{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:52:49Z","timestamp":1781524369664,"version":"3.54.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NORCE Research AS"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This paper explores the possibility of applying hyperspectral imaging and machine learning in detecting\n                    <jats:italic>infectious<\/jats:italic>\n                    coronavirus in the indoor environment. Currently, the polymerase chain reaction (PCR) test is the leading technique to detect the existence of SARS-CoV-2 virus in humans (serum or nasalpharyngelal specimens). While the test is a gold standard in detection of virus in humans, positive PCR results do not necessarily mean infectious virus is detected, as dead virus can trigger positive results in the nucleic acid amplification test. This paper proposes a novel method which is based on measuring\n                    <jats:italic>live cells being infected by infectious virus<\/jats:italic>\n                    , and has the potential to directly predict the infectiousness of the targeted virus within 48\u00a0hours. The infection status was measured by a hyperspectral camera. A Partial Least Square - Discriminant Analysis (PLS-DA) model was trained on the hyperspectral images to do prediction. We believe this method opens a new avenue to detecting infectious virus in an indoor environment and can be potentially applied in SARS-CoV-2 detections on surfaces, hence used to aid in protection control measures and detecting virus contamination.\n                  <\/jats:p>","DOI":"10.1007\/s42979-026-05101-6","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:00:34Z","timestamp":1781521234000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Clinical Hygiene Ruler on the Basis of Hyperspectral Imaging and Machine Learning: A Proof-of-Concept Model for SARS-CoV-2 Virus Detection"],"prefix":"10.1007","volume":"7","author":[{"given":"Nabil","family":"Belbachir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca Jane","family":"Cox","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julio","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnoud","family":"Jochemsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin Greve-Isdahl","family":"Mohn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juha Ilmari","family":"Vahokoski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars Kristian","family":"Vognild","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8828-7442","authenticated-orcid":false,"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"5101_CR1","doi-asserted-by":"crossref","unstructured":"Van Doremalen N, et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England J Med. 2020;382(16):1564\u20131567.","DOI":"10.1056\/NEJMc2004973"},{"key":"5101_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2021.113004","volume":"178","author":"J Lukose","year":"2021","unstructured":"Lukose J, Chidangil S, George SD. Optical technologies for the detection of viruses like Covid-19: progress and prospects. Biosens Bioelectron. 2021;178:113004.","journal-title":"Biosens Bioelectron"},{"key":"5101_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.clispe.2022.100022","volume":"4","author":"I Chaudhary","year":"2022","unstructured":"Chaudhary I, Jackson N, Denning D, O\u2019Neill L, Byrne HJ. Contributions of vibrational spectroscopy to virology: a review. Clin Spectrosc. 2022;4:100022.","journal-title":"Clin Spectrosc"},{"key":"5101_CR4","first-page":"429","volume":"1","author":"S Roy","year":"2020","unstructured":"Roy S, Perez-Guaita D, Bowden S, Heraud P, Wood B. Spectroscopy goes viral: diagnosis of hepatitis b and c virus infection from human sera using atr-ftir spectroscopy. Clin Spectrosc. 2020;1:429\u201338.","journal-title":"Clin Spectrosc"},{"key":"5101_CR5","doi-asserted-by":"publisher","first-page":"2950","DOI":"10.1021\/acs.analchem.0c04608","volume":"93","author":"VG Barauna","year":"2021","unstructured":"Barauna VG, et al. Ultrarapid on-site detection of sars-cov-2 infection using simple atr-ftir spectroscopy and an analysis algorithm: high sensitivity and specificity. Anal Chem. 2021;93:2950\u20138.","journal-title":"Anal Chem"},{"key":"5101_CR6","doi-asserted-by":"publisher","first-page":"10391","DOI":"10.1021\/acs.analchem.1c00596","volume":"93","author":"A Banerjee","year":"2021","unstructured":"Banerjee A, et al. Rapid classification of covid-19 severity by atr-ftir spectroscopy of plasma samples. Anal Chem. 2021;93:10391\u20136.","journal-title":"Anal Chem"},{"key":"5101_CR7","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3233\/BSI-200203","volume":"9","author":"MC Santos","year":"2020","unstructured":"Santos MC, Morais CL, Lima KM. Atr-ftir spectroscopy for virus identification: a powerful alternative. Biomed Spectrosc Imaging. 