Pressure distribution-based texture sensing by using a simple artificial mastication system.

Area of Science:

• Food Science and Technology
• Biomedical Engineering
• Materials Science

Background:

• Human texture perception involves simultaneous evaluation of mechanical and geometrical changes during mastication.
• Objective and quantitative texture analysis is crucial for developing specialized food products, like nursing-care gels.
• Current methods often fail to capture the complex sensory experience of food texture.

Purpose of the Study:

• To develop a novel texture sensing method for nursing-care gels using an artificial mastication system.
• To objectively and quantitatively evaluate both mechanical and geometrical characteristics of gels during simulated mastication.
• To establish a reliable model correlating objective measurements with human sensory perception of texture.

Main Methods:

• An artificial mastication system was employed to simulate the human chewing process.
• Pressure distribution of gel samples was measured during simulated mastication to acquire geometrical and mechanical data.
• Image texture analysis was used to numerically model the relationship between pressure distribution data and sensory evaluation values.

Main Results:

• The proposed method successfully acquired simultaneous information on geometrical and mechanical characteristics of gels.
• A numerical model was developed using image texture analysis to link pressure data with sensory perception.
• The model accurately estimated sensory evaluation values for nine different gel types, achieving a coefficient of determination > 0.93.

Conclusions:

• The artificial mastication system provides an objective and quantitative method for assessing gel texture.
• The developed texture sensing method effectively predicts human sensory evaluations, validating its efficacy.
• This approach holds significant potential for quality control and product development in the food industry, particularly for specialized gels.