Automatically identifying scatter in fluorescence data using robust techniques

Engelen, Sanne; Møller, Stina Frosch; Hubert, Mia

doi:10.1016/j.chemolab.2006.08.001

Cited by 35 publications

(30 citation statements)

References 28 publications

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“…The automatic scatter identification procedure is outlined in Engelen et al [2] and is established for dealing with the second type of outliers, that is the outlying elements. The algorithm starts by slicing the data cube X along the emission and excitation mode, respectively.…”

Section: The Combined Robust Algorithmmentioning

confidence: 99%

“…It has thus been proven in both Engelen and Hubert [1] and Engelen et al [2] that both types of outliers can be dealt with separately. On the other hand, no attention has been paid to data sets, which contain outlying samples as well as element-wise outliers.…”

Section: Introductionmentioning

confidence: 99%

“…Scatter can be considered as outlying elements or outliers of the second type, because this scatter does not contain any chemically relevant information and does not follow the tri-linear model. Several ways to handle scatter in relation to PARAFAC modeling can be found in the literature [7,11,[15][16][17][18][19][20][21] but treating scatter as outlying elements leads in Engelen et al [2] to the construction of the automated scatter identification method. The approach can handle both first and second order Rayleigh scatter as well as Raman scatter and require no a priori information about the scatter itself, like for example the placement or the width of the scatter ridges.…”

Section: Introductionmentioning

confidence: 99%

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A fully robust PARAFAC method for analyzing fluorescence data

Engelen

Frosch

Jørgensen

2009

Journal of Chemometrics

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Parallel factor analysis (PARAFAC) is a widespread method for modeling fluorescence data by means of an alternating least squares procedure. Consequently, the PARAFAC estimates are highly influenced by outlying excitation-emission landscapes (EEM) and element-wise outliers, like for example Raman and Rayleigh scatter. Recently, a robust PARAFAC method that circumvents the harmful effects of outlying samples has been developed. For removing the scatter effects on the final PARAFAC model, different techniques exist. Newly, an automated scatter identification tool has been constructed. However, there still exists no robust method for handling fluorescence data encountering both outlying EEM landscapes and scatter. In this paper, we present an iterative algorithm where the robust PARAFAC method and the scatter identification tool are alternately performed. A fully automated robust PARAFAC method is obtained in that way. The method is assessed by means of simulations and a laboratory-made data set.

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Section: The Combined Robust Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fully robust PARAFAC method for analyzing fluorescence data

Engelen

Frosch

Jørgensen

2009

Journal of Chemometrics

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“…Lager beer marketed as having organic certification (sample O22) was compared to three different mainstream lager beers (D6, E9 and P23). As shown in Figure 4, the organic beer sample had a higher intensity reading in the 395-400 nm, 825-830 nm (C-H) and 855-875 nm (presumably C-H aromatic groups) regions, compared to the other beers in the figure [14,15,[20][21][22]. …”

Section: Emission Excitation Matrix (Eem) Contour Plotsmentioning

confidence: 98%

“…It is important to note that Rayleigh scattering, which are the diagonal ridges in the contour plots, is a common phenomenon with EEMs and has no relevant Lager beer marketed as having organic certification (sample O22) was compared to three different mainstream lager beers (D6, E9 and P23). As shown in Figure 4, the organic beer sample had a higher intensity reading in the 395-400 nm, 825-830 nm (C-H) and 855-875 nm (presumably C-H aromatic groups) regions, compared to the other beers in the figure [14,15,[20][21][22]. Synchronous scanning fluorescence of selected lager beers and ales was compared to show the similarities and differences between these different beer styles ( Figure 3).…”

Section: Emission Excitation Matrix (Eem) Contour Plotsmentioning

confidence: 99%

Analysis of Australian Beers Using Fluorescence Spectroscopy

et al. 2017

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Classification of a series of Australian beers was performed using synchronous scanning fluorescence spectroscopy and emission-excitation matrices based on the IR fingerprint regions. The results indicate that synchronous scanning fluorescence spectroscopy is a robust and valuable method to discriminate between Australian lager beers based on their brand name. In addition, a subsequent spoiling study revealed that when beers are opened and stored at 4 • C for 4 weeks, the results demonstrated that the beers were not statistically different. The methods and techniques outlined may be of interest to brewing companies and microbrewers to determine the unique beer spectrum.

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