[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124794-en":3,"doc-seo-124794-105":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},124794,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Optimisation of electrochemical sensors based on molecularly imprinted polymers - from OFAT to machine learning","Molecularly imprinted polymers (MIPs) are engineered materials designed to selectively bind and recognise target molecules by tailoring chemical properties formed during polymerisation. Their sensing performance depends on sensitivity and cross-reactivity, as well as how sensor configurations retain selectivity in the presence of competing molecules. The review addresses rational, data-driven optimisation of electrochemical MIP sensors, moving from one-factor-at-a-time design of experiments toward chemometrics and machine-learning methods to reduce experimental trials and improve point-of-care configurations.","Analytical and Bioanalytical Chemistry  \n[https://doi.org/10.1007/s00216-023-05085-9](https://doi.org/10.1007/s00216-023-05085-9)  \nOptimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning  \nSabrina Di Masi1 · Giuseppe Egidio De Benedetto2 · Cosimino Malitesta1  \nReceived: 15 October 2023 / Revised: 28 November 2023 / Accepted: 29 November 2023  \n© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2023  \nAbstract  \nMolecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a bestperformant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.  \nKeywords Electrochemical sensor · Molecularly imprinted polymer · Optimisation · Chemometrics · Experimental design · Machine learning  \nPublished in the topical collection Advances in (Bio-)Analytical Chemistry: Reviews and Trends Collection 2024.  \n* Cosimino Malitesta  \ncosimino.malitesta@unisalento.it  \n1 Laboratorio di Chimica Analitica, Dipartimento di Scienzee Tecnologie Biologiche ed Ambientali, Università del Salento, Lecce, Italy  \n2 Laboratorio di Spettrometria di Massa Analitica e Isotopica, Dipartimento di Beni Culturali, Università del Salento, Lecce, Italy  \nIntroduction  \nOver the past decades, progression in the field of electrochemical sensors has faced the development of point-of-care (POC) devices for the rapid determination of a plenty of molecules of interest. Hence, superior recognition capabilities, with addressed improved selectivity properties have been recognised in the field of molecularly imprinted polymers (MIPs) [1–3] . These are synthetic engineered materials recognised as upper sensitive and selective receptors of a wide range of analytes. The polymerisation process is the core of MIP formation: at the first stage, the selection of functional monomers is fundamental in obtaining the sensitive polymeric material. As a rule, precursors of polymeric structure must be able to arrange the specific analyte  \n(template) through their accessible functional g","cbCaitgBffnjGOpg","https://ap.wps.com/l/cbCaitgBffnjGOpg","pdf",3201999,1,15,"English","en",105,"# Introduction\n## Molecularly imprinted polymers and electrochemical sensing\n## Need for optimisation and data-driven design\n# Optimisation through OFAT\n## One factor at a time strategy\n# Chemometrics and machine learning perspectives\n## Design of experiments and response surface methodologies","[{\"question\":\"What role do molecularly imprinted polymers (MIPs) play in electrochemical sensors?\",\"answer\":\"MIPs act as selective recognition receptors that bind target molecules based on size and functional compatibility shaped during polymerisation.\"},{\"question\":\"Why is optimisation important in MIP sensor development?\",\"answer\":\"Optimisation supports high performance by maintaining sensitivity and selectivity and by identifying the best-performing configuration while considering cross-reactivity with competitive molecules.\"},{\"question\":\"How do chemometrics and machine learning improve optimisation compared with OFAT?\",\"answer\":\"Chemometrics approaches using design of experiments reduce the number of experimental trials, and machine learning further enhances sensor data processing by enabling data-driven selection and management of configurations.\"}]","Optimisation of electrochemical sensors based on molecularly imprinted polymers - 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