Analysis on Food Crispness Based on Time and Frequency Domain Features of Acoustic Signal

Abstract

Crispness is an important indicator of crunchy food. However, it cannot be easily quantified by sensory evaluation, due to the high subjectivity of evaluators; instrument measurement of this indicator requires much manpower and time. To improve the efficiency of food crispness prediction, this paper attempts to build a rapid, convenient, and accurate crispness analysis model. Starting with the fracturing sound of crunchy food, the authors collected the fracturing acoustic signal, conducted wavelet denoising, analyzed the eigenvalues in time and frequency domains, and constructed crispness prediction models based on multiple linear regression (MLR) and neural network, respectively. Through fracturing test and acoustic test, cluster analysis was adopted to select the typical eigenvalues of acoustic signal, including the peak amplitude of power spectral density (PSD) curve, amplitude difference, and waveform index. Based on these eigenvalues, a crispness analysis model was established, and used to predict the crispness of four kinds of food, namely, potato, sweet potato, carrot, and turnip. The results show that the BP neural network had a smaller relative error than the MLR; when the threshold was 5%, the BP neural network maintained a prediction accuracy of >90%, and achieved 100% prediction accuracy on two types of food. To sum up, this paper reveals the relationship between the food chewing sound features and food quality, laying the theoretical basis for the research of food chewing sound mechanism.

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

“…Many applications of empirical modelling in the food industry were reported in prior literature. The ANN technique was useful for the determining changes in the water content, protein, and gluten in stored wheat [24], for accurate and rapid prediction of the moisture and fat content of tofu [25], for the development of a crispness prediction model of crunchy food [26], or the estimation of sugar concentration in food products [27]. Chauchard et al [28] proposed the sensor for acidity prediction in grapes based on NIR spectroscopy and Least-Squared Support Vector Machine regression.…”

Correlation between acoustic characteristics and sensory evaluation of puffed‐grain food based on energy analysis

“…The essence of food sounds is the energy transfer, where people perceive variations in sound through air or bone conduction (Jakubczyk et al, 2017). Many studies have explored the association between acoustic signals and food texture (Favalli et al, 2013; Sakurai et al, 2021), such as using the number of acoustic peaks and the level of acoustic pressure to predict potato chip crispness (Xu et al, 2020) and using acoustic signal characteristic parameters of acoustic signals to build a model to predict sensory crispness scores (Chen & Ding, 2021). Furthermore, the progress of alcohol fermentation was monitored using acoustic emission technology (Mamolar‐Domenech et al, 2023).…”

Classification of crispness of food materials by deep neural networks

“…Studies have been conducted to correlate sound crispness and mechanical crispness by evaluating the sound in time domain, acoustic signal amplitude, duration, and number of peaks (Akimoto et al, 2019;Dias-Faceto et al, 2020;Gouyo et al, 2020;O'Shea & Gallagher, 2019), This article was published on AA publication on: 22 July 2023 but, nowadays, with the use of artificial intelligence it is possible to predict the crispness of food more quickly, accurately, and advantageously by performing sound analysis (Chen & Ding, 2021;Liu, Cai, et al, 2021).…”

“…These networks, including back propagation neural networks (BPNNs), feedforward neural networks (FNNs), and multi-layer perceptrons (MLPs), have been used to analyze acoustic signals generated during mechanical tests on food samples. The frequency range of these signals varies, but often falls within 0-20 kHz (Chen & Ding, 2021;Iliassafov & Shimoni, 2007;Kato et al, 2018Kato et al, , 2019aKato et al, , 2019bLiu, Cai, et al, 2021;Liu & Tan, 1999;Liu, Wu, et al, 2021;Przybył et al, 2020;Sanahuja et al, 2018;Srisawas & Jindal, 2003;Świetlicka et al, 2015).…”

“…For instance, Chen and Ding (2021) used a BPNN model to analyze the crispness of vegetables like potatoes and carrots, while Iliassafov and Shimoni (2007) used a similar model to predict the sensory crispness of coated turkey breasts. Kato et al (2018, 2019a, 2019b) employed a simple BPNN model to quantify the texture of snacks such as rice crackers and potato chips.…”