[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125557-en":3,"doc-seo-125557-105":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},125557,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Automatic classification of Candida species using Raman spectroscopy and machine learning","Raman spectroscopy offers rapid, inexpensive molecular analysis that addresses the complexity and time burden of traditional microbiological identification. This study combines Raman spectroscopy with machine learning to automatically identify eleven Candida species, major causes of worldwide fungal infections. Raman spectra were collected from over 220 measurements of dried drops from pure cultures using a confocal Raman microscope with 532 nm excitation. After spectral preprocessing, multiple ML and deep learning models with hyperparameter optimization were trained to maximize accuracy while minimizing overfitting. A 1-D CNN achieved over 80% overall accuracy with good generalization.","Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 290 (2023) 122270  \nContents lists available at ScienceDirect  \nSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy  \njournal [homepage:](homepage: www.journals.elsevier.com/spectrochimica-acta-part-a)[ www.journals.elsevier.com/spectrochimica-acta-part-a](homepage: www.journals.elsevier.com/spectrochimica-acta-part-a)molecular-and-biomolecular-spectroscopy  \nAutomatic classification of Candida species using Raman spectroscopy and   machine learning  \nMaría Gabriela Fern´andez-Mantecaa, 1, *, Alain A. Ocampo-Sosaa, b, 1,  \nCarlos Ruiz de Alegría-Puiga, b, c, María Pía Roiza, b, Jorge Rodríguez-Grande a, b, Fidel Madrazoa, Jorge Calvoa, b, c, Luis Rodríguez-Cobo a, d, e, Jos´e Miguel L´opez-Higuera a, d, e,  \nMaría Carmen Fari˜nas a, c, f, g, Adolfo Cobo a, d, e, *  \na Instituto de Investigaci´on Sanitaria Valdecilla (IDIVAL), Santander, Spain  \nb Servicio de Microbiología, Hospital Universitario Marqu´es de Valdecilla, Santander, Spain c CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain d Photonics Engineering Group, Universidad de Cantabria, Santander, Spain  \ne CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain f Servicio de Enfermedades Infecciosas, Hospital Universitario Marqu´es de Valdecilla, Santander, Spain g Departamento de Medicina y Psiquiatría, Universidad de Cantabria, Santander, Spain  \nH I G H L I G H T S  \n• Traditional methods of microbiological identification are complex and timeconsuming.  \n• Raman spectroscopy solves these problems as it is a fast and cheap technique that does not require sample preparation.  \n• Raman spectroscopy combined with machine learning algorithms has great potential for identifying and classifying pathogenic microorganisms.  \nA R T I C L E I N F O  \nKeywords:  \nRaman spectroscopy Candida identification Machine learning Convolutional neural network Overfitting  \nG R A P H I C A L A B S T R A C T  \n\n|  |\n| --- |\n| A B S T R A C T |\n\nOne of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source.  \n* Corresponding authors.  \nE-mail addresses: ma-gabriela.fernandez@alumnos.unican.es (M.G. Fern´andez-Manteca), [adolfo.cobo@unican.es](adolfo.cobo@unican.es) (A. Cobo).  \n1 These authors contributed equally to this work.  \n[https://doi.org/10.1016/j.saa.2022.122270](https://doi.org/10.1016/j.saa.2022.122270)  \nReceived 1 October 2022; Received in revised form 29 November 2022; Accepted 20 December 2022  \nAvailable online 22 December 2022  \n1386-1425/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nM.G. Fern´andez-Manteca et al.  \nSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 290 (2023) 122270  \nAfter developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to di","cbCairNDVDECGLLF","https://ap.wps.com/l/cbCairNDVDECGLLF","pdf",5020030,1,12,"English","en",105,"# Highlights\n## Automatic identification workflow\n# Introduction\n## Clinical impact of candidiasis\n## Candida species diversity and prevalence\n## Disease spectrum and invasive forms","[{\"question\":\"Why is Raman spectroscopy useful for Candida identification?\",\"answer\":\"Traditional microbiological identification is complex and time-consuming. Raman spectroscopy is fast and low-cost and does not require sample preparation, supporting quicker results in clinical settings.\"},{\"question\":\"What dataset and measurement approach were used?\",\"answer\":\"Raman spectra were obtained from more than 220 measurements of dried drops from pure cultures of each Candida species using a confocal Raman microscope with 532 nm laser excitation.\"},{\"question\":\"Which machine learning model performed best and how accurate was it?\",\"answer\":\"A one-dimensional convolutional neural network (1-D CNN) reached above 80% overall accuracy across the eleven Candida classes with good generalization and reduced overfitting through hyperparameter optimization.\"}]","Automatic classification of Candida species using Raman spectroscopy and machine learning | PDF",1785899848,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"automatic-classification-of-candida-species-using-raman-spectroscopy-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automatic-classification-of-candida-species-using-raman-spectroscopy-and-machine-learning/125557/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is Raman spectroscopy useful for Candida identification?","Question",{"text":75,"@type":76},"Traditional microbiological identification is complex and time-consuming. Raman spectroscopy is fast and low-cost and does not require sample preparation, supporting quicker results in clinical settings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and measurement approach were used?",{"text":80,"@type":76},"Raman spectra were obtained from more than 220 measurements of dried drops from pure cultures of each Candida species using a confocal Raman microscope with 532 nm laser excitation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and how accurate was it?",{"text":84,"@type":76},"A one-dimensional convolutional neural network (1-D CNN) reached above 80% overall accuracy across the eleven Candida classes with good generalization and reduced overfitting through hyperparameter optimization.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]