{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:30:13Z","timestamp":1750185013650,"version":"3.40.5"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program of China","award":["2018YFC0809200"],"award-info":[{"award-number":["2018YFC0809200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,5,28]]},"abstract":"<jats:p>In order to improve the accuracy of amino acid identification, a model based on the convolutional neural network (CNN) and bidirectional gated recurrent network (BiGRU) is proposed for terahertz spectrum identification of amino acids. First, we use the CNN to extract the feature information of the terahertz spectrum; then, we use the BiGRU to process the feature vector of the amino acid time-domain spectrum, describe the time series dynamic change information, and finally achieve amino acid identification through the fully connected network. Experiments are carried out on the terahertz spectra of various amino acids. The experimental results show that the CNN-BiGRU model proposed in this study can effectively realize the terahertz spectrum identification of amino acids and will provide a new and effective analysis method for the identification of amino acids by terahertz spectroscopy technology.<\/jats:p>","DOI":"10.1155\/2021\/2097257","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T18:05:16Z","timestamp":1622311516000},"page":"1-7","source":"Crossref","is-referenced-by-count":6,"title":["A Method of Amino Acid Terahertz Spectrum Recognition Based on the Convolutional Neural Network and Bidirectional Gated Recurrent Network Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9568-2707","authenticated-orcid":true,"given":"Tao","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-2400","authenticated-orcid":true,"given":"Yuanyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiliang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianan","family":"He","sequence":"additional","affiliation":[{"name":"Central Laboratory of Health Quarantine, Shenzhen International Travel Health Care Center and Shenzhen Academy of Inspection and Quarantine, Shenzhen Customs District, Shenzhen 518033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-7357","authenticated-orcid":true,"given":"Shiming","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/22.989974"},{"issue":"10","key":"2","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.tibtech.2016.04.008","article-title":"Biomedical applications of terahertz spectroscopy and imaging","volume":"34","author":"Y. 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