Quantitative analysis of 16-17th century archaeological glass vessels using PLS regression of EPXMA and?�-XRF data

Lemberge, P.; Raedt, I. De; Janssens, Koen; Wang, Fuzhong; Espen, Pierre J. Van

doi:10.1002/1099-128x(200009/12)14:5/6<751::aid-cem622>3.0.co;2-d

Cited by 50 publications

(38 citation statements)

References 5 publications

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“…PCA is a useful technique for revealing the similarities in covariance or correlation between on the one hand different elements and on the other hand different samples when a series of samples has been characterized by means of a series of their elemental constituents (Lemberge et al, 2000). As can be seen in Fig.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Raw materials for medieval to post-medieval Tuscan glassmaking: new insight from LA-ICP-MS analyses

Cagno

Mendera

Jeffries

et al. 2010

Journal of Archaeological Science

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Raw materials for medieval to post-medieval Tuscan glassmaking: new insight from LA-ICP-MS analyses

Cagno

Mendera

Jeffries

et al. 2010

Journal of Archaeological Science

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“…69 The PLS regression is also suitable for making quantitative analysis of sixteenth to seventeenth century archaeological glass vessels by EPXMA and -XRF data. 78 Compared with partial least squares, polynomial partial least squares, partial least square neural networks, linear regression and corrected intensity, the ANN produces better predictions than the other methods, for both sulfur and lead, allowing their simultaneous determination in solid samples with good accuracy. 79 In general, ANNs can produce accurate prediction with robustness, but take a long time to train models.…”

Section: Related Methods and Comparison Among Themmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

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“…To fix ideas we will consider an example. The glass data set consists of spectra with d = 750 wavelengths resulting from spectroscopy on n = 180 archeological glass samples (Lemberge et al, 2000). Figure 6 shows the 180 curves.…”

Section: Functional Directional Outlyingnessmentioning

confidence: 99%

A Measure of Directional Outlyingness With Applications to Image Data and Video

Rousseeuw

Raymaekers

Hubert

2018

Journal of Computational and Graphical Statistics

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Functional data covers a wide range of data types. They all have in common that the observed objects are functions of of a univariate argument (e.g. time or wavelength) or a multivariate argument (say, a spatial position). These functions take on values which can in turn be univariate (such as the absorbance level) or multivariate (such as the red/green/blue color levels of an image). In practice it is important to be able to detect outliers in such data. For this purpose we introduce a new measure of outlyingness that we compute at each gridpoint of the functions' domain. The proposed directional outlyingness (DO) measure accounts for skewness in the data and only requires O(n) computation time per direction. We derive the influence function of the DO and compute a cutoff for outlier detection. The resulting heatmap and functional outlier map reflect local and global outlyingness of a function. To illustrate the performance of the method on real data it is applied to spectra, MRI images, and video surveillance data.

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Quantitative analysis of 16-17th century archaeological glass vessels using PLS regression of EPXMA and?�-XRF data

Cited by 50 publications

References 5 publications

Raw materials for medieval to post-medieval Tuscan glassmaking: new insight from LA-ICP-MS analyses

Raw materials for medieval to post-medieval Tuscan glassmaking: new insight from LA-ICP-MS analyses

Chemometrics and its applications to x‐ray spectrometry

A Measure of Directional Outlyingness With Applications to Image Data and Video

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