Final report : Profiling the impact of physical activity using infra-red spectroscopy and machine learning non-invasive sampling

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<title>Final report : Profiling the impact of physical activity using infra-red spectroscopy and machine learning non-invasive sampling</title>
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<namePart>Oliveira, Paulo</namePart>
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<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20260006406">
<namePart>Rama, Luís</namePart>
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<publisher>Fundación MAPFRE</publisher>
<dateIssued>2022</dateIssued>
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<abstract displayLabel="Summary">This document presents the final report of a research project focused on the use of Fourier transform infrared spectroscopy (FTIR) combined with machine learning models to analyze physiological responses to physical exercise through non-invasive biological sampling. The study evaluates the ability of these techniques to identify biomarkers associated with physical activity and predict parameters such as body fat percentage and VO2max. Capillary blood, serum, plasma, and saliva samples obtained during a three-month exercise protocol in sedentary individuals are analyzed. The results demonstrate the potential of neural networks to model complex relationships in biological data. The work lays the groundwork for future applications in sports science, health, and performance monitoring</abstract>
<note type="statement of responsibility">investigador principal: Paulo Oliveira ; Luís Rama... [et al.]</note>
<note>Memoria final de la investigación realizada con la Ayuda a la Investigación Ignacio H. de Larramendi de Fundación MAPFRE, convocatoria del año 2022. Universidad de Coimbra, Portugal</note>
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<topic>Actividad física</topic>
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<topic>Biomarcadores</topic>
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<topic>Análisis multivariante</topic>
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<topic>Neurobiología</topic>
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