{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:23:52Z","timestamp":1784172232913,"version":"3.55.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Plan","award":["No. 2018YFC1200203"],"award-info":[{"award-number":["No. 2018YFC1200203"]}]},{"name":"Shanghai Science and Technology Project in 2020","award":["No. 20040501500"],"award-info":[{"award-number":["No. 20040501500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 6217070290"],"award-info":[{"award-number":["No. 6217070290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s11063-021-10652-1","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T09:21:36Z","timestamp":1633944096000},"page":"677-690","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["One-Dimensional Deep Convolutional Neural Network for Mineral Classification from Raman Spectroscopy"],"prefix":"10.1007","volume":"54","author":[{"given":"Xiancheng","family":"Sang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ri-gui","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaochong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengjun","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"issue":"6","key":"10652_CR1","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1366\/0003702991947324","volume":"53","author":"V Allen","year":"1999","unstructured":"Allen V, Kalivas JH, Rodriguez RG (1999) Post-consumer plastic identification using Raman spectroscopy. Appl Spectrosc 53(6):672\u2013681. https:\/\/doi.org\/10.1366\/0003702991947324","journal-title":"Appl Spectrosc"},{"issue":"12","key":"10652_CR2","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.1002\/jrs.4919","volume":"47","author":"G Barone","year":"2016","unstructured":"Barone G, Bersani D, Lottici PP, Mazzoleni P, Raneri S, Longobardo U (2016) Red gemstone characterization by micro-Raman spectroscopy: the case of rubies and their imitations. J Raman Spectrosc 47(12):1534\u20131539. https:\/\/doi.org\/10.1002\/jrs.4919","journal-title":"J Raman Spectrosc"},{"issue":"10","key":"10652_CR3","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1002\/jrs.4757","volume":"46","author":"C Carey","year":"2015","unstructured":"Carey C, Boucher T, Mahadevan S, Bartholomew P, Dyar MD (2015) Machine learning tools for mineral recognition and classification from Raman spectroscopy. J Raman Spectrosc 46(10):894\u2013903. https:\/\/doi.org\/10.1002\/jrs.4757","journal-title":"J Raman Spectrosc"},{"issue":"2","key":"10652_CR4","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1366\/0003702001949168","volume":"54","author":"H Chung","year":"2000","unstructured":"Chung H, Ku MS (2000) Comparison of near-infrared infrared, and Raman spectroscopy for the analysis of heavy petroleum products. Appl Spectrosc 54(2):239\u2013245. https:\/\/doi.org\/10.1366\/0003702001949168","journal-title":"Appl Spectrosc"},{"key":"10652_CR5","doi-asserted-by":"publisher","DOI":"10.1111\/j.1750-3841.2010.01619.x","author":"C Fan","year":"2010","unstructured":"Fan C, Hu Z, Riley LK, Purdy GA, Mustapha A, Lin M (2010) Detecting food- and waterborne viruses by surface-enhanced Raman spectroscopy. J Food Sci. https:\/\/doi.org\/10.1111\/j.1750-3841.2010.01619.x","journal-title":"J Food Sci"},{"issue":"5","key":"10652_CR6","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1039\/c8an02212g","volume":"144","author":"X Fan","year":"2019","unstructured":"Fan X, Ming W, Zeng H, Zhang Z, Lu H (2019) Deep learning-based component identification for the Raman spectra of mixtures. Analyst 144(5):1789\u20131798. https:\/\/doi.org\/10.1039\/c8an02212g","journal-title":"Analyst"},{"key":"10652_CR7","doi-asserted-by":"publisher","first-page":"2414","DOI":"10.1016\/j.bios.2010.03.033","volume":"25","author":"S Feng","year":"2010","unstructured":"Feng S, Chen R, Lin J, Pan J, Chen G, Li Y, Cheng M, Huang Z, Chen J, Zeng H (2010) Nasopharyngeal cancer detection based on blood plasma surface-enhanced Raman spectroscopy and multivariate analysis. Biosens Bioelectron 25:2414\u20139","journal-title":"Biosens Bioelectron"},{"key":"10652_CR8","doi-asserted-by":"publisher","first-page":"187401","DOI":"10.1103\/PhysRevLett.97.187401","volume":"97","author":"AC Ferrari","year":"2006","unstructured":"Ferrari AC, Meyer JC, Scardaci V, Casiraghi C, Lazzeri M, Mauri F, Piscanec S, Jiang D, Novoselov KS, Roth S, Geim AK (2006) Raman spectrum of graphene and graphene layers. Phys Rev Lett 97:187401. https:\/\/doi.org\/10.1103\/PhysRevLett.97.187401","journal-title":"Phys Rev Lett"},{"key":"10652_CR9","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE. https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10652_CR10","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.fuel.2017.05.101","volume":"206","author":"X He","year":"2017","unstructured":"He X, Liu X, Nie B, Song D (2017) FTIR and Raman spectroscopy characterization of functional groups in various rank coals. Fuel 