Endometriosis detection using blood plasma through data fusion of Fourier transform infrared spectroscopy and high- performance liquid chromatography

Endometriosis detection using blood plasma through data fusion of Fourier transform infrared spectroscopy and high- performance liquid chromatography | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Endometriosis detection using blood plasma through data fusion of Fourier transform infrared spectroscopy and high- performance liquid chromatography Sanoop Pavithran Mattukarathi, Clint Mathew, Dr. Rekha Upadhya, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9824623/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract A minimally invasive detection method is highly desirable for endometriosis due to its long diagnostic delay, non-specific symptoms, and invasive diagnostic procedures. This study explored the synergetic application of vibrational spectroscopy (ATR-FTIR) and protein profile (HPLC-FLD) analysis of blood plasma samples for the detection of endometriosis. The different feature selection tools, such as genetic algorithm (GA), variable importance in projection (VIP) and random forest variable importance (RFVI), were adapted to screen the most significant variables among the whole data. The machine learning models, such as support vector machines (SVM), K-Nearest Neighbours (KNN) and shallow neural network (NN), were applied to the data for the classification of endometriosis from control. The protein profile data with common features selected out of feature selection tools such as GA and VIP, and KNN as a classifier resulted in an accuracy of 93.75%, sensitivity of 85.7%, specificity of 100% and area under the ROC curve of 0.99. Furthermore, the explainable AI tool SHAP (Shapley additive explanations) values was used to identify the blood plasma fractions responsible for classifying endometriosis relative to controls. The untargeted mass spectrometry analysis of these blood plasma fractions revealed a panel of proteins that were responsible for the classification of endometriosis and probable protein biomarkers of endometriosis. A more comprehensive diagnostic model was developed using a data fusion technique. The Mid-level data fusion (extracting PLS-DA score) was applied on the ATR-FTIR and HPLC-FLD data set, which resulted in an excellent classification accuracy of 100% with sensitivity and specificity of 100% and an AUC of ROC 1. This study suggests that a multimodal analytical approach with suitable data fusion can be a promising tool for minimally invasive endometriosis detection. Biological sciences/Biological techniques Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Blood plasma FTIR spectroscopy HPLC-Fluorescence detection Data fusion Endometriosis Machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files Supportinginformationforpublication.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Jul, 2026 Reviews received at journal 08 Jul, 2026 Reviewers agreed at journal 29 Jun, 2026 Reviewers agreed at journal 17 Jun, 2026 Reviewers invited by journal 17 Jun, 2026 Editor assigned by journal 17 Jun, 2026 Editor invited by journal 16 Jun, 2026 Submission checks completed at journal 09 Jun, 2026 First submitted to journal 09 Jun, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9824623","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":661460151,"identity":"7644172e-4146-4657-b212-0353a2888123","order_by":0,"name":"Sanoop Pavithran Mattukarathi","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Sanoop","middleName":"Pavithran","lastName":"Mattukarathi","suffix":""},{"id":661460155,"identity":"4f04580e-9208-4b40-b7b3-879ad162eac3","order_by":1,"name":"Clint Mathew","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Clint","middleName":"","lastName":"Mathew","suffix":""},{"id":661460157,"identity":"9a966bff-7ae7-4691-9b7e-3c2a06524912","order_by":2,"name":"Dr. Rekha Upadhya","email":"","orcid":"","institution":"Kasturba Medical College, Manipal","correspondingAuthor":false,"prefix":"Dr.","firstName":"Rekha","middleName":"","lastName":"Upadhya","suffix":""},{"id":661460161,"identity":"eae65c9d-60a6-4839-b8b3-0a7264393e66","order_by":3,"name":"Dr. Anjali Suneel Mundkur","email":"","orcid":"","institution":"Kasturba Medical College, Manipal","correspondingAuthor":false,"prefix":"Dr.","firstName":"Anjali","middleName":"Suneel","lastName":"Mundkur","suffix":""},{"id":661460162,"identity":"dfca264d-ce34-4502-b840-19b6667d9c38","order_by":4,"name":"Jacinta S. D’Souza","email":"","orcid":"","institution":"University of Mumbai-Department of Atomic Energy Centre for Excellence in Basic Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jacinta","middleName":"S.","lastName":"D’Souza","suffix":""},{"id":661460165,"identity":"7687483c-7ab5-4a3e-905d-bc58376e3d3d","order_by":5,"name":"Muraleedhar V pai","email":"","orcid":"","institution":"Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University","correspondingAuthor":false,"prefix":"","firstName":"Muraleedhar","middleName":"V","lastName":"pai","suffix":""},{"id":661460168,"identity":"35c83d65-a4d5-4922-823c-c90197c75655","order_by":6,"name":"Santhosh Chidangil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBAC+QYGBmYgnQCkGhg+AFlsDAyMByCC2IHBAQbGZogWxgbGGRAtDPi1MCBpYeaBiuLXwn72+OOCiro8BvaDzZ9tc+zy+KSbHxxgqLBObMDll568xOYZZw4XM/AktknnbksuZpM5BnTtmXScWhgO5Bg287YdSGyQYGxjzt3GnNgmkWBwgLHtMG4t59+AtNSBtDR/ttxWD9SS/uEA4z88Wm6AbWEGaWmQZtx2GKglB2hLA24tBjfeGM7mOQNUCfSLZO+244ltMmcKDiQcSzfG6f3+HIPPPBV1if3shw9/+LmtOnH+7PaNDz7UWMvidBgMsMFZEgzgaCIFSJCmfBSMglEwCoY/AADiiF9h/iPU0AAAAABJRU5ErkJggg==","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Santhosh","middleName":"","lastName":"Chidangil","suffix":""}],"badges":[],"createdAt":"2026-05-26 10:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9824623/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9824623/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":112952848,"identity":"8d56ee72-a9d2-48a9-a98a-6180e759e904","added_by":"auto","created_at":"2026-06-24 14:38:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":734288,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportresearcharticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9824623/v1_covered_d0e7e220-9580-4d2e-9cb0-36b9e9f3c584.pdf"},{"id":112832673,"identity":"2689f0d3-7255-4325-9cd7-8b2f0d902915","added_by":"auto","created_at":"2026-06-23 12:52:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":319721,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformationforpublication.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9824623/v1/cf5b5e9f50e8cf337b003d1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEndometriosis detection using blood plasma through data fusion of Fourier transform infrared spectroscopy and high- performance liquid chromatography\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blood plasma, FTIR spectroscopy, HPLC-Fluorescence detection, Data fusion, Endometriosis, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-9824623/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9824623/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA minimally invasive detection method is highly desirable for endometriosis due to its long diagnostic delay, non-specific symptoms, and invasive diagnostic procedures. This study explored the synergetic application of vibrational spectroscopy (ATR-FTIR) and protein profile (HPLC-FLD) analysis of blood plasma samples for the detection of endometriosis. The different feature selection tools, such as genetic algorithm (GA), variable importance in projection (VIP) and random forest variable importance (RFVI), were adapted to screen the most significant variables among the whole data. The machine learning models, such as support vector machines (SVM), K-Nearest Neighbours (KNN) and shallow neural network (NN), were applied to the data for the classification of endometriosis from control. The protein profile data with common features selected out of feature selection tools such as GA and VIP, and KNN as a classifier resulted in an accuracy of 93.75%, sensitivity of 85.7%, specificity of 100% and area under the ROC curve of 0.99. Furthermore, the explainable AI tool SHAP (Shapley additive explanations) values was used to identify the blood plasma fractions responsible for classifying endometriosis relative to controls. The untargeted mass spectrometry analysis of these blood plasma fractions revealed a panel of proteins that were responsible for the classification of endometriosis and probable protein biomarkers of endometriosis. A more comprehensive diagnostic model was developed using a data fusion technique. The Mid-level data fusion (extracting PLS-DA score) was applied on the ATR-FTIR and HPLC-FLD data set, which resulted in an excellent classification accuracy of 100% with sensitivity and specificity of 100% and an AUC of ROC 1. This study suggests that a multimodal analytical approach with suitable data fusion can be a promising tool for minimally invasive endometriosis detection.\u003c/p\u003e","manuscriptTitle":"Endometriosis detection using blood plasma through data fusion of Fourier transform infrared spectroscopy and high- performance liquid chromatography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-06-23 12:52:48","doi":"10.21203/rs.3.rs-9824623/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"178961390369516487108608503225884676710","date":"2026-07-08T13:30:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-07-08T09:38:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200807039142423944756300327510283077263","date":"2026-06-30T01:31:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177447515740678573922490824119089483444","date":"2026-06-17T12:02:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-06-17T08:02:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-06-17T07:51:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-06-16T05:24:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-06-09T21:58:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-06-09T09:16:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fce0a2a-3c09-4e15-914a-4bc36b5f4e7b","owner":[],"postedDate":"June 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"178961390369516487108608503225884676710","date":"2026-07-08T13:30:48+00:00","index":50,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-07-08T09:38:23+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"200807039142423944756300327510283077263","date":"2026-06-30T01:31:48+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":70767521,"name":"Biological sciences/Biological techniques"},{"id":70767522,"name":"Health sciences/Biomarkers"},{"id":70767523,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":70767524,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-06-23T12:52:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-06-23 12:52:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9824623","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9824623","identity":"rs-9824623","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}