Snack Texture Estimation System Using a Simple Equipment and Neural Network Model

Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as “crunchiness” and “crispness”. Experimental results validate the model’s capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed.

[1]  Aren Jansen,et al.  CNN architectures for large-scale audio classification , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Shoji Terasaki,et al.  Texture Evaluation of Cucumber by a New Acoustic Vibration Method , 2005 .

[3]  Gustavo V. Barbosa-Cánovas,et al.  Rheology for the food industry , 2005 .

[4]  Shervin Minaee,et al.  Ad-Net: Audio-Visual Convolutional Neural Network for Advertisement Detection In Videos , 2018, ArXiv.

[5]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[6]  Shervin Minaee,et al.  Iris recognition using scattering transform and textural features , 2015, 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE).

[7]  Jinglu Tan,et al.  ACOUSTIC WAVE ANALYSIS FOR FOOD CRISPNESS EVALUATION , 1999 .

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Tom J. Moir,et al.  Robust audio surveillance using spectrogram image texture feature , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  桜井 直樹,et al.  Evaluation of 'Fuyu' Persimmon Texture by a New Parameter, "Sharpness index" , 2005 .

[11]  Ryuji Ito,et al.  The estimation system of food texture considering sound and load using neural networks , 2017, 2017 International Conference on Biometrics and Kansei Engineering (ICBAKE).

[12]  J. Meullenet,et al.  Prediction of Rice Sensory Texture Attributes from a Single Compression Test, Multivariate Regression, and a Stepwise Model Optimization Method , 2001 .

[13]  Shervin Minaee,et al.  Multispectral palmprint recognition using textural features , 2014, 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[14]  Dong-Chen He,et al.  Texture Unit, Texture Spectrum, And Texture Analysis , 1990 .

[15]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[16]  Ryuji Ito,et al.  Texture Estimation System of Snacks Using Neural Network Considering Sound and Load , 2018, 3PGCIC.

[17]  Kaoru Kohyama,et al.  Classification of Japanese Texture Terms , 2013 .

[18]  L. Duizer.  A review of acoustic research for studying the sensory perception of crisp, crunchy and crackly textures , 2001 .

[19]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..

[20]  Kevin L. Priddy,et al.  Dealing with Limited Amounts of Data , 2005 .

[21]  Mitsuru Taniwaki,et al.  Development of Method for Quantifying Food Texture Using Blanched Bunching Onions , 2006 .

[22]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[23]  Loris Nanni,et al.  Combining visual and acoustic features for audio classification tasks , 2017, Pattern Recognit. Lett..

[24]  岩谷 真一郎,et al.  新しいパラメーター「シャープネス指標」によるカキの肉質評価 , 2005 .

[25]  Futoshi Kobayashi,et al.  A Study on Classification of Food Texture with Recurrent Neural Network , 2016, ICIRA.

[26]  V. Jindal,et al.  ACOUSTIC TESTING OF SNACK FOOD CRISPNESS USING NEURAL NETWORKS , 2003 .