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Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra

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Abstract

During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229–235), Zhang et al. (J I Brewing. 2012;118(4):361–367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (𝜖-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to linear methods, showing a clear out-performance in most cases and being able to meet the model quality requirements defined by the experts at the beer company.

Workflow for calibration of non-Linear model ensembles from FT-MIR spectra in beer production 

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Notes

  1. http://www.eigenvector.com/software/pls_toolbox.htm

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Acknowledgments

Financial support was provided by (i) the Austrian research funding association (FFG) under the scope of the COMET programme within the research project Industrial Methods for Process Analytical Chemistry - From Measurement Technologies to Information Systems (imPACts) (contract #843546), (ii) the Basque Government through the ELKARTEK and BERC 2014-2017 programs, and (iii) the Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa accreditation SEV-2013-032. This publication reflects only the authors’ views.

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Authors and Affiliations

  1. BCAM - Basque Center for Applied Mathematics, Bilbao, Spain

    Carlos Cernuda

  2. Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Carlos Cernuda & Edwin Lughofer

  3. BrauUnion GmbH, Poschacherstrasse 35, 4020, Linz, Austria

    Helmut Klein & Clemens Forster

  4. RECENDT GmbH, Science Park 2, Altenbergerstrasse 69, A-4040, Linz, Austria

    Marcin Pawliczek & Markus Brandstetter

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  1. Carlos Cernuda
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  2. Edwin Lughofer
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  3. Helmut Klein
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  4. Clemens Forster
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  5. Marcin Pawliczek
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Correspondence to Edwin Lughofer.

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Published in the topical collection Process Analytics in Science and Industry with guest editor Rudolf W. Kessler.

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Cernuda, C., Lughofer, E., Klein, H. et al. Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra. Anal Bioanal Chem 409, 841–857 (2017). https://doi.org/10.1007/s00216-016-9785-4

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