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Prediction of sensory stability of beer by infrared spectroscopy coupled with artificial intelligence

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60193697%3A_____%2F23%3AN0000053" target="_blank" >RIV/60193697:_____/23:N0000053 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Prediction of sensory stability of beer by infrared spectroscopy coupled with artificial intelligence

  • Original language description

    Oral presentation at international symposium 15th Trends in Brewing , KU Leuven - Technology Campus Ghent, Belgium, April 2nd - 6th, 2023. The sensory instability of beer is an undesirable and spontaneous phenomenon with a significant influence on its sensory quality resulting in the stale flavour of beer. Recent methods for evaluation of beer propensity for such staling are based on storage of beer for a few months at a lower temperature (e.g. 20 °C) or a few days at an elevated temperature (e.g. 45 °C). Although the methods provide some information regarding sensory (in)stability, their drawbacks make them useable for routine and informative evaluation with difficulty as well as for data-driven decision-making in practice. For this reason, a project focused on the evaluation of the sensory stability of beer was launched by Research Institute of Brewing and Malting in cooperation with Pilsner Urquell brewery. The project aims to develop a universal tool for quick evaluation of beer propensity for sensorial deterioration during storage (directly from fresh beer sample) by combining Fourier-transform infrared spectroscopy (FTIR) and convolutional neural network (CNN), useable for a routine quality control of beer. The FTIR acts as an analytical probe observing the chemical nature of analyzed beer in form of FTIR spectrum which is processed and evaluated by the CNN. To obtain practically usable artificial intelligence, the CNN was trained by data obtained from 163 beer brands (lagers, ales, and non-alcoholic beers) periodically brewed by collaborative breweries (more than 900 samples were obtained) and naturally aged at 20 °C for 6 and 9 months. Resulted prediction performance for an independent test set expressed by the coefficient of determination was 0.83 and the prediction uncertainty was comparable with the experimental one. Besides the sensory stability, obtained FTIR spectra were also tested for the determination of some analytical parameters of beer by CNN, e.g. alcohol, OG, IBU, FAN, polyphenols, etc., in order to make the developing tool more versatile.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    10406 - Analytical chemistry

Result continuities

  • Project

    <a href="/en/project/FW03010440" target="_blank" >FW03010440: Prediction of sensory stability of beer by infrared spectroscopy and artificial intelligence</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů