A comparison of food crispness based on the cloud model

Area of Science:

• Food science
• Acoustics
• Data modeling

Background:

• The cloud model transforms qualitative concepts into quantitative data.
• Its application in food texture analysis, particularly crispness, is underexplored.
• Traditional methods for texture evaluation have limitations.

Purpose of the Study:

• To apply the cloud model for comparing food crispness.
• To investigate the correlation between acoustic signals and crispness.
• To validate the cloud model's efficacy against established methods.

Main Methods:

• Recorded acoustic signals during compression of carrots, radishes, potatoes, apples, and pears.
• Extracted time-domain features: sound intensity, maximum short-time frame energy, and waveform index.
• Utilized the cloud model (Ex value) for quantitative crispness assessment.

Main Results:

• Established a crispness order: carrot > potato > white radish > Fuji apple > crystal pear.
• Confirmed the cloud model's feasibility through mechanical and sensory evaluations.
• Found a negative correlation between microstructure parameters and crispness (p < .01).

Conclusions:

• The cloud model offers a viable and accurate method for food crispness comparison.
• This approach surpasses traditional mechanical and sensory evaluations in accuracy.
• The cloud model has broad potential for diverse texture studies in food science.