PARAFAC models of fluorescence data with scattering: A comparative study

Elcoroaristizabal, Saioa; Bro, Rasmus; García, José María González; Alonso, Lucio

doi:10.1016/j.chemolab.2015.01.017

Cited by 56 publications

(30 citation statements)

References 15 publications

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“…Contributions to measured signal intensity from the first and second order Rayleigh scattering were removed and replaced with interpolated values. e use of interpolated values to remove scattering contribution has been shown to provide better and more meaningful results when dealing with EEMs compared to the data deletion [34].…”

Section: Measurements Of Fluorescence Excitation-emissionmentioning

confidence: 99%

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

Dramićanin

Acković

Zeković

et al. 2018

Journal of Spectroscopy

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Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. The classification of honey from fluorescence data is demonstrated with a linear discriminant analysis (LDA). When subjected to LDA, total fluorescence intensities of selected spectral regions provided classification of honey (natural or adulterated) with 100% accuracy. In addition, it is demonstrated that intensities of honey emissions in each of these spectral regions may serve as criteria for the discrimination between natural and fake honey.

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Section: Measurements Of Fluorescence Excitation-emissionmentioning

confidence: 99%

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

Dramićanin

Acković

Zeković

et al. 2018

Journal of Spectroscopy

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“…Compared to traditional EEMs, 68 HPSEC EEMs are affected by larger areas of scatter and a slightly reduced spectral range, both of which may influence the mathematical decomposition. 69 While the general influence of these factors has been investigated previously, 70 the detailed influence of variable size of scatter excision and changing spectral ranges on fluorescence modeling remains poorly understood and should be investigated further. While the identification of the primary factor responsible for the observed spectral differences between congruent fluorescence components is currently not identifiable, the onesample framework is best suited to investigate such issues since it would otherwise not be possible to make such observations using such a limited number of environmental samples.…”

Section: Hpsec Optical Properties: Beer's Law or Charge Transfer?mentioning

confidence: 99%

The One-Sample PARAFAC Approach Reveals Molecular Size Distributions of Fluorescent Components in Dissolved Organic Matter

Wünsch

Murphy

Stedmon

2017

Environ. Sci. Technol.

139

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Molecular size plays an important role in dissolved organic matter (DOM) biogeochemistry, but its relationship with the fluorescent fraction of DOM (FDOM) remains poorly resolved. Here high-performance size exclusion chromatography (HPSEC) was coupled to fluorescence emission-excitation (EEM) spectroscopy in full spectral (60 emission and 34 excitation wavelengths) and chromatographic resolution (<1 Hz), to enable the mathematical decomposition of fluorescence on an individual sample basis by parallel factor analysis (PARAFAC). The approach allowed cross-system comparisons of molecular size distributions for individual fluorescence components obtained from independent data sets. Spectra extracted from allochthonous DOM were highly similar. Allochthonous and autochthonous DOM shared some spectra, but included unique components. In agreement with the supramolecular assembly hypothesis, molecular size distributions of the fluorescence fractions were broad and chromatographically unresolved, possibly representing reoccurring fluorophores forming noncovalently bound assemblies of varying molecular size. Samples shared underlying fluorescence components that differed in their size distributions but not their spectral properties. Thus, in contrast to absorption measurements, bulk fluorescence is unlikely to reliably indicate the average molecular size of DOM. The one-sample approach enables robust and independent cross-site comparisons without large-scale sampling efforts and introduces new analytical opportunities for elucidating the origins and biogeochemical properties of FDOM.

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“…It is an accurate method to interpolate smooth but complex surfaces, which is exactly, what one would expect from a theoretical EEM. We suggest interpolating missing data whenever possible since the occurrence of non-converging models and models that converge in local minima has been shown to increase along with the proportion of missing values [56]. Therefore, eem_checkdata explicitly calculates the ratio of missing to nonmissing data to warn the user of this potential risk.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Except in the case of the Fjord6 dataset, multiple convergent solutions were obtained using staRdom and a reasonable model could be obtained from within the subset of models that converged. As a solution for slow and incomplete convergence, Reference [56] demonstrated the interpolation of missing data. staRdom monitors the number of models that converged within the specified number of iterations.…”

Section: Number Of Initializationsmentioning

confidence: 99%

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staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R

Pucher

Wuensch

Weigelhofer

et al. 2019

Water

168

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The roles of dissolved organic matter (DOM) in microbial processes and nutrient cycles depend on its composition, which requires detailed measurements and analyses. We introduce a package for R, called staRdom (“spectroscopic analysis of DOM in R”), to analyze DOM spectroscopic data (absorbance and fluorescence), which is key to deliver fast insight into DOM composition of many samples. staRdom provides functions that standardize data preparation and analysis of spectroscopic data and are inspired by practical work. The user can perform blank subtraction, dilution correction, Raman normalization, scatter removal and interpolation, and fluorescence normalization. The software performs parallel factor analysis (PARAFAC) of excitation–emission matrices (EEMs), including peak picking of EEMs, and calculates fluorescence indices, absorbance indices, and absorbance slope indices from EEMs and absorbance spectra. A comparison between PARAFAC solutions by staRdom in R compared with drEEM in MATLAB showed nearly identical solutions for most datasets, although different convergence criteria are needed to obtain similar results and interpolation of missing data is important when working with staRdom. In conclusion, staRdom offers the opportunity for standardized multivariate decomposition of spectroscopic data without requiring software licensing fees and presuming only basic R knowledge.

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PARAFAC models of fluorescence data with scattering: A comparative study

Cited by 56 publications

References 15 publications

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

The One-Sample PARAFAC Approach Reveals Molecular Size Distributions of Fluorescent Components in Dissolved Organic Matter

staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R

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