Study on light scattering characteristics of micro-nano particles based on SSA-BP model

Study on light scattering characteristics of micro-nano particles based on SSA-BP model | 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 Study on light scattering characteristics of micro-nano particles based on SSA-BP model Peng Xie, Hongzhi Guo, Jiamei Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8718906/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background. Accurate prediction of micro-nano particle concentration and size distribution in industrial environments is of great significance for mitigating dust explosion risks, especially in confined spaces. Traditional approaches, including light scattering methods and mechanistic models, are constrained by their inability to effectively handle the complex non-linear correlations between particle properties and environmental variables. Thus, there is an imperative demand for a non-contact, real-time technique to characterize micro-nano particles. Methods. A multi-layer backpropagation neural network (BP-NN) is proposed in this study to characterize the complex interdependencies between micro-nano particle properties (i.e., size, concentration) and optical parameters (i.e., extinction coefficient, scattered light intensity, refractive index). To improve the network’s prediction accuracy and avoid local optima, the Sparrow Search Algorithm (SSA) is incorporated to optimize the initial weights and thresholds of the BP-NN. Furthermore, the random forest (RF) algorithm is utilized to quantify the relative importance of input parameters, providing insights into their regulatory effects on prediction performance. Results. Although the BP-NN outperforms conventional cubic function methods, it suffers from prediction errors exceeding 5% for specific particle sizes of 1.16 µm, 1.30 µm, and 2.47 µm, which exerts a negative impact on the accuracy of concentration estimation. The SSA-BP hybrid model, by contrast, delivers substantially enhanced performance: it reduces micro-nano particle size prediction errors to within 3% and achieves highly robust concentration estimation with the training set showing RMSE = 0.6825 and R² = 0.9624, and the test set attaining RMSE = 0.2434 and R² = 0.9865. Additionally, RF-based feature importance analysis reveals that the refractive index (importance score of 0.74) and scattered light intensity of 0.6 are the dominant factors influencing concentration prediction, alongside particle size of 0.6, whereas the extinction coefficient of 0.2 has a negligible effect. Conclusion. These findings underscore the feasibility of weak-light scattering techniques for safe optical characterization in flammable and explosive industrial settings. Through the integration of neural networks with metaheuristic optimization algorithms, this study enhances the precision of micro-nano particle monitoring, presenting a scalable and reliable solution for industrial safety applications—especially in mitigating dust explosion hazards via rapid, non-contact optical detection. Furthermore, the identification of dominant optical parameters delivers actionable insights to streamline detection system configurations and prioritize key variables in industrial monitoring practices. Full Text Additional Declarations No competing interests reported. Supplementary Files rawdatacodes.zip Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 19 Feb, 2026 Editor invited by journal 09 Feb, 2026 Editor assigned by journal 31 Jan, 2026 Submission checks completed at journal 31 Jan, 2026 First submitted to journal 28 Jan, 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. 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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-8718906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594824608,"identity":"86e6df9d-865c-4015-908f-87d1bcc6a5a9","order_by":0,"name":"Peng Xie","email":"","orcid":"","institution":"Beijing Academy of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Xie","suffix":""},{"id":594824609,"identity":"77462f9f-c526-4c4c-9349-1b6f74a84d38","order_by":1,"name":"Hongzhi Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYHACNjBpwMzYcCDBQEKOjb39ALFamBsPPCiwMObjOZNApBYG9uaDDz5UJM6TcDDAq14+IvnYg487au3N2SEOS2+TYEhg+FGxDacWwxtp6YYzzxxntmyGaMltk248wNhz5jZuLTNyzKR5246xGRyGaZE5kMDM2EZYCw9MSzqbRIIBXi3yEmAtNRIwLQkEtRjwPEuTnNl2wACmxbANGMgH8flFvj35mMTHtjp7g/PHH3/88adOXr69/eCDHxV4bDkApg6jih7AqR5kSwOYqsOnZhSMglEwCkY6AABm1Fq2uQCrjgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Academy of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Guo","suffix":""},{"id":594824610,"identity":"b99342a1-4277-4c49-92fb-288dfb3da6cc","order_by":2,"name":"Jiamei Zhao","email":"","orcid":"","institution":"Beijing Academy of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiamei","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-01-28 09:59:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8718906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8718906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103210460,"identity":"94a83c41-dd8e-4240-b1d2-3c05157dbc79","added_by":"auto","created_at":"2026-02-23 08:27:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298965,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8718906/v1_covered_a85fcd35-f2bb-4891-9dd2-34f9467659e5.pdf"},{"id":103210438,"identity":"d7998063-bdb5-4942-ab5b-242c5ca0e05c","added_by":"auto","created_at":"2026-02-23 08:27:04","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1035371,"visible":true,"origin":"","legend":"","description":"","filename":"rawdatacodes.zip","url":"https://assets-eu.researchsquare.com/files/rs-8718906/v1/3ff371136b4ccd068e616f85.