2020;9:103\u201318.","journal-title":"Biomed Spectrosc Imaging"},{"key":"5101_CR8","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1021\/acs.analchem.0c04049","volume":"93","author":"L Zhang","year":"2021","unstructured":"Zhang L, et al. Fast screening and primary diagnosis of Covid-19 by atr-ft-ir. Anal Chem. 2021;93:2191\u20139.","journal-title":"Anal Chem"},{"key":"5101_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2021.122916","volume":"237","author":"Z Guleken","year":"2022","unstructured":"Guleken Z, et al. Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications. Talanta. 2022;237:122916.","journal-title":"Talanta"},{"key":"5101_CR10","doi-asserted-by":"publisher","first-page":"13810","DOI":"10.1021\/acs.analchem.2c02337","volume":"94","author":"D Zhang","year":"2022","unstructured":"Zhang D, et al. Integrated system for on-site rapid and safe screening of Covid-19. Anal Chem. 2022;94:13810\u20139.","journal-title":"Anal Chem"},{"key":"5101_CR11","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","volume":"228","author":"AF Goetz","year":"1985","unstructured":"Goetz AF, Vane G, Solomon JE, Rock BN. Imaging spectrometry for earth remote sensing. Science. 1985;228:1147\u201353.","journal-title":"Science"},{"key":"5101_CR12","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/S0925-5214(02)00122-9","volume":"28","author":"C Clark","year":"2003","unstructured":"Clark C, McGlone V, Jordan R. Detection of brownheart in \u2018braeburn\u2019 apple by transmission nir spectroscopy. Postharvest Biol Technol. 2003;28:87\u201396.","journal-title":"Postharvest Biol Technol"},{"key":"5101_CR13","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1081\/ESE-120016905","volume":"38","author":"Y Li","year":"2003","unstructured":"Li Y, Wang J, Huang Z, Xuetie Z. Mapping air contaminant concentrations using remote sensing ftir. J Environ Sci Health. 2003;38:429\u201338.","journal-title":"J Environ Sci Health"},{"key":"5101_CR14","first-page":"90","volume":"7673","author":"M Segal-Rosenheimer","year":"2010","unstructured":"Segal-Rosenheimer M, et al. Feasibility study of detection of hazardous airborne pollutants using passive open-path ftir. Adv Environ Chem Biol Sens Technol VII. 2010;7673:90\u2013102.","journal-title":"Adv Environ Chem Biol Sens Technol VII"},{"key":"5101_CR15","doi-asserted-by":"crossref","unstructured":"Harig R et\u00a0al. Infrared remote sensing of hazardous vapours: surveillance of public areas during the fifa football world cup 2006. SPIE - The International Society for Optical Engineering 2007.","DOI":"10.1117\/12.720227"},{"key":"5101_CR16","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.19.1.010901","volume":"19","author":"G Lu","year":"2014","unstructured":"Lu G, Fei B. Medical hyperspectral imaging: a review. J Biomed Opt. 2014;19:010901.","journal-title":"J Biomed Opt"},{"key":"5101_CR17","doi-asserted-by":"crossref","unstructured":"Ozaki Y, Huck C, Tsuchikawa S & Engelsen SB. Near-infrared spectroscopy. Springer Singapore, S.l. (2021). OCLC: 1221557757.","DOI":"10.1007\/978-981-15-8648-4"},{"key":"5101_CR18","doi-asserted-by":"crossref","unstructured":"Heise HM. Medical applications of nir spectroscopy. In: Near-infrared spectroscopy: theory, spectral analysis, instrumentation, and applications. 2021;437\u201373.","DOI":"10.1007\/978-981-15-8648-4_20"},{"key":"5101_CR19","doi-asserted-by":"publisher","first-page":"16201","DOI":"10.1038\/s41598-021-95756-3","volume":"11","author":"E Gomez-Gonzalez","year":"2021","unstructured":"Gomez-Gonzalez E, et al. Hyperspectral image processing for the identification and quantification of lentiviral particles in fluid samples. Sci Rep. 2021;11:16201.","journal-title":"Sci Rep"},{"key":"5101_CR20","doi-asserted-by":"publisher","first-page":"2356","DOI":"10.1038\/s41598-022-06393-3","volume":"12","author":"E Gomez-Gonzalez","year":"2022","unstructured":"Gomez-Gonzalez E, et al. Optical imaging spectroscopy for rapid, primary screening of sars-cov-2: a proof of concept. Sci Rep. 2022;12:2356.","journal-title":"Sci Rep"},{"key":"5101_CR21","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/s11082-022-03658-z","volume":"54","author":"YH El-Sharkawy","year":"2022","unstructured":"El-Sharkawy YH, Aref MH, Elbasuney S, Radwan SM, El-Sayyad GS. Oxygen saturation measurements using novel diffused reflectance with hyperspectral imaging: towards facile covid-19 diagnosis. Opt Quant Electron. 