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Psychophysical Tracking Method to Measure Taste Preferences in Children and Adults

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Published on: July 16, 2016

A machine learning method for juice human sensory hedonic prediction using electronic sensory features.

Huihui Yang^1,2, Yutang Wang¹, Jinyong Zhao¹

¹Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, PR China.

Current Research in Food Science

|September 11, 2023

Summary

This study introduces a novel method using fused electronic sensory analysis and artificial neural networks to predict fruit juice appeal. This technology shows potential for replacing human senses in sensory evaluation.

Keywords:

Artificial neural network Electronic sensory Human sensory Prediction

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Area of Science:

Food Science and Technology
Sensory Science
Artificial Intelligence

Background:

Human sensory evaluation is crucial for assessing food product quality and consumer acceptance.
Traditional sensory methods can be subjective, time-consuming, and expensive.
Electronic sensory (e-sensory) technologies offer objective and rapid alternatives.

Purpose of the Study:

To develop and validate a method combining fused e-sensory technology with artificial neural networks (ANNs) for predicting human sensory hedonic responses to fruit juice.
To establish a predictive model linking e-sensory features to human sensory attributes and acceptance.
To explore the potential of e-sensory fusion as a replacement for human sensory panels.

Main Methods:

Fused electronic sensory analysis technology was employed to capture fruit juice characteristics.
Artificial neural networks were utilized to model the relationship between e-sensory data and human sensory evaluations.
Human sensory evaluation was conducted using Quantitative Descriptive Analysis (QDA) and a scoring test method.

Main Results:

The initial modeling of fused e-sensory features with human sensory attributes yielded R² values of 0.77 (QDA) and 0.63 (scoring test).
The final model predicting human sensory hedonic from fused e-sensory features achieved high accuracy, with R² values of 0.95 (Model-1) and 0.88 (Model-2).
Root Mean Square Error (RMSE) values were low, indicating good predictive performance (e.g., 0.04 for Model-1).

Conclusions:

The fusion of e-sensory technologies with ANNs provides a robust and accurate method for predicting human sensory hedonic responses in fruit juice.
This approach demonstrates significant potential to replace or supplement traditional human sensory panels, offering objectivity and efficiency.
The study paves the way for developing advanced devices capable of simultaneous, multi-sensory analysis.