Mining large Raman spectroscopic data beyond the shot noise limit

• المؤلفون الرئيسيون: Shimada, Rintaro, Ozawa, Takeaki
• مؤلفون آخرون: Asian Spectroscopy Conference 2020
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2020
• الموضوعات: Science::Chemistry; Chemometrics; Hyperspectral Data Analysis
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/144412
• المؤسسة: 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)...