[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121112-en":3,"doc-seo-121112-105":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},121112,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting the microalgae lipid profile obtained by supercritical fluid extraction using a machine learning model","A machine learning framework predicts lipid profiles obtained from supercritical fluid extraction (SFE) of the extremophile microalgae Galdieria sp. USBA-GBX-832 under varied temperature (40, 50, 60°C), pressure (150, 250 bar), and ethanol flow (0.6, 0.9 mL/min) conditions. Six regression models were trained with 33 variables combining RD-Kit molecular descriptors, extraction conditions, and infinite dilution activity coefficient (IDAC). Lipidomic analysis identified 139 features, with 89 annotated lipids as model inputs, and representative-lipid selection was supported by unsupervised learning plus COSMO-SAC-HB2 comparisons. Decision-tree models, especially XGBoost, achieved strong accuracy and generalized to unseen conditions, enabling cost-effective SFE optimization for other biological samples.","TYPE Original Research  \nPUBLISHED 25 October 2024  \nDOI 10.3389/fchem.2024.1480887  \nOPEN ACCESS  \nEDITED BY  \nWojciech Smulek,  \nPoznań University of Technology, Poland  \nREVIEWED BY  \nFilipe Hobi Bordon Sosa, University of Aveiro, Portugal Abel Zúñiga-Moreno,  \nNational Polytechnic Institute (IPN), Mexico  \n*CORRESPONDENCE  \nJuan David Rangel Pinto,  \n [jd.rangel10@uniandes.edu.co](jd.rangel10@uniandes.edu.co)[ ](jd.rangel10@uniandes.edu.co)Andrés Fernando González Barrios,  \n [andgonza@uniandes.edu.co](andgonza@uniandes.edu.co)  \nRECEIVED 14 August 2024  \nACCEPTED 15 October 2024  \nPUBLISHED 25 October 2024  \nCITATION  \nRangel Pinto JD, Guerrero JL, Rivera L, Parada-Pinilla MP, Cala MP, López G and González Barrios AF (2024) Predicting the microalgae lipid profile obtained by supercritical fluid extraction using a machine learning model.  \nFront. Chem. 12:1480887 .  \ndoi: 10.3389/fchem.2024.1480887  \nCOPYRIGHT  \n© 2024 Rangel Pinto, Guerrero, Rivera, ParadaPinilla, Cala, López and González Barrios. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting the microalgae lipid profile obtained by supercritical fluid extraction using a machine learning model  \nJuan David Rangel Pinto 1*, Jose L. Guerrero 2, Lorena Rivera 3, María Paula Parada-Pinilla 3, Mónica P. Cala 2, Gina López 3 and Andrés Fernando González Barrios 1*  \n1Grupo de Diseño de Productos Y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de los Andes, Bogotá, Colombia, 2Metabolomics Core Facility—MetCore, Vice-Presidency for Research, Universidad de los Andes, Bogotá, Colombia, 3Unidad de Saneamiento y Biotecnología Ambiental (USBA), Departamento de Biología, Facultad de Ciencias, Pontificia Universidad Javeriana (PUJ), Bogotá, Colombia  \nIn this study a Machine Learning model was employed to predict the lipid profile from supercritical fluid extraction (SFE) of microalgae Galdieria sp. USBA-GBX- 832 under different temperature (40, 50, 60°C), pressure (150, 250 bar), and ethanol flow (0 . 6, 0 . 9 mL min-1) conditions. Six machine learning regression models were trained using 33 independent variables: 29 from RD-Kit molecular descriptors, three from the extraction conditions, and the infinite dilution activity coefficient (IDAC). The lipidomic characterization analysis identified 139 features, annotating 89 lipids used as the entries of the model, primarily glycerophospholipids and glycerolipids. It was proposed a methodology for selecting the representative lipids from the lipidomic analysis using an unsupervised learning method, these results were compared with Tanimoto scores and IDAC calculations using COSMO-SAC-HB2 model. The models based on decision trees, particularly XGBoost, outperformed others (RMSE: 0. 035, 0 . 095, 0 . 065 and coefficient of determination (R2): 0 . 971, 0 . 933, 0 .946 for train, test and experimental validation, respectively), accurately predicting lipid profiles for unseen conditions. Machine Learning methods provide a costeffective way to optimize SFE conditions and are applicable to other biological samples.  \nKEYWORDS  \nsupercritical fluid extraction, regression models, lipidomic, COSMO-SAC, extremophile microalgae  \n1 Introduction  \n1.1 Lipids extraction techniques  \nLipids are a diverse group of biomolecules, generally classified into eight categories (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids and polyketides), based on their hydrophobic or amphipathic properties and chemically functional backbones (Fahy et al., 2005; Liebisch et al., 2020) . Tradit","cbCainyIWsCvUBer","https://ap.wps.com/l/cbCainyIWsCvUBer","pdf",2380904,1,13,"English","en",105,"# Introduction\n## Lipids extraction techniques","[{\"question\":\"What microalgae and extraction conditions were used to build the predictive models?\",\"answer\":\"The study used Galdieria sp. USBA-GBX-832 and varied temperature (40–60°C), pressure (150–250 bar), and ethanol flow (0.6–0.9 mL/min) in supercritical fluid extraction.\"},{\"question\":\"How were the machine learning regression models trained and what inputs were used?\",\"answer\":\"Six regression models were trained using 33 variables, including RD-Kit molecular descriptors, extraction-condition variables, and the infinite dilution activity coefficient (IDAC).\"},{\"question\":\"Which model performed best and how was performance evaluated?\",\"answer\":\"Decision-tree models, particularly XGBoost, outperformed others, with reported RMSE and high coefficients of determination (R2) for train, test, and experimental validation.\"}]","Predicting the microalgae lipid profile obtained by supercritical fluid extraction using a machine learning model | PDF",1785733799,33,{"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},"predicting-the-microalgae-lipid-profile-obtained-by-supercritical-fluid-extraction-using-a-machine-learning-model","",{"@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/predicting-the-microalgae-lipid-profile-obtained-by-supercritical-fluid-extraction-using-a-machine-learning-model/121112/",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","2026-08-03",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},"What microalgae and extraction conditions were used to build the predictive models?","Question",{"text":75,"@type":76},"The study used Galdieria sp. 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