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A Multidimensional Tactile Feature Information Fusion Method for Food Graininess Evaluation
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DOI：10.1109/JSTSP.2026.3665106.png)
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Abstract
En 中文
Conventional texture instruments rely on limited feature parameters, which poses challenges for the accurate quantitative evaluation of food graininess. In this study, a multidimensional tactile feature fusion method was proposed to assess food graininess, by using a self-developed tactile information acquisition system. A total of 180 sets of tactile friction and vibration data were collected based on six types of food-saliva mixture samples. Four machine learning algorithms were applied for feature extraction, and the convolutional neural network (CNN) exhibited the best performance, enabling the identification of nine key tactile friction and vibration features. These features, together with sensory evaluation scores, were utilized for correlation analysis, qualitative discrimination, and quantitative prediction. The results demonstrated that tactile vibration features exhibited a stronger correlation with sensory graininess than friction features. Principal component analysis (PCA) effectively discriminated food samples with different graininess levels, achieving an accuracy exceeding 95.8%. Furthermore, graininess was accurately predicted by a stepwise multiple linear regression (SMLR) model (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R_{p} = 0.889$</tex-math></inline-formula>, RMSEP = 0.622). Overall, these findings confirm the effectiveness of multidimensional tactile feature information fusion methods for evaluating food graininess and highlight their potential for the quantitative characterization of food texture.
Keywords:
Food graininess
tactile friction
tactile vibration
feature fusion
quantitative evaluation
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