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MACHINE LEARNING METHODS FOR SPECTRAL ANALYSIS

thesis
posted on 28.07.2021, 03:04 by Youlin LiuYoulin Liu
Measurement science has seen fast growth of data in both volume and complexity in recent years, new algorithms and methodologies have been developed to aid the decision
making in measurement sciences, and this process is automated for the liberation of labor. In light of the adversarial approaches shown in digital image processing, Chapter 2 demonstrate how the same attack is possible with spectroscopic data. Chapter 3 takes the question presented in Chapter 2 and optimized the classifier through an iterative approach. The optimized LDA was cross-validated and compared with other standard chemometrics methods, the application was extended to bi-distribution mineral Raman data. Chapter 4 focused on a novel Artificial Neural Network structure design with diffusion measurements; the architecture was tested both with simulated dataset and experimental dataset. Chapter 5 presents the construction of a novel infrared hyperspectral microscope for complex chemical compound classification, with detailed discussion in the segmentation of the images and choice of a classifier to choose.

Funding

NSF GOALI

History

Degree Type

Doctor of Philosophy

Department

Chemistry

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Garth J. Simpson

Additional Committee Member 2

Chengde Mao

Additional Committee Member 3

Hilkka I Kenttamaa

Additional Committee Member 4

Chi (Jesse) Zhang