{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T01:50:43Z","timestamp":1767837043446,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,23]],"date-time":"2020-02-23T00:00:00Z","timestamp":1582416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)\u2014the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)\u2014are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.<\/jats:p>","DOI":"10.3390\/s20041223","type":"journal-article","created":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T03:33:43Z","timestamp":1582515223000},"page":"1223","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees"],"prefix":"10.3390","volume":"20","author":[{"given":"Zhong","family":"Zheng","sequence":"first","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China"},{"name":"School of Data Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China"},{"name":"Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information, Ministry of Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-4724-0234","authenticated-orcid":false,"given":"Jinxing","family":"Yu","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China"},{"name":"Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information, Ministry of Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Guo","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China"},{"name":"Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information, Ministry of Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Zhangzhong","sequence":"additional","affiliation":[{"name":"National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China"},{"name":"Key Laboratory for Quality Testing of Hardware and Software Products on Agricultural Information, Ministry of Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1002\/(SICI)1097-0010(199802)76:2<263::AID-JSFA943>3.0.CO;2-F","article-title":"Use of Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression for the Detection of Adulteration of Strawberry Purees","volume":"76","author":"Holland","year":"1998","journal-title":"J. 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