Curve resolution for multivariate images with applications to TOF-SIMS and Raman

Gallagher, Neal B.; Shaver, Jeremy M.; Martin, E.B.; Morris, Julian; Wise, Barry M.; Windig, Willem

doi:10.1016/j.chemolab.2004.04.003

Cited by 76 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternating least squaresbased multivariate curve resolution (MCR-ALS) is another common factorization method used for spectral image analysis. [4,7,8,13] This technique typically forces spectral and abundance factors to be nonnegative. Although the MCR-ALS factor model will not, in general, fit the data as well as the PCA model, the hope is that the application of constraints will yield a more physically realistic description of the pure components.…”

Section: Introductionmentioning

confidence: 99%

Exploiting spatial‐domain simplicity in spectral image analysis

Keenan

2009

Surface & Interface Analysis

View full text Add to dashboard Cite

Full-spectrum imaging is fast becoming a tool of choice for characterizing heterogeneous materials. Spectral images, which consist of a complete spectrum at each point in a spatial array, can be acquired from a wide variety of surface and microanalytical spectroscopic techniques. It is not uncommon that such spectral image data sets comprise tens of thousands of individual spectra, or more. Given the vast quantities of raw spectral data, factor analysis methods have proved indispensable for extracting the chemical information from these high-dimensional data sets into a limited number of factors that represent the spectral and spatial characteristics of the sample's composition. It is well known that factor models suffer a 'rotational ambiguity', that is, there are an infinite number of factor models that will fit the data equally well. Thus, physically inspired constraints are often employed to derive relatively unique models that make the individual factors more easily interpreted by the practicing analyst. In the present work, we note that many samples undergoing spectral image analysis are 'simple' in the sense that only one or a few of the sample's constituents are present at any particular location. When this situation prevails, simplicity in the spatial domain can be exploited to make the resulting factor models more realistic. In particular, orthogonal rotation of the spatial-domain vectors arising from singular value decomposition (SVD) of the spectral data matrix will be shown to be an effective method for making physically acceptable and easily interpretable estimates of the pure-component spectra and abundances.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploiting spatial‐domain simplicity in spectral image analysis

Keenan

2009

Surface & Interface Analysis

View full text Add to dashboard Cite

show abstract

“…Alternatively, the initial estimates can be extracted directly from the data using purity based methods. These methods can be either interactive, like simple-to-use interactive self-modelling mixture analysis (SIMPLISMA) [33] or automatic and based on the identification of the more extreme sample points in the data matrix [34]. This last method is based on the observation that the true spectra must lie on the exterior of the data space and that the measured spectra at the extremes of the data space provide a useful first estimate.…”

Section: Ma-xrf Data Analysismentioning

confidence: 99%

Jackson Pollock’s Number 1A, 1948: a non-invasive study using macro-x-ray fluorescence mapping (MA-XRF) and multivariate curve resolution-alternating least squares (MCR-ALS) analysis

et al. 2016

View full text Add to dashboard Cite

Jackson Pollock's Number 1A, 1948 painting was investigated using in situ scanning macro-x-ray fluorescence mapping (MA-XRF) to help characterize the artist's materials and his creative process. A multivariate curve resolution-alternating least squares (MCR-ALS) approach was used to examine the hyperspectral data and obtain distribution maps and signature spectra for the paints he used. The composition of the paints was elucidated based on the chemical elements identified in the signature spectra and a tentative list of pigments, fillers and other additives is proposed for eleven different paints and for the canvas. The paint distribution maps were used to virtually reconstruct the artist process and document the sequence and manner in which Pollock applied the different paints, using deliberate and specific gestures.

show abstract

“…Reference spectra can be used if available but this is not feasible in the particular case of XRF analysis of heterogeneous layered materials such as paint layers in a painting, although methods have been proposed to correct for intra and interlayer absorption effect [9,32]. Instead, initial estimates can be extracted in an interactive way from the data using a method such as SIMPLISMA (SIMPLe-to-use self modelling mixture analysis) [33] or automatically, and in an iterative way, using a method based on the selection of the purest pixel in the image data set [34]. This last method was chosen and is available in the SOLO+MIA MCR-ALS options (exteriorpts).…”

Section: Multivariate Image Analysismentioning

confidence: 99%

Piet Mondrian’s Broadway Boogie Woogie: non invasive analysis using macro X-ray fluorescence mapping (MA-XRF) and multivariate curve resolution-alternating least square (MCR-ALS)

et al. 2016

View full text Add to dashboard Cite

Piet Mondrian's Broadway Boogie Woogie (1942Woogie ( -1943 was examined using Macro X-Ray Fluorescence mapping (MA-XRF) to help characterize the artist's materials and understand his creative process as well as the current condition issues of the painting. The presence and distribution of key chemical elements was used to identify the main pigments in the different paint layers and under-layers, namely titanium white/barium sulfate, zinc white, bone black, cadmium yellow and/or cadmium-zinc yellow, cadmium red and/or cadmium-barium red and ultramarine. The XRF data was also examined using a multivariate curve resolution-alternating least square (MCR-ALS) approach to virtually separate and help characterize the different paint layers. Results suggest that Broadway Boogie Woogie was originally conceived as an asymmetrical grid of interlacing red and yellow bars. Mondrian then reworked the composition extensively breaking the bars by painting small squares in red, blue and gray and repainting them over and over again changing their size, color or tonality, and by adding and reworking large colored shapes in the background. Mondrian scraped off the paint in some areas before making adjustments to the composition but did not do it consistently throughout the painting. The yellow paint on the surface is severely cracked. Wherever red paint has been covered with yellow paint, it has oozed through the cracks in the top layer. The results illustrate how the MA-XRF / MCR-ALS approach can complement the examination of a painting and contribute to the understanding of the artist's process and choice of materials in a non-invasive way.

show abstract

Curve resolution for multivariate images with applications to TOF-SIMS and Raman

Cited by 76 publications

References 35 publications

Exploiting spatial‐domain simplicity in spectral image analysis

Exploiting spatial‐domain simplicity in spectral image analysis

Jackson Pollock’s Number 1A, 1948: a non-invasive study using macro-x-ray fluorescence mapping (MA-XRF) and multivariate curve resolution-alternating least squares (MCR-ALS) analysis

Piet Mondrian’s Broadway Boogie Woogie: non invasive analysis using macro X-ray fluorescence mapping (MA-XRF) and multivariate curve resolution-alternating least square (MCR-ALS)

Contact Info

Product

Resources

About