Evaluation of regression methods for predicting molecule concentrations from voltammetric data
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Perustieteiden korkeakoulu |
Master's thesis
Ask about the availability of the thesis by sending email to the Aalto University Learning Centre oppimiskeskus@aalto.fi
Authors
Date
2020
Department
Major/Subject
Data Science( ICT Innovation )
Mcode
SCI3095
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
46+8
Series
Abstract
The application of machine learning regression technology to the field of electro- chemical bio-sensing is investigated in this thesis. Experimental data is collected from voltammetry measurements of dopamine and ascorbic acid using tetrahedral amorphous carbon (ta-C) electrodes. Fixed concentrations of dopamine and ascorbic acid in a phosphate buffer solution are measured. And the data is used to train and test in regression algorithms. Feature extraction and dimension reduction is applied to the experimental data as well. The agreement is shown rationally between the actual concentration and the concentration predicted by gradient boosting and support vector machine with polynomial kernel from the experimental results. Cross validation is applied for each models. For the test dataset, the support vector regression algorithm with polynomial kernel achieves R2 score above 0.84. It shows us the potential ability to use machine learning algorithms in the detection and identification of different molecules in the future study.Description
Supervisor
Koskinen, JariThesis advisor
Laurila, TomiKeywords
machine learning, regression, electrochemistry, bio-monitoring