Food texture is one of the important quality indicators in foodstuffs, along with appearance and flavor, contributing to taste and odor. This study proposes a novel magnetic food texture sensor that corresponds to the tactile sensory capacity of the human tooth. The sensor primarily consists of a probe, linear slider, spring, and circuit board. The probe has a cylindrical shape and includes a permanent magnet. Both sides of the spring are fixed to the probe and circuit board. The linear slider enables the smooth, single-axis motion of the probe during food compression. Two magnetoresistive elements and one inductor on the circuit board measured the probe’s motion. A measurement system then translates the measurement data collected by the magnetoresistive elements into compression force by means of a calibration equation. Fundamental experiments were performed to evaluate the range, resolution, repetitive durability of force, and differences in the frequency responses. Furthermore, the sensor was used to measure seven types of chicken nuggets with different coatings. The difference between the force and vibration measurement data is revealed on the basis of the discrimination rate of the nuggets.
Crispness is one of the words most frequently used to describe the texture of fried or dried food in addition to being a key to the determination of freshness for many non-fried foods. In this study, a new feature value called the sum of variance was assessed for its contribution to the estimation of crispness. Dynamic time warping and its averaging algorithms were employed to determine the sum of variance from a set of sequential force data measured using an instrument. The sum of variance is a feature value that expresses the variance of multiple sequential data. In an experiment, seven chicken nugget samples were prepared, and five panels evaluated their texture according to six Japanese word descriptors. An instrument experiment determined the six feature values, including the sum of variance from the measurement data, whereas multiple linear regression was applied to determine the relationship between the sensory values and feature values. For three of the six textures, the sum of variance reduced the error between the sensory values and their estimated values by up to 50%, confirming that this feature contributes to the textural estimation of food crispness.
Food texture is one of the most important factors in determining the personal palatability of foods, so food companies require food texture measurement and evaluation in developing novel food products. However, instruments are not fast enough or strong enough to imitate human mastication. To measure the textures of different foods, this study proposes a sensor stand that uses a rod-type actuator. The target speed and force of the sensor stand are 100 mm/s and 100 N, respectively. A food texture sensor that imitates the structure of a human tooth is attached to the sensor stand. The sensor stand and a desktop computer make up the measurement system. Using the system, the fundamental characteristics of the sensor stand with the texture sensor are demonstrated. Verification experiments confirm that the sensor stand satisfies the target values of speed and force. Experiments on actual food items also demonstrate the effectiveness of the measurement system in evaluating food textures.
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