MCR-ALS GUI 2.0: New features and applications

Jaumot, Joaquim; Juan, Anna de; Tauler, Romà

doi:10.1016/j.chemolab.2014.10.003

Cited by 641 publications

(520 citation statements)

References 34 publications

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“…Here, we used a MATLAB based MCR-ALS toolbox [23] to determine C and T S . The initial estimates of C and T S were determined by means of a purest variable detection method [26].…”

Section: Data Preprocessing and Mcr Analysismentioning

confidence: 99%

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Raman active components of skin cancer

Feng¹,

Moy²,

Nguyen³

et al. 2017

Biomed. Opt. Express

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Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

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“…Here, we used a MATLAB based MCR-ALS toolbox [23] to determine C and T S . The initial estimates of C and T S were determined by means of a purest variable detection method [26].…”

Section: Data Preprocessing and Mcr Analysismentioning

confidence: 99%

“…Multivariate curve resolution (MCR) analysis [23] was used to resolve in situ skin constituents from Raman images. Our results suggest that eight skin constituents are the most relevant building blocks, illustrating some variances with their corresponding synthetic components.…”

Section: Introductionmentioning

confidence: 99%

Raman active components of skin cancer

Feng¹,

Moy²,

Nguyen³

et al. 2017

Biomed. Opt. Express

View full text Add to dashboard Cite

show abstract

“…3 Since only the product DZ¢ can be estimated from Y and S, the individual matrices D and Z need to be determined by methods such as MCR-ALS. 4 …”

Section: Estimation Of Spectral Overlap a And Level Overlap Bmentioning

confidence: 99%

Multivariate data modelling for de-shadowing of airborne hyperspectral imaging

Fortuna

Martens²

2017

J. Spectral Imaging

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Airborne hyperspectral imaging is a powerful technique for high-resolution classification of large areas of ground, applied today in fields like agriculture and environmental monitoring. Even though many classification algorithms are capable of handling shadows without a decrease in performance, visual inspection can be made easier if shadows are removed. In this paper we present a method for separating the effect of shadows (de-shadowing) and other partially known lighting condition changes from the effects due to the physical, chemical or biological properties of the ground, which are of interest. An example application is shown with good results.

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“…MCR-ALS analysis was also performed on Matlab ® using an updated version of the graphical interface for MCR-ALS that can be freely downloaded from the MCR webpage. 33,34 …”

Section: Data Pretreatment and Softwarementioning

confidence: 99%

NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint

Sales¹,

Lima²,

Stragevitch³

et al. 2016

J. Braz. Chem. Soc.

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Near infrared spectroscopy in-line monitoring and modelling of soybean oil methanolysis has been done using multivariate curve resolution alternating least squares (MCR-ALS) with correlation constraint. This constraint allows for quantitation of the methyl ester formed with little calibration effort and the MCR model provides additionally a general description (qualitative and quantitative) of the rest of components in the process. Due to the complexity of this process, which shows components with severe kinetic and spectral overlap, suitably designed multiset analysis strategies were adopted to improve the recovery of concentration profiles of the methyl ester. To assess the temperature and catalyst concentration effects on the kinetic reaction, five batches with different temperatures (20, 44 and 55 °C) and catalyst concentrations (0.75 and 1 m/m%) were produced. The concentration profiles of methyl ester obtained by MCR-ALS for each batch was the starting information used to develop a simplified kinetic model and calculate the activation energy.

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MCR-ALS GUI 2.0: New features and applications

Cited by 641 publications

References 34 publications

Raman active components of skin cancer

Raman active components of skin cancer

Multivariate data modelling for de-shadowing of airborne hyperspectral imaging

NIR Monitoring and Modelling of Soybean Oil Methanolysis with Multivariate Curve Resolution-Alternating Least Squares with Correlation Constraint

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