Abstract
Food texture is a human perception occurring during mastication of food. Texture changes in mastication due toreactions or interactions among food components and saliva produced during chewing. This study identified a mathematicalmodel to express facial muscle motion, which in turn predicts food texture. Crispy foods were the primary foods used in thisresearch. An electronic sensing system (ESS) was used for data acquisition and analysis. Log-normal distribution (LND) ofFourier power (FP) of masseter muscle was the best model for texture studies, with regression coefficients (R2) ranging from0.79 to 0.92. Sensory hardness (SH) and fracturability (SF) evaluation models were constructed on the basis of data processingand analysis technique. SH and SF scores were closely related to LND-FP parameters, with R2 values of 0.71 and 0.84,respectively, and standard error estimates (SEE) of 0.75 and 0.63, respectively. The ESS is able to characterize hardness andfracturability of crispy foods and may provide a technique for study of other texture attribute evaluations and texture changesduring mastication.
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