# Snack Texture Estimation System Using a Simple Equipment and Neural Network Model +
## 1 Introduction  
Food texture is a typical sensory experience influencing an individual's preference or distaste of a food product. The relationship between sensory perception and texture, described by the adjectives such as \"crispy\", \"crunchy\", and \"crackly\", has been analyzed previously [1]. According to [PERSON] [2], there are 445 terms related to texture in the Japanese language. For example, \"crunchy\" is equivalently represented by several onomatopoeic words, including \"Kali-Kali\" or \"Boli-Boli\". Japanese people might be comparatively sensitive to the texture of foods. In Japan, snack texture is typically examined by human inspectors and quality evaluators of manufacturing firms. In the process, sensory evaluation is based on the person's sensibility. In cases of discrepancy, an artificial Intelligence (AI) might be supportive in finalizing the decision of an evaluation task. For example, given differing evaluations of the human inspectors, the AI can propose objective numerical values in the form of \"crispness = 0.8\" or \"crunchiness = 0.5\". With such criteria, an agreement would be met among the inspectors, thus alleviating their burden in the course. AI-based systems, such as a neural network model, are potentially capable of learning from a large amount of data, while simultaneously inferring moderate estimation; more available data provide a more precise estimation. Therefore, such intelligent systems could be effective aids in the management and inspection of food texture quality. Research on automatic food-texture-estimation methods has been conducted previously. [PERSON] et al. [3; 4; 5] proposed a method for texture diagnosis, which analyzes the sound of a sharpmetal probe stabbing the food for texture estimation. [PERSON] and [PERSON] [6] applied a neural network model to evaluate the \"crispness\" score of snacks by magnifying the crushing sound with a microphone; they crushed the snacks using a pair of pliers by hand and inputted the produced sound signal features to the neural network model. Moreover, [PERSON] and [PERSON] [7] used a neural network to estimate the grade of snacks by estimating \"crispness\", the snack was manually crushed with a pair of pincers, and then the resulting sound was inputted to the neural network. While several food-texture-estimation studies [3; 4; 5; 6; 7] inferred texture considering only sound, humans tend to evaluate texture by also considering the load on their teeth. The load signal is as equally important as sound in texture estimation. We therefore developed novel equipment capable of examining both signals simultaneously. We applied a neural network model for numerical texture level inference for vegetables such as cucumbers and radishes, in terms of \"munch-ness\" and \"crunchiness\" [8]. Similarly, [PERSON] and [PERSON] [9] developed a human-tooth-imitating-sensor that could sense the vibration and load on the tooth, complemented by a recurrent neural network model, which inferred the numerical classification value of the snacks or the sweets into \"biscuits\", \"gummy candy\", or \"corn snack\". Conversely, our study does not aim to categorize snacks but to quantify the texture level of their \"crunchiness\" or \"crispness\" within the numerical range (0,1), and develop simple and durable equipment and an intelligent model for the texture level estimation. The load sensor used in our previous equipment [8] was very narrow and fragile, as shown in Figure 1a, and could accommodate only small food specimens. The current paper developed equipment built from the ground up [10]. Figure 1b illustrates the system, in which the load sensor is attached to the top of the probe for examining differently sized food specimens. Rheometers are commonly used in food manufacturing companies for measuring the force response of the food [11]; nevertheless, rheometers are not built with a sound sensor, unlike our equipment. Therefore, we could regard our equipment as a next-generation rheometer capable of observing load change and sound signals simultaneously.