Mining large Raman spectroscopic data beyond the shot noise limit

• Main Authors: Shimada, Rintaro, Ozawa, Takeaki
• Other Authors: Asian Spectroscopy Conference 2020
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Science::Chemistry; Chemometrics; Hyperspectral Data Analysis
• Online Access: https://hdl.handle.net/10356/144412
• Institution: 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)...