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on light scattering characteristics of micro-nano particles based on SSA-BP model","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8718906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8718906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eAccurate prediction of micro-nano particle concentration and size distribution in industrial environments is of great significance for mitigating dust explosion risks, especially in confined spaces. Traditional approaches, including light scattering methods and mechanistic models, are constrained by their inability to effectively handle the complex non-linear correlations between particle properties and environmental variables. Thus, there is an imperative demand for a non-contact, real-time technique to characterize micro-nano particles.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eA multi-layer backpropagation neural network (BP-NN) is proposed in this study to characterize the complex interdependencies between micro-nano particle properties (i.e., size, concentration) and optical parameters (i.e., extinction coefficient, scattered light intensity, refractive index). To improve the network\u0026rsquo;s prediction accuracy and avoid local optima, the Sparrow Search Algorithm (SSA) is incorporated to optimize the initial weights and thresholds of the BP-NN. Furthermore, the random forest (RF) algorithm is utilized to quantify the relative importance of input parameters, providing insights into their regulatory effects on prediction performance.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eAlthough the BP-NN outperforms conventional cubic function methods, it suffers from prediction errors exceeding 5% for specific particle sizes of 1.16 \u0026micro;m, 1.30 \u0026micro;m, and 2.47 \u0026micro;m, which exerts a negative impact on the accuracy of concentration estimation. The SSA-BP hybrid model, by contrast, delivers substantially enhanced performance: it reduces micro-nano particle size prediction errors to within 3% and achieves highly robust concentration estimation with the training set showing RMSE\u0026thinsp;=\u0026thinsp;0.6825 and R\u0026sup2; = 0.9624, and the test set attaining RMSE\u0026thinsp;=\u0026thinsp;0.2434 and R\u0026sup2; = 0.9865. Additionally, RF-based feature importance analysis reveals that the refractive index (importance score of 0.74) and scattered light intensity of 0.6 are the dominant factors influencing concentration prediction, alongside particle size of 0.6, whereas the extinction coefficient of 0.2 has a negligible effect.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eThese findings underscore the feasibility of weak-light scattering techniques for safe optical characterization in flammable and explosive industrial settings. Through the integration of neural networks with metaheuristic optimization algorithms, this study enhances the precision of micro-nano particle monitoring, presenting a scalable and reliable solution for industrial safety applications\u0026mdash;especially in mitigating dust explosion hazards via rapid, non-contact optical detection. Furthermore, the identification of dominant optical parameters delivers actionable insights to streamline detection system configurations and prioritize key variables in industrial monitoring practices.\u003c/p\u003e","manuscriptTitle":"Study on light scattering characteristics of micro-nano particles based on SSA-BP model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 08:25:56","doi":"10.21203/rs.3.rs-8718906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T11:33:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T03:56:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T13:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181976007950713698523991841749984103139","date":"2026-03-02T18:35:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203640272969719497011784663607464435390","date":"2026-03-02T10:11:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159139530680850047645946697775651861003","date":"2026-02-21T08:17:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284677738889270103717956907049041052524","date":"2026-02-19T07:29:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T06:34:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-09T12:40:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-31T09:28:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-31T09:27:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Computing","date":"2026-01-28T08:20:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bcbf7b66-8372-45af-b072-e5cb67ee2bae","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T06:09:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 08:25:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8718906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8718906","identity":"rs-8718906","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}