Chemometrics and its applications to x‐ray spectrometry

Luo, Liqiang

doi:10.1002/xrs.894

Cited by 32 publications

(27 citation statements)

References 131 publications

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“…Multivariate analysis is rapidly gaining popularity as a tool for the interpretation of spectral data, and principal components analysis (PCA) is one of the most used techniques for discerning structural relationships between samples based on the projection of data [9][10][11][12]. PCA reduces the dimensionality in a data set while retaining the PCA is an ideal approach for the direct examination of XANES spectra where subtle spectral differences are sometimes difficult to discern visually or with conventional data modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

Characterization of green copper phase pigments in Egyptian artifacts with X-ray absorption spectroscopy and principal components analysis

Lau

Kappen

Strohschnieder

et al. 2008

Spectrochimica Acta Part B: Atomic Spectroscopy

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The characterization of materials in historical artifacts can contribute significantly to their preservation and understanding; however, sampling and characterization are ideally performed using non-destructive approaches. The analysis of green pigments from Egyptian artifacts presents a further challenge as responses to laboratory based techniques have proven unsuccessful in many cases. An alternative approach is the use of non-destructive X-ray absorption near-edge structure spectroscopy, which was performed on a reference set of copper-containing green minerals and other compounds. Data projection using principal component analysis was used to explore the spectral data structures and to illustrate the relationship between the spectra and copper speciation, resulting in a calibration or training set of the reference materials used. Data from the training set were compared with samples from Egyptian artifacts. The combination of X-ray absorption spectroscopy with principal components analysis provides a novel approach in archeometry and the characterization of objects of cultural heritage.Crown

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Section: Introductionmentioning

confidence: 99%

Characterization of green copper phase pigments in Egyptian artifacts with X-ray absorption spectroscopy and principal components analysis

Lau

Kappen

Strohschnieder

et al. 2008

Spectrochimica Acta Part B: Atomic Spectroscopy

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“…This process repeats until the assignments do not change from one iteration to another. [23] The NMF algorithm provides a pattern recognition scheme, [24] which is responsible for finding the main characteristics and the similarities of all spectra. However, the most important function of NMF here is the data reduction that enables the use of k-means, which is less effective in the cluster identification in cases of a large number of variables.…”

Section: K-means Algorithmmentioning

confidence: 99%

Multivariate analysis applied to particle‐induced X‐ray emission mapping

2018

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The use of particle‐induced X‐ray emission (PIXE) for elemental speciation and quantification has gained new attention thanks to mapping capabilities. Microprobes are able to raster a proton beam and produce elemental maps on the micrometre scale. Moreover, recent developments of in‐air PIXE instrumentation have enabled the acquisition of large area elemental maps. However, the amount of data produced is very large, and the data processing is not trivial. In this paper, we propose the use of multivariate analysis to process data of PIXE mapping. First, we apply the non‐negative matrix factorization (NMF), which is a nonsupervised machine‐learning algorithm, to decompose the data into a smaller number of components; then, we use the k‐means algorithm to divide the pixels into categories regarding similarities in the NMF results; finally, we sum the spectra of all pixels in the same category so that the results can be analyzed by standard procedures for PIXE quantification. This last step is important to enable the quantification of the elements found in each component by correctly accounting for matrix self‐absorptions. With the procedure described in this paper, not just, we reduced the number of variables, facilitating the reasoning process on the data by employing the multivariate analysis, but we also increased the counting statistics by summing similar pixels leading to better results concerning the quantification of trace elements. We also propose methods for both, the automatic determination of the optimal number of components to describe the dataset, and for the combined analysis of multiple detectors.

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“…It is very common to use non-destructive methodologies like gamma-ray spectrometry and X-ray spectrometry for soil characterization (West et al, 2013;Kuang et al, 2012). Furthermore, because of advances in analytical instrumentation, it is now possible to generate large data sets that are difficult to evaluate using simple univariate statistical methods, especially due to their complexity and to their multivariate nature (Luo, 2006). Consequently, multivariate methods have been widely applied to investigate and interpret the large amounts of data generated by current spectrometric methods (Bagur-González et al, 2009;Dragovic and Onjia, 2006).…”

Section: Introductionmentioning

confidence: 99%