[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118025-en":3,"doc-seo-118025-105":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},118025,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Photothermal Radiometry Data Analysis by Using Machine Learning","Photothermal radiometry enables infrared remote sensing for non-invasive biomedical and industrial non-destructive testing applications. The work reviews recent machine learning progress and its use in photothermal techniques, then presents a machine learning-based analysis approach for Opto-Thermal Transient Emission Radiometry (OTTER). The study covers regression and deep learning neural network models, introduces theoretical background, and validates performance using experimental results across skin hydration, depth profiling, pigments, and skin penetration measurements.","Article 1  \nPhotothermal Radiometry Data Analysis by Using Machine 2 Learning  \n3  \nPerry Xiao* and Daqing Chen 4  \nCitation: Lastname, F.; Lastname, F.; Lastname, F. Title. Sensors 2022, 22, x. [https://doi.org/10.3390/xxxxx](https://doi.org/10.3390/xxxxx)  \nAcademic Editor: Firstname Lastname  \nReceived: date  \nAccepted: date  \nPublished: date  \nPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/license](https://creativecommons.org/license)[s/by/4.0/](s/by/4.0/)) .  \nSchool of Engineering, London South Bank University; [xiaop@lsbu.ac.uk](xiaop@lsbu.ac.uk) 5  \n* [Correspondence: xiaop@lsbu.ac.uk](Correspondence: xiaop@lsbu.ac.uk); Tel.: +44 (0) 2078157569 6  \nAbstract: Photothermal techniques are infrared remote sensing techniques that have been used for 7  \nbiomedical applications as well as industrial non-destructive testing (NDT). Machine Learning is a 8  \nbranch of artificial intelligence, which includes a set of algorithms for learning from past data and 9  \nanalyzing new data without being explicitly programmed to do so. In this paper, we first review the 10  \nlatest development of Machine Learning and its applications in photothermal techniques. Next, we 11  \npresent our latest work on Machine Learning for data analysis in Opto-Thermal Transient Emission 12  \nRadiometry (OTTER), which is a type of photothermal techniques that has been extensively used in 13  \nskin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances 14  \nskin penetration measurements. We have investigated different algorithms such as Random Forest 15  \nRegression, Gradient Boosting Regression, Support Vector Machine (SVM) Regression, Partial Least 16  \nSquares Regression, as well as Deep Learning Neural Networks Regression. We first introduce the 17  \ntheoretical background, then illustrate its applications with experimental results. 18  \nKeywords: photothermal techniques, skin hydration, machine learning, deep learning, regression, 19 classification; 20  \n  21  \n1. Introduction 22 Photothermal techniques [1] are infrared remote sensing techniques that have been 23 used for biomedical applications as well as industrial non-destructive testing (NDT). They 24 can be dated back to the 1970s [2,3] . Photothermal techniques have since developed into 25 different approaches, such as photothermal radiometry [4-7], photothermal tomography 26 [8], photothermal imaging [9], photothermal radar [10], photothermal lens [11,12], photo- 27 thermal cytometry [13] and so on. The main advantages of photothermal techniques lie in 28 their non-invasive, remote-sensing, most importantly spectroscopic nature, which make 29 photothermal techniques a potentially powerful tool in many industrial, agricultural, en- 30 vironmental and biomedical applications. Pawlak has highlighted the advantages of spec- 31 trally resolved photothermal radiometry measurements on semiconductor samples [14] . 32 Machine learning [15,16] is a branch of artificial intelligence, which includes a set of 33 algorithms for learning from the past data and analyzing the new data without being ex- 34 plicitly programmed to do so. Machine Learning can be generally divided into Supervised 35 Learning, Un-supervised Learning, Semi-supervised Learning and Reinforcement Learn- 36 ing. Machine Learning has also been used in photothermal techniques recently. Verdel et 37 al have developed a predictive model for the quantitative analysis of human skin using 38 photothermal radiometry and diffuse reflectance spectroscopy [17,18], as well as a hybrid 39 technique for characterization of human skin by combining Machine Learning and in- 40 verse Monte Carlo approach [19], and they made their Machine Learning m","cbCaib3j9c2ywFnD","https://ap.wps.com/l/cbCaib3j9c2ywFnD","pdf",1171008,1,18,"English","en",105,"# Abstract\n# Introduction\n## Photothermal techniques overview\n## Machine learning fundamentals\n## Prior machine learning applications in photothermal methods\n# OTTER-based data analysis approach","[{\"question\":\"What problem does the document address in photothermal radiometry?\",\"answer\":\"It focuses on using machine learning to analyze photothermal measurement data, specifically via OTTER for quantitative interpretation across biomedical use cases.\"},{\"question\":\"Which machine learning methods are investigated for OTTER data analysis?\",\"answer\":\"The document studies several regression approaches, including Random Forest Regression, Gradient Boosting Regression, Support Vector Machine (SVM) Regression, Partial Least Squares Regression, and Deep Learning Neural Network Regression.\"},{\"question\":\"Why is OTTER presented as advantageous for photothermal measurements?\",\"answer\":\"OTTER is described as non-contact, non-destructive, quick to measure (a few seconds), spectroscopic in nature, and suitable for monitoring skin hydration, depth profiles, pigments, and trans-dermal delivery.\"}]","Photothermal Radiometry Data Analysis by Using Machine Learning | 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problem does the document address in photothermal radiometry?","Question",{"text":75,"@type":76},"It focuses on using machine learning to analyze photothermal measurement data, specifically via OTTER for quantitative interpretation across biomedical use cases.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are investigated for OTTER data analysis?",{"text":80,"@type":76},"The document studies several regression approaches, including Random Forest Regression, Gradient Boosting Regression, Support Vector Machine (SVM) Regression, Partial Least Squares Regression, and Deep Learning Neural Network Regression.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is OTTER presented as advantageous for photothermal measurements?",{"text":84,"@type":76},"OTTER is described as non-contact, non-destructive, quick to measure (a few seconds), spectroscopic in nature, and suitable for monitoring skin hydration, depth profiles, pigments, and trans-dermal 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