Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to remote sensing hyperspectral imaging

2013 ◽  
Vol 762 ◽  
pp. 25-38 ◽  
Author(s):  
Xin Zhang ◽  
Romà Tauler
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Hyperspectral Imaging ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
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.

scholarly journals Quantitative analysis and resolution of pharmaceuticals in the environment using multivariate curve resolution-alternating least squares (MCR-ALS)

2019 ◽  
Vol 69 (2) ◽  
pp. 217-231 ◽  
Author(s):  
Ahmed Mostafa ◽  
Heba Shaaban
Keyword(s):  
Least Squares ◽  
Water Samples ◽  
Alternating Least Squares ◽  
Multivariate Curve Resolution ◽  
Least Squares Regression ◽  
Environmental Matrices ◽  
Curve Resolution ◽  
Real Water Samples ◽  
Target Analytes ◽  
Spectrophotometric Data

Abstract The study presents the application of multivariate curve resolution alternating least squares (MCR-ALS) with a correlation constraint for simultaneous resolution and quantification of ketoprofen, naproxen, paracetamol and caffeine as target analytes and triclosan as an interfering component in different water samples using UV-Vis spectrophotometric data. A multivariate regression model using the partial least squares regression (PLSR) algorithm was developed and calculated. The MCR-ALS results were compared with the PLSR obtained results. Both models were validated on external sample sets and were applied to the analysis of real water samples. Both models showed comparable and satisfactory results with the relative error of prediction of real water samples in the range of 1.70–9.75 % and 1.64–9.43 % for MCR-ALS and PLSR, resp. The obtained results show the potential of MCR-ALS with correlation constraint to be applied for the determination of different pharmaceuticals in complex environmental matrices.

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