Effectively saltiness enhanced odorants screening and prediction by database establish, sensory evaluation and deep...

Area of Science:

• Food Science
• Sensory Science
• Computational Chemistry

Background:

• Odor-taste interactions are crucial for flavor perception.
• Enhancing saltiness perception through aroma is a promising strategy for salt reduction in foods.

Purpose of the Study:

• To identify specific odorants that enhance saltiness perception.
• To develop a predictive model for saltiness-enhancing abilities of aroma compounds.

Main Methods:

• Systematic analysis of volatile compounds in salty foods.
• Sensory evaluation to determine saltiness enhancement concentrations.
• Development of a deep learning model (SELF-referencing Embedded Strings-based representation leaning, graph attention network, and Backpropagation Neural Network) for predicting odorant efficacy.

Main Results:

• Ketones, pyrazines, and sulfur-containing compounds demonstrated significant saltiness-enhancing potential.
• Mushroom and fatty aroma attributes were associated with enhanced saltiness.
• The deep learning model achieved high accuracy (95.93%) in predicting saltiness enhancement.

Conclusions:

• Specific aroma compounds and attributes can effectively enhance saltiness perception.
• A deep learning approach provides an efficient method for screening potential saltiness-enhancing agents.
• This research offers novel strategies for the food industry to achieve salt reduction goals.