Mining large Raman spectroscopic data beyond the shot noise limit

• Main Authors: Shimada, Rintaro, Ozawa, Takeaki
• 其他作者: Asian Spectroscopy Conference 2020
• 格式: Conference or Workshop Item
• 語言: English
• 出版: 2020
• 主題: Science::Chemistry; Chemometrics; Hyperspectral Data Analysis
• 在線閱讀: https://hdl.handle.net/10356/144412
• 機構: Nanyang Technological University

Various multivariate chemometric techniques have been proven to be robust in analyzing complex Raman hyperspectral datasets obtained from chemically diverse biological samples for classification, quantification, and exploratory studies. Among various techniques, singular value decomposition (SVD)...