2022;54:322.","journal-title":"Opt Quant Electron"},{"key":"5101_CR22","doi-asserted-by":"crossref","unstructured":"Gosavi D, Cheatham B, Sztuba-Solinska J. Label-free detection of human coronaviruses in infected cells using enhanced darkfield hyperspectral microscopy (edhm). J Imaging. 2022;8(2):24.","DOI":"10.3390\/jimaging8020024"},{"key":"5101_CR23","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3390\/digital3020010","volume":"3","author":"E Marasco","year":"2023","unstructured":"Marasco E, Ricanek K, Le H. We are also metabolites: towards understanding the composition of sweat on fingertips via hyperspectral imaging. Digital. 2023;3:137\u201345.","journal-title":"Digital"},{"key":"5101_CR24","unstructured":"Gulley JL. Hyperspectral analysis of sweat metabolite biometrics for real-time detection of Covid-19. https:\/\/clinicaltrials.gov\/ct2\/show\/NCT05044780 (2024). Accessed: Jul 7, 2024."},{"issue":"6078","key":"5101_CR25","first-page":"100","volume":"II","author":"LL Randeberg","year":"2006","unstructured":"Randeberg LL, Baarstad I, L\u00f8ke T, Kaspersen P, Svaasand LO. Hyperspectral imaging of bruised skin. Photonic Therapeutics Diagnostics. 2006;II(6078):100\u201310.","journal-title":"Photonic Therapeutics Diagnostics"},{"key":"5101_CR26","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1016\/j.jdent.2014.08.007","volume":"42","author":"P Usenik","year":"2014","unstructured":"Usenik P, B\u00fcrmen M, Fidler A, Pernu\u0161 F, Likar B. Near-infrared hyperspectral imaging of water evaporation dynamics for early detection of incipient caries. J Dent. 2014;42:1242\u20137.","journal-title":"J Dent"},{"key":"5101_CR27","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TBME.2010.2049110","volume":"57","author":"H Akbari","year":"2010","unstructured":"Akbari H, Kosugi Y, Kojima K, Tanaka N. Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging. IEEE Trans Biomed Eng. 2010;57:2011\u20137.","journal-title":"IEEE Trans Biomed Eng"},{"key":"5101_CR28","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1111\/j.1349-7006.2011.01849.x","volume":"102","author":"H Akbari","year":"2011","unstructured":"Akbari H, Uto K, Kosugi Y, Kojima K, Tanaka N. Cancer detection using infrared hyperspectral imaging. Cancer Sci. 2011;102:852\u20137.","journal-title":"Cancer Sci"},{"key":"5101_CR29","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3390\/s120100162","volume":"12","author":"Z Liu","year":"2011","unstructured":"Liu Z, Wang H, Li Q. Tongue tumor detection in medical hyperspectral images. Sensors. 2011;12:162\u201374.","journal-title":"Sensors"},{"key":"5101_CR30","doi-asserted-by":"publisher","first-page":"430","DOI":"10.3390\/s18020430","volume":"18","author":"H Fabelo","year":"2018","unstructured":"Fabelo H, et al. An intraoperative visualization system using hyperspectral imaging to aid in brain tumor delineation. Sensors. 2018;18:430.","journal-title":"Sensors"},{"key":"5101_CR31","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.57309","volume":"9","author":"YM Bar-On","year":"2020","unstructured":"Bar-On YM, Flamholz A, Phillips R, Milo R. Science forum: Sars-cov-2 (Covid-19) by the numbers. Elife. 2020;9:e57309.","journal-title":"Elife"},{"key":"5101_CR32","doi-asserted-by":"crossref","unstructured":"Siragam V et\u00a0al. Seasonal human coronaviruses oc43, 229e, and nl63 induce cell surface modulation of entry receptors and display host cell-specific viral replication kinetics. Microbiol Spectrum 2024;12(7):e04220\u201323.","DOI":"10.1128\/spectrum.04220-23"},{"key":"5101_CR33","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1099\/jgv.0.001453","volume":"101","author":"NS Ogando","year":"2020","unstructured":"Ogando NS, et al. Sars-coronavirus-2 replication in vero e6 cells: replication kinetics, rapid adaptation and cytopathology. J Gen Virol. 