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

Smith, Joseph P.; Holahan, Erin C.; Smith, F.C.; Marrero, Veronica; Booksh, Karl S.

doi:10.1039/c9an00787c

Cited by 39 publications

(13 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using MCR-ALS analysis, a complex system with multiple evolving components is reduced to the least number of components that can describe the initial dataset. This makes MCR-ALS especially suitable for the analysis of large datasets generated during in situ and/or operando experiments, e.g., IR, 54 Raman, 55 XAS, 56 XRD, 57 or PDF. 58,59 MCR-ALS provides the characteristic profiles of the individual components and the evolution of these components with time (time-concentration profiles).…”

Section: In Situ Pdf and Xrd Analysesmentioning

confidence: 99%

Effect of molten sodium nitrate on the decomposition pathways of hydrated magnesium hydroxycarbonate to magnesium oxide probed byin situtotal scattering

et al. 2020

View full text Add to dashboard Cite

show abstract

Section: In Situ Pdf and Xrd Analysesmentioning

confidence: 99%

Effect of molten sodium nitrate on the decomposition pathways of hydrated magnesium hydroxycarbonate to magnesium oxide probed byin situtotal scattering

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The hyperspectral Raman imaging data were analyzed using MCR-ALS to generate spatially resolved chemical images (scores) and corresponding resolved Raman spectra of the individual chemical species (pure components). This method is often used in the case of complex samples of unknown composition [ 14 , 15 ].…”

Section: Methodsmentioning

confidence: 99%

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

et al. 2022

View full text Add to dashboard Cite

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a “coffee ring”. MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R2CV.

show abstract

“…Multivariate curve resolution–alternating least squares (MCR–ALS), a machine learning method, was performed to analyze all Raman hyperspectral imaging data sets herein. MCR–ALS is commonly used to elucidate pure response profiles of the chemical constituents within a mixture. − Data collected from a variety of analytical methods, including Raman spectroscopy, have been successfully analyzed using MCR–ALS. − This machine learning methodology resolves spectral and concentration information of each individual, pure component within complex hyperspectral images, offering a powerful analysis tool to extract all useful information embedded within a data set. ,, MCR–ALS provides distinct advantages over other methods, such as PCA, due to its ability to provide more meaningful biochemical information. , This analytical methodology generates both chemical information representing species in a mixture and the corresponding spatial distributions of those species, making it ideal for studying enzyme immobilization.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis

Ralbovsky

Smith

2021

Anal. Chem.

Self Cite

View full text Add to dashboard Cite

Biocatalysis has rapidly become an essential tool in the scientific and industrial communities for the development of efficient, safe, and sustainable chemical syntheses. Immobilization of the biocatalyst, typically an engineered enzyme, offers significant advantages, including increased enzyme stability and control, resistance to environmental change, and enhanced reusability. Determination and optimization of the spatial and chemical distribution of immobilized enzymes are critical for proper functionality; however, analytical methods currently employed for doing so are frequently inadequate. Machine learning, in the form of multivariate curve resolution, with Raman hyperspectral imaging is presented herein as a potential method for investigating the spatial and chemical distribution of evolved pantothenate kinase immobilized onto two diverse, microporous resins. An exhaustive analysis indicates that this method can successfully resolve, both spatially and spectrally, all chemical species involved in enzyme immobilization, including the enzyme, both resins, and other key components. Quantitation of the spatial coverage of immobilized enzymes, a key parameter used for process development, was accomplished. Optimal analytical parameters were determined by the evaluation of different excitation wavelengths. Exploratory chemometric approaches, including principal component analysis, were utilized to investigate the chemical species embedded within the data sets and their relationships. The totality of this information can be utilized for an enhanced understanding of enzyme immobilization processes and can allow for the further implementation of biocatalysis within the scientific and pharmaceutical communities.

show abstract

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

Abstract: 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.

Cited by 39 publications

References 48 publications

Effect of molten sodium nitrate on the decomposition pathways of hydrated magnesium hydroxycarbonate to magnesium oxide probed byin situtotal scattering

Effect of molten sodium nitrate on the decomposition pathways of hydrated magnesium hydroxycarbonate to magnesium oxide probed byin situtotal scattering

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis

Contact Info

Product

Resources

About