Multivariate and machine learning approach to honey adulteration: Integrating Principal Component Analysis and artificial neural network models

Multivariate and machine learning approach to honey adulteration: Integrating Principal Component Analysis and artificial neural network models | 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 Research Article Multivariate and machine learning approach to honey adulteration: Integrating Principal Component Analysis and artificial neural network models Sukhmanjot Kaur, Sandhya Singh, Kirti Kumari, Gurveer Kaur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9060387/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 Honey adulteration is a major issue in India. It affects product quality and can pose health risks to consumers. This study examined the concentration of adulterants' impacts on honey quality using four types from different floral sources. The samples were mixed with brown rice syrup, corn syrup, and malt syrup at levels of 10%, 20%, and 30%. We analyzed nine physicochemical properties: moisture content, color, pH, electrical conductivity, density, viscosity, total soluble solids (TSS), hydroxymethylfurfural (HMF) content, and diastase activity. The results showed moisture content ranging from 15.23% to 18.00%, pH between 3.77 and 4.20, color ΔE from 20.71 to 29.71, TSS between 75.07°Bx and 80.83°Bx, electrical conductivity from 0.20 to 0.32 mS/cm, viscosity ranging from 5.51 to 16.80 Pa·s, density from 1.35 to 1.44 kg/m³, HMF content between 17.24 and 21.57 mg/kg, and diastase activity from 10.74 to 13.56. The analysis of variance (ANOVA) showed significant increases in HMF content and pH with the addition of adulterants. At the same time, viscosity, diastase activity, and electrical conductivity decreased significantly (P<0.05). Principal Component Analysis (PCA) found three main components that accounted for 83.5% of the variability in the data. Electrical conductivity, pH, color, and diastase activity were the key factors that distinguished the samples. An Artificial Neural Network (ANN) model using feed-forward backpropagation showed high accuracy (R²=0.96 for training, R²=0.94 for testing). It highlighted HMF content, pH, and diastase activity as the most important parameters for detecting adulteration. This combined multivariate machine learning approach offers a strong method for assessing honey quality and detecting adulteration Adulteration Electrical conductivity HMF content Physico-chemical Total soluble content Principal Component Analysis Artificial Neural Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 May, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 07 Mar, 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-9060387","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602759800,"identity":"c7c0f14b-d2b1-4e64-8416-afa4a6270b0b","order_by":0,"name":"Sukhmanjot Kaur","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sukhmanjot","middleName":"","lastName":"Kaur","suffix":""},{"id":602759801,"identity":"ebbf3f0a-4aa4-4d29-9a8f-3826c3c4e9d8","order_by":1,"name":"Sandhya Singh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sandhya","middleName":"","lastName":"Singh","suffix":""},{"id":602759802,"identity":"7aae2883-8d52-492c-9f4e-a38633fbdcbe","order_by":2,"name":"Kirti Kumari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACCTBpkMDDwMDY+PhHBZDDzNxApBY2xmZjhjMgLYzEaGFIYGBgY2CTZmwDcQhokZyRnfi5oiBNxnx+c5t04bzaaP52oJYfFdtwapGWyN0secYgh0fmGGOz9cxtx3NnHGZsYOw5cxunFjmJ3A2SDQYVPBJsjI03eLcdy20AamFmbMOrZfNPqJYGCd45x3LnE9ICdNg2oC05IC1N0rwNNbkbCGmR7Hm7zbLBIA2oJbHZcMaxA7kbgVoO4vOLxPHczTcb/iTbSzAff/jgQ01d7rzzhw8++FGBWws6OAwmDxCtHgjqSFE8CkbBKBgFIwQAAH5JV5LNuI1PAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Kirti","middleName":"","lastName":"Kumari","suffix":""},{"id":602759803,"identity":"bc22d4c7-6677-42e4-b0dd-85f6a1c0e1dc","order_by":3,"name":"Gurveer Kaur","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gurveer","middleName":"","lastName":"Kaur","suffix":""}],"badges":[],"createdAt":"2026-03-07 18:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9060387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9060387/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104298842,"identity":"6c8fd815-bc4e-4a9a-98a6-1930735e4712","added_by":"auto","created_at":"2026-03-10 08:27:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":881324,"visible":true,"origin":"","legend":"","description":"","filename":"NewResearchPaperNEW.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9060387/v1_covered_2b8e91a2-b81c-4d26-9db7-a9c8e9287452.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multivariate and machine learning approach to honey adulteration: Integrating Principal Component Analysis and artificial neural network models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adulteration, Electrical conductivity, HMF content, Physico-chemical, Total soluble content, Principal Component Analysis, Artificial Neural Networks","lastPublishedDoi":"10.21203/rs.3.rs-9060387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9060387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHoney adulteration is a major issue in India. It affects product quality and can pose health risks to consumers. This study examined the concentration of adulterants' impacts on honey quality using four types from different floral sources. The samples were mixed with brown rice syrup, corn syrup, and malt syrup at levels of 10%, 20%, and 30%. We analyzed nine physicochemical properties: moisture content, color, pH, electrical conductivity, density, viscosity, total soluble solids (TSS), hydroxymethylfurfural (HMF) content, and diastase activity. The results showed moisture content ranging from 15.23% to 18.00%, pH between 3.77 and 4.20, color ΔE from 20.71 to 29.71, TSS between 75.07°Bx and 80.83°Bx, electrical conductivity from 0.20 to 0.32 mS/cm, viscosity ranging from 5.51 to 16.80 Pa·s, density from 1.35 to 1.44 kg/m³, HMF content between 17.24 and 21.57 mg/kg, and diastase activity from 10.74 to 13.56. The analysis of variance (ANOVA) showed significant increases in HMF content and pH with the addition of adulterants. At the same time, viscosity, diastase activity, and electrical conductivity decreased significantly (P\u0026lt;0.05). Principal Component Analysis (PCA) found three main components that accounted for 83.5% of the variability in the data. Electrical conductivity, pH, color, and diastase activity were the key factors that distinguished the samples. An Artificial Neural Network (ANN) model using feed-forward backpropagation showed high accuracy (R²=0.96 for training, R²=0.94 for testing). It highlighted HMF content, pH, and diastase activity as the most important parameters for detecting adulteration. This combined multivariate machine learning approach offers a strong method for assessing honey quality and detecting adulteration\u003c/p\u003e","manuscriptTitle":"Multivariate and machine learning approach to honey adulteration: Integrating Principal Component Analysis and artificial neural network models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 08:24:42","doi":"10.21203/rs.3.rs-9060387/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-02T14:32:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T11:46:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173561413333167388864985398643260961061","date":"2026-04-09T17:33:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49556697212542548401249931265811925778","date":"2026-04-08T12:01:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295583441625879381312822522331020086053","date":"2026-04-08T07:49:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T10:15:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T08:28:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T08:27:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Analytical Methods","date":"2026-03-07T18:35:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7d79f34f-dbf1-4b7c-a329-86e513603328","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-02T14:32:41+00:00","index":23,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T10:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 08:24:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9060387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9060387","identity":"rs-9060387","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}