2020;101:925\u201340.","journal-title":"J Gen Virol"},{"key":"5101_CR34","unstructured":"Prediketera. Hyperspectal imaging made easy your software solution. https:\/\/prediktera.com\/wp-content\/uploads\/2022\/08\/Prediktera-Breeze-2206.pdf (2024). Accessed: Jul 7, 2024."},{"key":"5101_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-8283-3","volume-title":"New perspectives in partial least squares and related methods","author":"H Abdi","year":"2013","unstructured":"Abdi H, Chin WW, Vinzi VE, Russolillo G, Trinchera L. New perspectives in partial least squares and related methods. New York: Springer; 2013."},{"key":"5101_CR36","doi-asserted-by":"crossref","unstructured":"LeCun Y. Deep learning hardware: past, present, and future. 2019 IEEE International Solid-State Circuits Conference - (ISSCC) 2019;12\u20139.","DOI":"10.1109\/ISSCC.2019.8662396"},{"key":"5101_CR37","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. The IEEE conference on computer vision and pattern recognition 2016;770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"5101_CR38","unstructured":"Vaswani A. Advances in neural information processing systems. 2017;30 (Attention is all you need)."},{"key":"5101_CR39","doi-asserted-by":"crossref","unstructured":"Carion N et\u00a0al. End-to-end object detection with transformers. European conference on computer vision 2020;213\u201329.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"5101_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2020.3041864","volume":"19","author":"T Dam","year":"2020","unstructured":"Dam T, Anavatti SG, Abbass HA. Mixture of spectral generative adversarial networks for imbalanced hyperspectral image classification. IEEE Geosci Remote Sens Lett. 2020;19:1\u20135.","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5101_CR41","doi-asserted-by":"crossref","unstructured":"Dam T. Latent preserving generative adversarial network for imbalance classification. In: 2022 IEEE International Conference on Image Processing (ICIP). 2022;3712\u20136.","DOI":"10.1109\/ICIP46576.2022.9897874"},{"key":"5101_CR42","first-page":"1","volume":"20","author":"GY Lee","year":"2023","unstructured":"Lee GY, Dam T, Ferdaus MM, Poenar DP, Duong VN. Watt-effnet: a lightweight and accurate model for classifying aerial disaster images. IEEE Geosci Remote Sens Lett. 2023;20:1\u20135.","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5101_CR43","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18 2015;234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"5101_CR44","doi-asserted-by":"crossref","unstructured":"Goodwin M, et al. Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook$$\\dagger $$. ICES J Mar Sci. 2022;79:319\u201336.","DOI":"10.1093\/icesjms\/fsab255"},{"key":"5101_CR45","doi-asserted-by":"publisher","first-page":"6988","DOI":"10.1007\/s10489-020-02154-9","volume":"52","author":"KM Knausg\u00e5rd","year":"2022","unstructured":"Knausg\u00e5rd KM, et al. Temperate fish detection and classification: a deep learning based approach. Appl Intell. 2022;52:6988\u20137001.","journal-title":"Appl Intell"},{"key":"5101_CR46","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/BF02174528","volume":"1","author":"RD Cramer","year":"1993","unstructured":"Cramer RD. Partial least squares (pls): its strengths and limitations. Perspect Drug Discovery Des. 1993;1:269\u201378.","journal-title":"Perspect Drug Discovery Des"},{"key":"5101_CR47","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.dss.2012.11.015","volume":"54","author":"S-C Carlos","year":"2013","unstructured":"Carlos S-C, Bego\u00f1a G-N. Partial least square discriminant analysis (pls-da) for bankruptcy prediction. Decis Support Syst. 2013;54:1245\u201355.","journal-title":"Decis Support Syst"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-05101-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-026-05101-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-05101-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:00:50Z","timestamp":1781521250000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-026-05101-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["5101"],"URL":"https:\/\/doi.org\/10.1007\/s42979-026-05101-6","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"15 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Publication is approved.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"561"}}