Improved Quantification of Important Beer Quality Parameters based on Nonlinear Calibration Methods applied to FT-MIR Spectra
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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 already 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 non-linear 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 such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation or foam stability.
The calibration models are established with enhanced non-linear 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 ($\epsilon$-PLSSVR and $\nu$-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.