[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118053-en":3,"doc-seo-118053-105":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},118053,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning Techniques for Analysis of Biochemical Data","A biomedical engineering thesis develops and evaluates machine-learning approaches for predicting biochemical substance concentrations from spectroscopic measurements. It reviews fundamentals of UV/Visible, near-infrared (NIR), and mid-infrared (MIR) spectroscopy, surveys substances detectable in blood, and performs an in-depth study of lactate including structure, physiological roles, and metabolic cycles. The work then formulates mathematical modeling strategies and compares regression and latent-variable methods. Finally, it validates models through laboratory experiments using aqueous pigmented solutions and culture medium samples, including glucose and lactate detection under varied experimental conditions.","ALMA MATER STUDIORUM-UNIVERSIT`A DI BOLOGNA  \nCESENA CAMPUS  \nDEPARTMENT OF ELECTRICAL, ELECTRONIC, AND INFORMATION  \nENGINEERING  \n“GUGLIELMO MARCONI”  \nSECOND CYCLE DEGREE IN BIOMEDICAL ENGINEERING  \nClass: LM-21  \nTHESIS TITLE  \nMachine Learning Techniques for Analysis of  \nBiochemical Data  \nGraduation thesis in  \nSENSORS AND NANOTECHNOLOGY  \nSupervisor Candidate  \nProf. Marco Tartagni Matilde Arfilli  \nCo-Supervisor  \nProf. Emanuele Domenico Giordano  \nCo-Supervisor  \nProf. Joseph Lovecchio  \nAcademic Year 2022/2023  \nContents  \nIntroduction 3  \n1 Fundamentals of Spectroscopy: UV/Visible, Near Infrared (NIR), and Mid Infrared (MIR) 5  \n1.1 General introduction .............................. 5  \n1.2 Physical Principles of Spectroscopy ...................... 6  \n1.3 UV/Visible Spectroscopy ............................ 13  \n1.3.1 Spectroscopic Principles and Common Methodologies for NIR, MIR, and UV/Visible: From Calibration to Model Validation ........ 13  \n1.3.2 Instrumental Aspects of UV/Visible Spectroscopy ........... 14  \n1.3.3 Advantages and Limitations ...................... 17  \n1.4 Infrared Spectroscopy .............................. 18  \n1.4.1 Near Infrared (NIR) Spectroscopy ................... 18  \n1.4.2 Mid Infrared (MIR) Spectroscopy ................... 25  \n1.5 Conclusion ................................... 30  \n2 Substances Detectable in Blood 31  \n2.1 Overview of Analyzable Substances ...................... 31  \n2.1.1 Carbon Dioxide (CO2 ) ......................... 32  \n2.1.2 Oxygen and Hemoglobin ........................ 34  \n3 In-Depth Examination of Lactate 40  \n3.1 Chemical Structure of Lactate ......................... 40  \n3.1.1 Molecular Composition ........................ 40  \n3.1.2 Isomeric Configuration ......................... 41  \n3.1.3 Structural and Functional Properties .................. 41  \n3.2 Physiological Properties and Significance ................... 42  \n3.2.1 Role of Lactate in Biological Processes ................ 42  \n3.2.2 Interaction with Energetic Metabolism ................. 43  \n3.2.3 Physiological Effects in Tissues and Cells ............... 43  \n3.3 Metabolic Cycles Associated with Lactate ................... 44  \n3.3.1 Glycolysis ............................... 46  \n3.3.2 Cori Cycle ............................... 48  \n3.4 Importance of Lactate Measurement ...................... 49  \n3.4.1 State of Art of the technology ..................... 50  \n4 Mathematical Models for the Prediction of Biochemical Substance Concentra  \ntions 58  \n4.1 Exploring Dataset Configurations: From Classic Tools to Contemporary Complexities ..................................... 59  \n4.2 Critical Challenges with Engineering Data: Size, Independence, Noise, and Other Considerations .............................. 60  \n4.3 Multiple Linear Regression (MLR) Model ................... 61  \n4.3.1 Mathematical Foundations ....................... 61  \n4.3.2 Model Operation and Application Domains .............. 63  \n4.3.3 Pros and Cons ............................. 63  \n4.4 Latent Variable Modeling ............................ 64  \n4.4.1 Concept of Latent Variables ...................... 64  \n4.5 Principal Component Analysis (PCA) Model ................. 65  \n4.5.1 Mathematical Foundations ....................... 65  \n4.5.2 Model Operation and Application Domains .............. 67  \n4.5.3 Pros and Cons ............................. 72  \n4.6 Principal Component Regression (PCR) Model ................ 72  \n4.6.1 Application Domains .......................... 74  \n4.6.2 Pros and Cons ............................. 74  \n4.7 Partial Least Squares (PLS) Regression Model ................. 75  \n4.7.1 Model Operation and Application Domains .............. 75  \n4.7.2 Construction of the PLS model ..................... 76  \n4.7.3 Pros and Cons ............................. 77  \n4.8 Comparison and Model Selection ........................ 78  \n4.8.1 Comparative Analysis of Models .................... 78  \n4.8.2 Select","cbCaiilQV1apSOfA","https://ap.wps.com/l/cbCaiilQV1apSOfA","pdf",6016038,1,151,"English","en",105,"# Introduction\n# Fundamentals of Spectroscopy: UV/Visible, Near Infrared (NIR), and Mid Infrared (MIR)\n# Substances Detectable in Blood\n# In-Depth Examination of Lactate\n# Mathematical Models for the Prediction of Biochemical Substance Concentrations\n# Laboratory Experiments and Results Analysis: Aqueous Pigmented Solutions and Culture Medium Samples\n# Bibliography","[{\"question\":\"What spectroscopic techniques are covered for biochemical data analysis?\",\"answer\":\"The thesis covers UV/Visible spectroscopy as well as infrared approaches including near-infrared (NIR) and mid-infrared (MIR). It addresses physical principles, instrumentation aspects, and how calibration connects to model validation.\"},{\"question\":\"Which biochemical focus is emphasized beyond general blood analytes?\",\"answer\":\"Lactate is examined in depth, including its chemical structure, physiological significance, and the metabolic cycles related to lactate such as glycolysis and the Cori cycle.\"},{\"question\":\"How are biochemical concentrations predicted and compared in the modeling section?\",\"answer\":\"Concentration prediction uses multiple mathematical models, including multiple linear regression, latent variable modeling, PCA, PCR, and PLS regression. The thesis performs comparison and model selection to identify suitable methods for concentration prediction.\"}]","Machine Learning Techniques for Analysis of Biochemical Data | PDF",1785681053,381,{"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},"machine-learning-techniques-for-analysis-of-biochemical-data","",{"@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/machine-learning-techniques-for-analysis-of-biochemical-data/118053/",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","2026-08-02",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 spectroscopic techniques are covered for biochemical data analysis?","Question",{"text":75,"@type":76},"The thesis covers UV/Visible spectroscopy as well as infrared approaches including near-infrared (NIR) and mid-infrared (MIR). It addresses physical principles, instrumentation aspects, and how calibration connects to model validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which biochemical focus is emphasized beyond general blood analytes?",{"text":80,"@type":76},"Lactate is examined in depth, including its chemical structure, physiological significance, and the metabolic cycles related to lactate such as glycolysis and the Cori cycle.",{"name":82,"@type":73,"acceptedAnswer":83},"How are biochemical concentrations predicted and compared in the modeling section?",{"text":84,"@type":76},"Concentration prediction uses multiple mathematical models, including multiple linear regression, latent variable modeling, PCA, PCR, and PLS regression. 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