206:555\u2013563. https:\/\/doi.org\/10.1016\/j.fuel.2017.05.101","journal-title":"Fuel"},{"key":"10652_CR11","doi-asserted-by":"publisher","first-page":"2111","DOI":"10.1177\/0003702817695571","volume":"71","author":"S Khan","year":"2017","unstructured":"Khan S, Ullah R, Khan A, Sohail A, Wahab N, Bilal M, Ahmed M (2017) Random forest-based evaluation of Raman spectroscopy for dengue fever analysis. Appl Spectrosc 71:2111\u20132117","journal-title":"Appl Spectrosc"},{"issue":"6","key":"10652_CR12","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"10652_CR13","doi-asserted-by":"crossref","unstructured":"Lafuente B, Downs RT, Yang H, Stone N (2015) 1. The power of databases: the RRUFF project. In: Highlights in mineralogical crystallography. De Gruyter (O), pp 1\u201330. https:\/\/doi.org\/10.1515\/9783110417104-003","DOI":"10.1515\/9783110417104-003"},{"issue":"11","key":"10652_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"issue":"2","key":"10652_CR15","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1002\/jrs.5770","volume":"51","author":"W Lee","year":"2019","unstructured":"Lee W, Lenferink AT, Otto C, Offerhaus HL (2019) Classifying Raman spectra of extracellular vesicles based on convolutional neural networks for prostate cancer detection. J Raman Spectrosc 51(2):293\u2013300. https:\/\/doi.org\/10.1002\/jrs.5770","journal-title":"J Raman Spectrosc"},{"key":"10652_CR16","doi-asserted-by":"publisher","first-page":"4067","DOI":"10.1039\/C7AN01371J","volume":"142","author":"J Liu","year":"2017","unstructured":"Liu J, Osadchy M, Ashton L, Foster M, Solomon C, Gibson S (2017) Deep convolutional neural networks for Raman spectrum recognition: a unified solution. Analyst 142:4067\u20134074","journal-title":"Analyst"},{"key":"10652_CR17","doi-asserted-by":"publisher","first-page":"e1122","DOI":"10.1002\/mbo3.1122","volume":"9","author":"M Maruthamuthu","year":"2020","unstructured":"Maruthamuthu M, Raffiee A, De OD, Ardekani A, Verma M (2020) Raman spectra-based deep learning: a tool to identify microbial contamination. Microbiologyopen 9:e1122","journal-title":"Microbiologyopen"},{"issue":"48","key":"10652_CR18","doi-asserted-by":"publisher","first-page":"18898","DOI":"10.1073\/pnas.0708596104","volume":"104","author":"BD Piorek","year":"2007","unstructured":"Piorek BD, Lee SJ, Santiago JG, Moskovits M, Banerjee S, Meinhart CD (2007) Free-surface microfluidic control of surface-enhanced Raman spectroscopy for the optimized detection of airborne molecules. Proc Natl Acad Sci 104(48):18898\u201318901. https:\/\/doi.org\/10.1073\/pnas.0708596104","journal-title":"Proc Natl Acad Sci"},{"key":"10652_CR19","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.coal.2016.04.001","volume":"159","author":"G Rantitsch","year":"2016","unstructured":"Rantitsch G, L\u00e4mmerer W, Fisslthaler E, Mitsche S, Kaltenb\u00f6ck H (2016) On the discrimination of semi-graphite and graphite by Raman spectroscopy. Int J Coal Geol 159:48\u201356. https:\/\/doi.org\/10.1016\/j.coal.2016.04.001","journal-title":"Int J Coal Geol"},{"key":"10652_CR20","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: Computer science. arXiv:1409.1556v6. Accessed on Tue, 06 July 2021"},{"key":"10652_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE. https:\/\/doi.org\/10.1109\/cvpr.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"5","key":"10652_CR22","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.bjp.2014.09.004","volume":"24","author":"W Toma","year":"2014","unstructured":"Toma W, Guimar\u00e3es LL, Brito AR, Santos AR, Cortez FS, Pusceddu FH, Cesar A, J\u00fanior LS, Pacheco MT, Pereira CD (2014) Safflower oil: an integrated assessment of phytochemistry antiulcerogenic activity, and rodent and environmental toxicity. Rev Bras Farmacogn 24(5):538\u2013544. https:\/\/doi.org\/10.1016\/j.bjp.2014.09.004","journal-title":"Rev Bras Farmacogn"},{"issue":"1","key":"10652_CR23","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1002\/jrs.5750","volume":"51","author":"R Zhang","year":"2019","unstructured":"Zhang R, Xie H, Cai S, Hu Y, Liu GK, Hong W, Tian ZQ (2019) Transfer-learning-based Raman spectra identification. J Raman Spectrosc 51(1):176\u2013186. https:\/\/doi.org\/10.1002\/jrs.5750","journal-title":"J Raman Spectrosc"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10652-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10652-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10652-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T16:22:09Z","timestamp":1645806129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10652-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,11]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["10652"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10652-1","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,11]]},"assertion":[{"value":"30 September 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"There are no conflicts to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}