Separation of overlapping fingerprints by principal component analysis and multivariate curve resolution—alternating least squares analysis of hyperspectral imaging data

2022 ◽  
Author(s):  
Norimitsu Akiba ◽  
Atsushi Nakamura ◽  
Takayuki Sota ◽  
Kazuhito Hibino ◽  
Hidetoshi Kakuda ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Hyperspectral Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
Imaging Data ◽  
Curve Resolution

A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis

The Analyst ◽  
2019 ◽  
Vol 144 (18) ◽  
pp. 5425-5438 ◽  
Author(s):  
Joseph P. Smith ◽  
Erin C. Holahan ◽  
Frank C. Smith ◽  
Veronica Marrero ◽  
Karl S. Booksh
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Chemical Constituents ◽  
Alternating Least Squares ◽  
Model Complexity ◽  
Multivariate Curve Resolution ◽  
Imaging Analysis ◽  
Imaging Data ◽  
Optimal Model ◽  
Curve Resolution

A new multivariate curve resolution-alternating least squares (MCR-ALS) methodology is presented that uses approximate reference spectra to determine optimal model complexity for identifying chemical constituents within hyperspectral imaging data.

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