Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS)

Felten, Judith; Hall, Hardy; Jaumot, Joaquim; Tauler, Romà; Juan, Anna de; Gorzsás, András

doi:10.1038/nprot.2015.008

Cited by 241 publications

(229 citation statements)

References 52 publications

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“…For example, multivariate curve resolution (MCR) has been developed by several research groups for the analysis of hyperspectral confocal luorescence and Raman image data sets [29,[32][33][34] inding success in complex multicomponent biological samples. It is therefore used in the analysis presented in this chapter.…”

Section: Introductionmentioning

confidence: 99%

Localizing and Quantifying Carotenoids in Intact Cells and Tissues

Timlin¹,

Collins²,

Beechem³

et al. 2017

Carotenoids

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Raman spectroscopy provides detailed information about the molecular structure of carotenoids. Advances in detector sensitivity and acquisition speed have driven the expansion of Raman spectroscopy from a bulk analytical tool to a powerful method for mapping carotenoid abundance in cells and tissues. In many applications, the technique is compatible with living organisms, providing highly speciic molecular structure information in intact cells and tissues with subcellular spatial resolution. This leads to spatial-temporalchemical resolution critical to understanding the complex processes in the life cycle of carotenoids and other biomolecules.

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Section: Introductionmentioning

confidence: 99%

Localizing and Quantifying Carotenoids in Intact Cells and Tissues

Timlin¹,

Collins²,

Beechem³

et al. 2017

Carotenoids

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“…Molecular vibrations are defined as infrared (IR) active when the dipolar momentum of the molecule changes as the molecule vibrates, whereas vibrations are considered Raman active when the polarizability of the molecule changes as the molecule vibrates [1][2][3]. On the other hand, one of the main reasons for using Raman is that the spectrum delivers a structural fingerprint of the molecules analyzed.…”

Section: Introductionmentioning

confidence: 99%

Determination of Processed Salmon Components Sticking to Polyethylene Terephthalate Coatings of Containers by FT-IR and Raman Vibrational Spectroscopy

Zumelzu

Sanz

Medina

et al. 2018

Journal of Spectroscopy

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Earlier studies determined that portions of salmon were strongly sticking to the polymer coating of the container walls after emptying the cans. In this sense, this work performed high-and low-frequency spectral characterizations of fresh salmon muscle, fat, and skin by Fourier-transform infrared (FT-IR) and Raman spectroscopy analyses to elucidate which components were effectively sticking to the underlying coating. The spectral analyses evidenced that the bands of skin and muscle were clearly distinctive. However, less perceptible contrasts were observed between fat and muscle until band 1700 cm −1 , but above this limit, the minor spectral changes detected were sufficient to characterize both salmon components. The new spectral bands for skin occurred at 1030, 1202, and 1336 cm , even though it appeared in all components of the salmon. The bands for the ν(C-H) and ν(O-H) vibrations in the high-frequency region were the same, but the intensities and profiles were different. The similarities between the spectra of fresh salmon muscle and residues sticking to the polymer layers were substantial, corroborating that this is in fact the main component sticking to the polymer surface coating of industrial food cans.

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“…Normally, initial estimations are constructed with an instrumental signal of pure components or with an estimate generated from the sample signal by chemometric tools capable of detecting the purest variables. 24,[31][32][33] Among these chemometric tools, we highlight the simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) 31,34 and the PURE function. 19 Another possibility is the use of individual matrices, for one sample, or augmented matrices, with the signal of more than one sample.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Experimental Parameters in Analysis of Pharmaceutical Pellets by Near Infrared-Chemical Imaging (NIR-CI) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS)

Da-Col¹,

Poppi²

2018

J. Braz. Chem. Soc.

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The quality control of pellets homogeneity cannot be assessed by conventional techniques and near infrared-chemical imaging combined with multivariate curve resolution with alternating least squares is an attractive alternative. In this study, composition and spatial distribution of pellets components were determined after assessment of experimental parameters. The use of a 25 μm intermediate pixel size, an initial estimation matrix with instrumental signals for pure substances and individual matrices provided a model with explained variance of more than 99% and a value of 0.00263 for percentage of lack of fit. In addition, the similarity between the pure substances spectra and those recovered by the model were 0.9501 for sucrose, 0.9480 for starch, 0.9910 for ketoprofen and 0.5941 for SiO 2 . Chemical images were generated and show that the pellet is composed of an inert nucleus of starch and cellulose, surrounded by a ketoprofen layer. All this information was obtained quickly, in minutes, being an excellent alternative for pellets analysis.

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Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS)

Cited by 241 publications

References 52 publications

Localizing and Quantifying Carotenoids in Intact Cells and Tissues

Localizing and Quantifying Carotenoids in Intact Cells and Tissues

Determination of Processed Salmon Components Sticking to Polyethylene Terephthalate Coatings of Containers by FT-IR and Raman Vibrational Spectroscopy

Optimization of Experimental Parameters in Analysis of Pharmaceutical Pellets by Near Infrared-Chemical Imaging (NIR-CI) and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS)

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