[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125926-en":3,"doc-seo-125926-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125926,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods","Bacterial infections and antibiotic resistance are major public-health challenges, making the rapid identification of novel antibacterial compounds urgent. The study uses Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with advanced machine-learning to predict the effects of compounds extracted from Cynara cardunculus against Escherichia coli. Plant tissues and solvents yield compounds with distinct phenol-related compositions and antioxidant, antimicrobial activities. Principal component analysis separates inhibitors of E. coli growth, while supervised models predict growth impact with reported accuracies from 72% to 100%, supporting streamlined discovery.","antibiotics   \nArticle  \nEnhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods  \nPedro N. Sampaio 1,2, * and Cecília C. R. Calado 3,4  \nCitation: Sampaio, P.N.; Calado,  \nC.C.R. Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods. Antibiotics 2024, 13, 428. [https://](https://)[ ](https://)[doi.org/10.3390/antibiotics13050428](doi.org/10.3390/antibiotics13050428)  \n[Academic Editor: Juraj Greg](Academic Editor: Juraj Greg)á  \nReceived: 29 March 2024  \nRevised: 29 April 2024  \nAccepted: 7 May 2024  \nPublished: 9 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 COPELABS—Computação e Cognição Centrada nas Pessoas, Faculty of Engineering, Lusófona University, Campo Grande, 376, 1749-024 Lisbon, Portugal  \n2 GREEN-IT—BioResources for Sustainability Unit, Institute of Chemical and Biological Technology António Xavier, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal  \n3 ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal; [cecilia.calado@isel.pt](cecilia.calado@isel.pt)  \n4 iBB—Institute for Bioengineering and Biosciences, i4HB—The Associate Laboratory Institute for Health and Bioeconomy, IST—Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal  \n* [Correspondence: pedro.sampaio@ulusofona.pt](Correspondence: pedro.sampaio@ulusofona.pt); Tel.: 351-21-750-55-00  \nAbstract: Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds’ impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.  \nKeywords: antimicrobial; Cynara cardunculus; machine learning; MIR-Spectroscopy; PCA; PLS-DA; SVM; KNN; BPN  \n1. Introduction  \nAs a consequence of the rise in antibiotic resistance, it is imperative to develop fast and affordable systems for discovering bioactive compounds with antimicrobial activity that are also non-cytotoxic to the human host [1] . Antibiotic-resistant bacteria present a formidable and concerning issue in contemporary medicine, which is compounded by the dwindling progress in new antibiotic development and the escalating spread of multi-drugresistant determinants,","cbCaikdAYyOcbaqC","https://ap.wps.com/l/cbCaikdAYyOcbaqC","pdf",4519287,5,1,17,"English","en",105,"# Introduction\n## Antibiotic resistance and need for faster screening\n## Role of plant extracts\n## FTIR (MIR) as metabolic fingerprinting\n## Applications of FTIR in biomedical classification and diagnosis","[{\"question\":\"What problem does the study address?\",\"answer\":\"It targets the challenge of antibiotic resistance and the need for fast, affordable ways to discover non-cytotoxic bioactive antibacterial compounds.\"},{\"question\":\"How does the approach work?\",\"answer\":\"FT-MIR spectroscopy is coupled with machine-learning and neural network models to predict how extracted compounds from Cynara cardunculus affect E. coli growth.\"},{\"question\":\"What performance was achieved by the classification models?\",\"answer\":\"Reported accuracies include 94% for PLS-DA, 89% for SVM, 72% for KNN, and 100% for a backpropagation network.\"}]","Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods | 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