{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:53:53Z","timestamp":1775040833042,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T00:00:00Z","timestamp":1552003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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 \u201ccrunchiness\u201d and \u201ccrispness\u201d. Experimental results validate the model\u2019s 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.<\/jats:p>","DOI":"10.3390\/fi11030068","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T11:21:59Z","timestamp":1552044119000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Snack Texture Estimation System Using a Simple Equipment and Neural Network Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7275-7748","authenticated-orcid":false,"given":"Shigeru","family":"Kato","sequence":"first","affiliation":[{"name":"Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Wada","sequence":"additional","affiliation":[{"name":"Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryuji","family":"Ito","sequence":"additional","affiliation":[{"name":"Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takaya","family":"Shiozaki","sequence":"additional","affiliation":[{"name":"Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudai","family":"Nishiyama","sequence":"additional","affiliation":[{"name":"Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomomichi","family":"Kagawa","sequence":"additional","affiliation":[{"name":"Niihama College, National Institute of Technology, Niihama City, Ehime Prefecture 792-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0924-2244(01)00050-4","article-title":"A Review of Acoustic Research for Studying the Sensory Perception of Crisp, Crunchy, and Crackly Textures","volume":"12","author":"Duizer","year":"2001","journal-title":"Trends Food Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1111\/jtxs.12006","article-title":"Classification of Japanese Texture Terms","volume":"44","author":"Hayakawa","year":"2013","journal-title":"J. Texture Stud."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"31","DOI":"10.2503\/jjshs.74.31","article-title":"Texture Evaluation of Cucumber by a New Acoustic Vibration Method","volume":"74","author":"Sakurai","year":"2005","journal-title":"J. Jpn. Soc. Hortic. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150","DOI":"10.2503\/jjshs.74.150","article-title":"Evaluation of \u2018Fuyu\u2019 Persimmon Texture by a New Parameter, Sharpness index","volume":"74","author":"Sakurai","year":"2005","journal-title":"J. Jpn. Soc. Hortic. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"410","DOI":"10.2503\/jjshs.75.410","article-title":"Development of Method for Quantifying Food Texture Using Blanched Bunching Onions","volume":"75","author":"Taniwaki","year":"2006","journal-title":"J. Jpn. Soc. Hortic. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1111\/j.1745-4603.1999.tb00227.x","article-title":"Acoustic Wave Analysis for Food Crispness Evaluation","volume":"30","author":"Liu","year":"1999","journal-title":"J. Texture Stud."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1111\/j.1745-4603.2003.tb01072.x","article-title":"Acoustic Testing of Snack Food Crispness Using Neural Networks","volume":"34","author":"Srisawas","year":"2003","journal-title":"J. Texture Stud."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kato, S., Wada, N., Murakami, N., Ito, R., Kondo, R., and Goto, Y. (2017, January 15\u201317). The Estimation System of Food Texture Considering Sound and Load Using Neural Networks. Proceedings of the 2017 International Conference on Biometrics and Kansei Engineering, Kyoto, Japan.","DOI":"10.1109\/ICBAKE.2017.8090622"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Okada, S., Nakamoto, H., Kobayashi, F., and Kojima, F. (2016, January 22\u201324). A Study on Classification of Food Texture with Recurrent Neural Network. Proceedings of the Intelligent Robotics and Applications (ICIRA 2016), Lecture Notes in Computer Science, Tokyo, Japan.","DOI":"10.1007\/978-3-319-43506-0_21"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kato, S., Wada, N., Ito, R., Shiozaki, T., Nishiyama, Y., and Kagawa, T. (2018, January 27\u201329). Texture Estimation System of Snacks Using Neural Network Considering Sound and Load. Proceedings of the 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2018), Lecture Notes on Data Engineering and Communications Technologies, Taichung, Taiwan.","DOI":"10.1007\/978-3-030-02607-3_5"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.jfoodeng.2004.05.062","article-title":"Rheology for the food industry","volume":"67","year":"2005","journal-title":"J. Food Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TGRS.1990.572934","article-title":"Texture Unit, Texture Spectrum, And Texture Analysis","volume":"28","author":"He","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sharan, R.V., and Moir, T.J. (2015, January 19\u201324). Robust audio surveillance using spectrogram image texture feature. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178312"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.patrec.2017.01.013","article-title":"Combining visual and acoustic features for audio classification tasks","volume":"88","author":"Nanni","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","article-title":"Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification","volume":"24","author":"Salamon","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Minaee, S., and Abdolrashidi, A. (2014, January 13). Multispectral palmprint recognition using textural features. Proceedings of the 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2014.7002964"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Minaee, S., Abdolrashidi, A., and Wang, Y. (2015, January 9\u201312). Iris recognition using scattering transform and textural features. Proceedings of the 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP\/SPE), Salt Lake City, UT, USA.","DOI":"10.1109\/DSP-SPE.2015.7369524"},{"key":"ref_19","unstructured":"Minaee, S., Bouazizi, I., Kolan, P., and Najafzadeh, H. (arXiv, 2018). Ad-Net: Audio-Visual Convolutional Neural Network for Advertisement Detection In Videos, arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R.J., and Wilson, K. (2017, January 5\u20139). CNN architectures for large-scale audio classification. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1111\/j.1365-2621.2001.tb15593.x","article-title":"Prediction of Rice Sensory Texture Attributes from a Single Compression Test, Multivariate Regression, and a Stepwise Model Optimization Method","volume":"66","author":"Sesmat","year":"2001","journal-title":"J. Food Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Priddy, K.L., and Keller, P.E. (2005). Artificial Neural Networks\u2014An Introduction, SPIE Press. Chapter 11.","DOI":"10.1117\/3.633187"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance evaluation of classification algorithms by k-fold and leave-one-out cross-validation","volume":"48","author":"Wong","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_25","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Alex","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","unstructured":"(2019, February 28). MathWorks, Transfer Learning Using AlexNet. Available online: https:\/\/www.mathworks.com\/help\/deeplearning\/examples\/transfer-learning-using-alexnet.html?lang=en."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/3\/68\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:37:25Z","timestamp":1760186245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/3\/68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,8]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["fi11030068"],"URL":"https:\/\/doi.org\/10.3390\/fi11030068","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,8]]}}}