Peripheral Nervous System Responses to Food Stimuli: Analysis Using Data Science Approaches

TL;DR

This paper introduces a data science methodology to analyze physiological responses to food stimuli, aiming to better understand emotional reactions and sensory properties in food science.

Contribution

It presents a novel data mining approach for describing emotional responses to food based on physiological data, which is underutilized in food research.

Findings

Physiological responses can be modeled to describe food-related emotions.

The subgroup discovery method effectively identifies patterns in physiological data.

The approach enhances understanding of sensory and hedonic properties of odors and aromas.

Abstract

In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data science and especially data mining. It is still little used in the field of food and is based on a descriptive modeling of emotions on the basis of human physiological responses.