Sensory Analysis and Statistical Methods

This study develops a modular Python framework called digimouth to model aroma release during food oral processing, validated with experimental data from 30 subjects.

This study benchmarks sensory properties of dairy and dairy-free products across ten categories, finding significant gaps in mozzarella, Greek-style yogurt, and butter.

This study compares the textural properties and consumer acceptance of plant-based and conventional breaded shrimp, finding that plant-based shrimp are harder and chewier but receive comparable hedonic ratings.

This study evaluates consumer acceptance of different burger formulations by combining sensory evaluation and texture analysis, finding that moistness and meatiness are key factors influencing preference.

Stanford diners (n = 116) rated soy and soy-wheat meatballs below beef-based ones on moistness, meatiness, fattiness and tastiness, yet perceived no hardness or chewiness gap despite instrumental differences.

This study evaluates how adding raw rapeseed press cake to snack bars affects sensory qualities and identifies bitter compounds responsible for the taste.

The paper creates a molecular atlas of food odorants and uses a generative AI model to design aroma formulations that mimic meat-like aromas using plant-derived compounds.

This study models consumer willingness to pay for honey based on sensory attributes and EEG data, finding that taste, perceived quality, gender, and frontal brain activity significantly influence willingness to pay.

This paper uses EEG to measure perceived food product similarity in a cross-modal task, finding that gamma oscillation power better correlates with similarity scores than ERP amplitude.

The paper analyzes consumer reviews to show that taste is the most frequently mentioned factor in food product ratings, with specific taste qualities like sweet and bitter being particularly common.