Kan AI dingen zoals koffie of chocolade proeven met sensoren en hun smaak verbeteren voor menselijke consumptie ?

AI kan helpen bij het analyseren en verbeteren van de smaak van voedingsmiddelen zoals koffie of chocolade door sensorische data te combineren met machinaal leren-modellen die getraind zijn op chemische samenstelling en menselijke sensorische feedback. Elektronische tongen en gassensoren detecteren smaakstoffen, en AI koppelt deze signalen aan waargenomen smaakprofielen, waardoor formuleringen kunnen worden aangepast om smaak, aroma en algehele acceptatie te verbeteren. Hoewel AI niet kan "proeven" zoals mensen, versnelt het de productontwikkeling door te voorspellen hoe veranderingen in ingrediënten of verwerking de sensorische eigenschappen beïnvloeden. Deze aanpak wordt steeds vaker gebruikt in de voedingswetenschap om smaak en kwaliteit te optimaliseren.

— Verrijkt 15 mei 2026 · Bron: Nature Food, 2023

Background

AI-assisted taste engineering relies on electronic tongues and gas chromatography sensors that detect volatile organic compounds and non-volatile taste-active molecules. These instruments quantify compounds such as furfuryl acetate (ethyl-maltol-like aroma), 2-ethylphenol (smoky/phenolic notes), theobromine, and trigonelline in coffee; and theobromine, phenylethylamine, and various Maillard reaction products in chocolate. Machine learning models trained on both GC-MS or LC-MS chemical fingerprints and human sensory panels (e.g., trained assessors scoring attributes such as bitterness, acidity, sweetness, astringency, and aroma intensity) learn to predict perceived flavor profiles from raw chemical data. Partial least squares regression and deep neural networks are commonly used to map sensor outputs to human ratings, enabling rapid “virtual tasting.” Industry workflows iteratively adjust roast profiles or conching times, then re-measure, reducing sensory evaluation cycles from weeks to days. Similar approaches are reported for modulating bitterness in cocoa by varying fermentation time or adding natural bitter-masking peptides derived from enzymatic hydrolysis of milk proteins (Cao et al., Sci. Rep. 2022). In coffee, controlling 3-methylbutanal and guaiacol concentrations via controlled roasting can shift profiles from “green/grassy” to “caramel/smoky,” aligning with consumer preference clusters identified by preference mapping in supermarket panels (Nature Food, 2023). Sensor arrays and electronic noses have demonstrated classification accuracy above 90% for roast level and origin in Arabica lots (Romano et al., Food Chem., 2021).