[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124190-en":3,"doc-seo-124190-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124190,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Biological contaminants analysis in microalgae culture by UV–vis spectroscopy and machine learning","This study evaluates UV–visible spectroscopy for detecting and characterizing biological contaminants in microalgae cultures, enhanced with machine learning methods. Biological contamination, especially by flagellates and rotifers, threatens entire cultures and thus commercial viability. Traditional monitoring techniques such as microscopy and cytometry are time-consuming, dependent on specialized expertise, and often insufficiently specific. UV–vis spectroscopy leverages pigment chemistry to provide characteristic spectra, enabling automated, real-time monitoring and earlier intervention.","This document is downloaded from the VTT Research Information Portal  \n[https://cris.vtt.fi](https://cris.vtt.fi)  \nVTT Technical Research Centre of Finland  \nBiological contaminants analysis in microalgae culture by UV–vis spectroscopy and machine learning  \nPaiva, Eduardo Maia; Hyttinen, Eevi; Dönsberg, Timo; Barth, Dorothee  \nPublished in:  \nSpectrochimica Acta-Part A: Molecular and Biomolecular Spectroscopy  \nDOI:  \n10.1016/j.saa.2024.125690  \nPublished: 05/04/2025  \nDocument Version  \nPublisher's final version  \nLicense CC BY  \nLink to publication  \nPlease cite the original version:  \nPaiva, E. M. , Hyttinen, E. , Dönsberg, T. , & Barth, D. (2025) . Biological contaminants analysis in microalgae culture by UV–vis spectroscopy and machine learning. Spectrochimica Acta-Part A: Molecular and  \nBiomolecular Spectroscopy, 330, Article 125690. [https://doi.org/10.1016/j.saa.2024.125690](https://doi.org/10.1016/j.saa.2024.125690)  \nVTT  \n[https://www.vttresearch.com](https://www.vttresearch.com)  \nVTT Technical Research Centre of Finland Ltd  \n[P.O. box 1000](P.O. box 1000)[ ](P.O. box 1000)[FI-02044 VTT](FI-02044 VTT)[ ](FI-02044 VTT)Finland  \nBy using VTT Research Information Portal you are bound by the following Terms & Conditions.  \nI have read and I understand the following statement:  \nThis document is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of this document is not permitted, except duplication for research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered for sale.  \nDownload date: 13. May. 2025  \nSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 330 (2025) 125690  \nContents lists available at ScienceDirect  \nSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy  \njournal [homepage: www.journals.elsevier.com/spectrochimica-acta-part-a](homepage: www.journals.elsevier.com/spectrochimica-acta-part-a)molecular-and-biomolecular-spectroscopy  \n| Biological contaminants analysis in microalgae culture by UV–vis spectroscopy and machine learning |  |  |\n| --- | --- | --- |\n| Eduardo Maia Paivaa,*, Eevi Hyttinen b, Timo D¨onsberg a, Dorothee Barth b\u003Cbr>a VTT Technical Research Centre of Finland, Tekniikantie 1, 02150 Espoo, Finland\u003Cbr>b VTT Technical Research Centre of Finland Ltd, Tietotie 2, P.O. Box 1000, 02044 Espoo, Finland |  |  |\n| H I G H L I G H T S\u003Cbr>• UV–vis absorption spectra of microalgae, flagellates and rotifers from 200 to 800 nm.\u003Cbr>• Biopigments UV–vis absorption spectra as probes to identify flagellates and rotifers contaminating microalgae culture.\u003Cbr>• UV–visible spectroscopy band assignment of microalgae, flagellates and rotifers based on their biopigments.\u003Cbr>• Machine Learning spectral analysis based on PCA of ternary mixtures.\u003Cbr>• Chemometric analysis based on score maps and loading plots from PCA. A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Microalgae\u003Cbr>UV–visible spectroscopy Machine learning Biological contaminants Flagellate\u003Cbr>Rotifer | G R A P H I C A L A B S T R A C T\u003Cbr>A B S T R A C T\u003Cbr>This study elucidates the utility and efficacy of UV–visible spectroscopy for the detection and characterization of biological contaminants within microalgae cultures, augmented by machine learning algorithms. Biological contamination, exemplified by flagellates and rotifers, poses a significant concern due to its potential to rapidly devastate entire cultures, thus jeopardizing commercial viability. Conventional analytical methods for monitoring contamination, such as microscopy and cytometry, are often labor-intensive, reliant on specialized expertise for microorganism identification, and may lack specificity in discerning the nature of contamination, impeding timely intervention. UV–visible spectroscopy offers a compelling solution by overcoming many of these challenges, affording specificity in analysis, real-time mo","cbCaivtfJ6tDahKz","https://ap.wps.com/l/cbCaivtfJ6tDahKz","pdf",3554634,1,10,"English","en",105,"# Introduction\n## Microalgae cultivation and contamination risks\n## Limits of conventional monitoring methods\n## UV–vis spectroscopy as an analytical alternative\n## Machine learning for spectral interpretation","[{\"question\":\"What biological contaminants are addressed in the study?\",\"answer\":\"The study focuses on biological contaminants exemplified by flagellates and rotifers that can infiltrate microalgae cultures.\"},{\"question\":\"Why can existing monitoring methods be insufficient?\",\"answer\":\"Conventional approaches like microscopy and cytometry are labor-intensive, require specialized expertise, and may lack specificity for distinguishing the type of contamination.\"},{\"question\":\"How does UV–vis spectroscopy help detect contamination?\",\"answer\":\"UV–vis spectroscopy captures distinct absorption spectra driven by the biopigment chemistry of microalgae and contaminant organisms, supporting specific and automated monitoring.\"}]","Biological contaminants analysis in microalgae culture by UV–vis spectroscopy and machine learning | 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