Event Details
Functional Principal Component Analysis based Machine Learning Algorithms for Spectral Analysis
Presenter: Yifeng Bie
Supervisor:
Date: Fri, August 27, 2021
Time: 08:30:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
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Abstract:
The ability to probe molecular electronic and vibrational structures gives rise to optical absorption spectroscopy, which is a credible tool used in molecular quantification and classification with high sensitivity, low limit of detection (LoD), and immunity to electromagnetic noises. Spectra are sensitive to slight analyte variations, so they are often used to identify a sample’s components. This thesis proposes several methods for quick classification and quantification of analysts based on their
absorbance spectra. functional Principal Component Analysis (fPCA) is employed for feature extraction and dimension reduction. For 1,000-pixel spectra data, fPCA can capture the majority variance with as few output scores as the number of expected analytes. This reduces the amount of calculation required for the following machine learning algorithms. Further, the output scores are fed into XGBoost and logistic regression for classification, and fed into XGBoost and linear regression for
quantification.