Dr. Peter de B. Harrington, Professor of Chemistry & Biochemistry at Ohio University, authored a chapter in a book titled Resolving Spectral Mixtures, 1st Edition With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging, due out in September.
This comprehensive book presents an interdisciplinary approach to demonstrate how and why data analysis, signal processing, and chemometrics are essential to resolving the spectral unmixing problem. Harrington’s chapter is on a very important topic regarding Big Data
“Back in the 1990s, my group at OHIO demonstrated that you might compress Big Data to a manageable size using techniques such as multiway wavelet compression (WT), then model the compressed data using techniques such as multivariate curve resolution (MCR) or fuzzy rule-building expert systems FuRES (an OHIO Invention), and as the last step we uncompress the model so that we can interpret the results,” says Harrington.
“By compression, we not only make the calculation faster, but the results are often better because the compression can remove noise from the data. This approach has important ramifications for hyperspectral image analysis which is the title of the book,” he adds.
Harrington also serves on the Editorial Board of three psychology journals:
- The International Journal of Spectroscopy is a peer-reviewed, open-access journal that publishes original research and review articles dealing with the use of spectroscopic techniques in all areas of science.
- Nature, a nature research journal.
- Critical Reviews in Analytical Chemistry which provides in-depth, scholarly, insightful reviews of important topics within the discipline of analytical chemistry and related measurement sciences.
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