stability. Journal of the Institute of Brewing, 114(4), 357-364.

Yi, L., Dong, N., Yun, Y., Deng, B., Ren, D., Liu, S., & Liang, Y. (2016). Chemometric methods in data processing of mass spectrometry-based metabolomics: A review. Analytica Chimica Acta, 914, 17-34.

Zielinski, A. A. F., Haminiuk, C. W. I., Nunes, C. A., Schnitzler, E., van Ruth, S. M., & Granato, D. (2014). Chemical Composition, Sensory Properties, Provenance, and Bioactivity of Fruit Juices as Assessed by Chemometrics: A Critical Review and Guideline. Comprehensive Reviews in Food Science and Food Safety, 13(3), 300-316.



Cómo citar

Leoncio Braga, F. ., & Braga, S. . (2021). Fast pattern recognition of malted and unmalted beer: An investigation using FTIR, UV-VIS, fluorescence spectroscopy and chemometrics. Scientia Agropecuaria, 12(3), 361-367.



Artículos originales