IBIS
Infrared spectroscopy Beer analyses artificial Intelligence System
The flavor stability of beer is a critical quality attribute, directly influencing consumer satisfaction and marketability. While the chemical changes occurring during beer aging are known, the specific markers influencing beer's susceptibility to aging remain elusive. This study investigates the chemical markers driving beer's susceptibility to aging, identified through the integration of infrared spectroscopy and artificial intelligence.
Using a diverse set of beer samples, advanced machine learning algorithms were applied to infrared spectral data and experimentally determined degrees of beer aging to uncover key chemical signatures in fresh beers associated with flavor deterioration over time. Specifically, a trained convolutional neural network was explained using regression activation maps and Shapley values to elucidate how beer aging is influenced by its chemical fingerprint.
These insights provide brewers with actionable knowledge to optimize formulations, extend shelf life, and ensure consistent quality. Our findings highlight the transformative potential of AI-driven analytical tools to revolutionize quality control in the brewing industry, paving the way for more robust and data-driven decision-making processes.
What are the benefits?
- Analytical quality control of wort, fermenting and final beer
- Susceptibility of beer to aging– unique method
- All in one analysis – approx. 3 minutes per sample
- No extensive sample pretreatment – slight degassing only
- Simple, rapid analysis – at-line – suitable for an action during production
- Resource savings: chemical, material, time, and energy
