A IA consegue saborear coisas como café ou chocolate com sensores e melhorar o seu sabor para consumo humano ?

A IA pode ajudar a analisar e melhorar o sabor de alimentos como café ou chocolate, utilizando dados de sensores combinados com modelos de *machine learning* treinados com composição química e feedback sensorial humano. Línguas eletrónicas e sensores de gás detetam compostos de sabor, e a IA correlaciona estes sinais com perfis de sabor percebidos, permitindo ajustes na formulação para melhorar o sabor, aroma e aceitabilidade geral. Embora a IA não consiga "provar" como os humanos, acelera o desenvolvimento de produtos ao prever como as alterações nos ingredientes ou no processamento afetam as qualidades sensoriais. Esta abordagem é cada vez mais utilizada na ciência alimentar para otimizar sabor e qualidade.

— Enriched 15 de maio de 2026 · Source: 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).