Application of the method of rank annihilation to quantitative analyses of multicomponent fluorescence data from the video fluorometer

Ho, C. N.; Christian, Gary D.; Davidson, Ernest R.

doi:10.1021/ac50030a026

Cited by 248 publications

(103 citation statements)

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“…Excellent reviews and books exist on the matter. [12][13][14] Multi-way calibration is relatively new in this regard: the first work describing a secondorder calibration was published in 1978, 47 reporting the determination of polycyclic aromatic hydrocarbons in the presence of potential interfering agents. Then an impasse of ca.…”

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confidence: 99%

Analytical Figures of Merit: From Univariate to Multiway Calibration

2014

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confidence: 99%

Analytical Figures of Merit: From Univariate to Multiway Calibration

2014

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“…Rank annihilation factor analysis (RAFA) is an effective chemometric tool that the idea behind it, is the rank analysis of two way spectrum data, and quantitatively can be used to analysis of mix systems with unknown background. The original algorithm of RAFA was written by Ho et al and applied on one and two component mixtures of perylene and anthracene [20][21][22]. They showed that they were able to determine the concentration of one analyte in presence of the other using only one pure standard and using fluorescence excitation emission landscapes [23].…”

Section: Introductionmentioning

confidence: 99%

Application of central composite design and rank annihilation factor analysis for optimization of mixed chelate of UO₂ ²⁺ complex with ammonium-N,N-tetramethylenedithiocarbamate and estimation of K_f value

Aslani¹,

Kuru²,

Aslani³

2015

Radiochimica Acta

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Abstract:The interaction between UO 2+ 2 and ammonium-N,N-tetramethylenedithiocarbamate (ATMDTC) was investigated experimentally by means of UV-Vis spectrophotometry. Central Composite Design (CCD) was applied to optimisation of reaction with 54 runs in duplicate. This design type was also used to developed mathematical model equation. Analysis of Variance (ANOVA) indicates that the terms of the model are significant within the 95% confident interval. Remarkably initial solution pH, initial AT-MDTC concentration, initial Cu 2+ concentration and temperature have effect on the reaction. Formation constant (log f ) was calculated as 2.917 by using Rank Annihilation Factor Analysis (RAFA).

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“…This property is the so-called "second-order advantage" 1 and it is based on the earlier work done in the psychometrics field. 2,3 The use of this advantage in analytical chemistry was proposed by Ho et al 4 (rank annihilation factor analysis, RAFA), for the multicomponent analysis of fluorescent mixtures using excitation-emission matrix (EEM). Later, Lorber 5 and Sanchez and Kowalski 6 proposed new and simple solutions for the method of Ho et al and now it is called the generalized rank annihilation method (GRAM).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Spectrofluorometric Determination of Piroxicam and Pyridoxine Using Generalized Rank Annihilation Method

2006

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One of the most serious problems that can occur in classical quantitative analysis is the presence of one or more spectral interferent-chemical species which affect the instrument response and which are unaccounted for in the calibration process. Advances in chemometric methods allow quantitative analysis in the presence of unidentified interferents if a threeway experimental data matrix is available for each sample. This property is the so-called "second-order advantage" 1 and it is based on the earlier work done in the psychometrics field. 2,3 The use of this advantage in analytical chemistry was proposed by Ho et al. 4 (rank annihilation factor analysis, RAFA), for the multicomponent analysis of fluorescent mixtures using excitation-emission matrix (EEM). Later, Lorber 5 and Sanchez and Kowalski 6 proposed new and simple solutions for the method of Ho et al. and now it is called the generalized rank annihilation method (GRAM).At present, analytical chemistry laboratories have instrumentation that easily generates multidimensional data structures of experimental data for each sample. Fluorescence is a particularly interesting technique because it allows, in a very straightforward manner, the acquisition of this type of information, of which the most typical example is the excitation-emission matrix (EEM). Several algorithms that use second-order data are described in the literature. [7][8][9] Of these, the generalized rank annihilation method (GRAM) 6,11,12 has interesting properties since it only requires two data matrices for quantification: one from the calibration sample and the other from the unknown sample. Recently, it has been proved that GRAM predicts results that are similar to those from the parallel factor analysis (PARAFAC) when only two samples are used, 9 so both methods can be useful as calibration methods for second-order data. But, unlike PARAFAC, GRAM is a non-iterative method which tries to implement the second-order advantage. Second-order calibration using GRAM has several distinct advantages. 13The most pronounced advantage is that it makes possible the quantification of the analyte(s) of interest in samples containing interferents. Those interferents can be completely unknown, that is, it is not necessary that they have been incorporated in the standards used for calibration. Another advantage is that it is possible to recover the individual instrument responses of the analyte(s) of interest, e.g., the excitation and emission spectra can be obtained for the individual analyte(s). This makes it possible to perform qualitative analysis and to check the identity of the analytes. The Kowalski group has proposed the trilinear decomposition (TLD) method. 14,15 In this work we focused our attention on the ability of GRAM for analyzing the second-order data.Fluorescence spectroscopy is a versatile tool mainly used because of its selectivity and sensitivity. Fluorescence spectra can be recorded in difference modes such as emission, excitation, synchronous and excitation-emission matrix (EEM). T...

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Application of the method of rank annihilation to quantitative analyses of multicomponent fluorescence data from the video fluorometer

Cited by 248 publications

References 7 publications

Analytical Figures of Merit: From Univariate to Multiway Calibration

Analytical Figures of Merit: From Univariate to Multiway Calibration

Application of central composite design and rank annihilation factor analysis for optimization of mixed chelate of UO₂ ²⁺ complex with ammonium-N,N-tetramethylenedithiocarbamate and estimation of K_f value

Simultaneous Spectrofluorometric Determination of Piroxicam and Pyridoxine Using Generalized Rank Annihilation Method

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Application of the method of rank annihilation to quantitative analyses of multicomponent fluorescence data from the video fluorometer

Cited by 248 publications

References 7 publications

Analytical Figures of Merit: From Univariate to Multiway Calibration

Analytical Figures of Merit: From Univariate to Multiway Calibration

Application of central composite design and rank annihilation factor analysis for optimization of mixed chelate of UO2 2+ complex with ammonium-N,N-tetramethylenedithiocarbamate and estimation of Kf value

Simultaneous Spectrofluorometric Determination of Piroxicam and Pyridoxine Using Generalized Rank Annihilation Method

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Application of central composite design and rank annihilation factor analysis for optimization of mixed chelate of UO₂ ²⁺ complex with ammonium-N,N-tetramethylenedithiocarbamate and estimation of K_f value