Quantitative taste evaluation of total enteral nutrients

Area of Science:

• Nutritional Science
• Sensory Science
• Biomedical Engineering

Background:

• Enteral nutrition is crucial for patient recovery.
• Palatability significantly impacts patient compliance with enteral nutrition.
• Quantitative taste evaluation methods are needed for product development.

Purpose of the Study:

• To quantitatively evaluate the taste profiles of enteral nutrients available in Japan.
• To compare human gustatory sensation test results with artificial taste sensor data.
• To explore the potential of taste sensors in predicting enteral nutrient palatability.

Main Methods:

• Human gustatory sensation tests using the semantic differential (SD) method for basic tastes and palatability.
• Factor analysis to identify overall palatability indicators.
• Principal component analysis (PCA) on taste sensor data for product classification.
• Correlation analysis between human sensory data and taste sensor outputs.

Main Results:

• Overall palatability positively correlated with sweetness and sourness, negatively with bitterness and saltiness.
• Flavor addition to an amino acid-based nutrient significantly improved palatability, likely due to sour components reducing bitterness.
• Taste sensor predictions for sweetness, sourness, and overall palatability showed high accuracy compared to human tests.
• PCA effectively classified enteral nutrients into groups based on their composition and taste profiles.

Conclusions:

• Artificial taste sensors can accurately predict the taste and palatability of enteral nutrients.
• Taste sensors offer a valuable tool for developing and improving enteral nutrition products.
• Enhanced palatability through taste sensor guidance can improve patient compliance with enteral nutrition therapy.