Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel–kerosene mixtures: A comparative study

Divya, O.; Mishra, Ashok Kumar

doi:10.1016/j.aca.2007.03.079

Cited by 55 publications

(21 citation statements)

References 24 publications

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“…If the core consistency is not close to 100%, the model does not give an appropriate description of the data and a lower number of components should be chosen. As shown in Table 1, when more than four components were used, core consistency value became very low, which indicated that the model was over fitted or unstable (Andersen and Bro, 2003;Divya and Mishra, 2007).…”

Section: Componential Characterization Of Smp and Efom Samplesmentioning

confidence: 99%

Relationship between soluble microbial products (SMP) and effluent organic matter (EfOM): Characterized by fluorescence excitation emission matrix coupled with parallel factor analysis

Sun

et al. 2015

Chemosphere

123

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Section: Componential Characterization Of Smp and Efom Samplesmentioning

confidence: 99%

Relationship between soluble microbial products (SMP) and effluent organic matter (EfOM): Characterized by fluorescence excitation emission matrix coupled with parallel factor analysis

Sun

et al. 2015

Chemosphere

123

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“…According to Jerome and Workman (2008), RMSEC, RMSECV and RMSEP values should be as low as possible (close to 0), while R 2 should be as high as possible (close to 1). Thus, the low RMSEC and RMSECV values and high R 2 value (close to 1) confirm the ruggedness of the models (Divya and Mishra, 2007). Then, the PLS model was used to predict the adulterant concentration in the prediction data set as follows.…”

Section: Parafac-pls Of Apple Spirit Blendsmentioning

confidence: 92%

“…To determine the number of components, various numerical characteristics of the model can be used e.g. explained variance (%) (Surribas et al, 2006) and core consistency (corcondia) (%) (Divya and Mishra, 2007) -ideally, both values are 100 %. The results of PARAFAC modelling are relative concentrations (score) and spectral profiles (loadings) of components in the samples (Bro 1997).…”

Section: Multivariate Analysismentioning

confidence: 99%

Determination of adulterants in adulterant-fruit spirit blends using excitation-emission matrix fluorescence spectroscopy

Jakubíková

Sádecká

Májek

2015

Acta Chimica Slovaca

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This study introduces a reliable method to detect adulteration of spirit drinks. Excitation-emission matrix (EEM) fluorescence in combination with parallel factor analysis (PARAFAC) and partial least squares (PLS) regression was used to determine the content of water and ethanol in adulterated fruit spirit samples. EEM fluorescence spectra recorded in the emission wavelength range of 315-450 nm and in the excitation wavelength range of 240-305 nm were used for PARAFAC. The model created using PARAFAC-PLS was able to predict the water and ethanol level in adulterated apple spirit with the root mean square error of prediction (RMSEP) values of 1.9 % and 1.8 %, respectively. Regarding adulterated plum spirit, the RMSEP values of 0.7 % and 3.5 % were obtained for water and ethanol, respectively. The aim of this work was to determine whether EEM-PARAFAC can be used to distinguish between plum and apple spirit. Better results were obtained for apple spirit and the method is useful also for water-apple spirit blends.

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“…Examples of the fluorescence data analysis using PARAFAC and N-PLS include characterization of the organic matter-metal binding process (Ohno et al, 2008), quantitative determination of the kerosene fraction present in diesel (Divya and Mishra, 2007), classification of ballast water (Hall et al, 2005), estuarine water (Stedmon et al, 2003) and edible oils (Guimet et al, 2005).…”

Section: Multiway Analysismentioning

confidence: 99%

Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment

2010

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Fluorescence spectroscopy enables fast and sensitive analysis of environmental samples containing various organic matter constituents. However, to retrieve valuable information from fluorescence spectra, robust techniques for data analysis should be employed. Here, different multivariate analysis methods and artificial neural networks (ANNs) were applied for decomposition and calibration of fluorescence excitation-emission matrices (EEMs). This is the first paper summarizing the application of different data mining methods, from multiway analysis to ANNs, for fluorescence EEMs technique employed to characterize organic matter properties and removal in the field of drinking water treatment. Fluorescence analysis was carried out on municipal water treatment samples of raw and partially-treated water. Parallel factor analysis (PARAFAC) method and self-organizing maps were used to analyse EEMs, extract information on the organic matter constituents and reduce the dimensionality of the data to enhance the efficiency of calibration methods. Partial least squares (PLS), multiple linear regression (MLR) and neural network with back-propagation were employed for calibration of fluorescence data with actual total organic carbon (TOC) concentrations. All models except PARAFAC-MLR produced consistent results with correlation coefficient R 2 ¼ 0.93 for validation dataset. This is the first such comparative analysis of fluorescence data modelling that clarifies fundamental fluorescence data analysis questions regarding the suitability of different decomposition and calibration methods.

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Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel–kerosene mixtures: A comparative study

Cited by 55 publications

References 24 publications

Relationship between soluble microbial products (SMP) and effluent organic matter (EfOM): Characterized by fluorescence excitation emission matrix coupled with parallel factor analysis

Relationship between soluble microbial products (SMP) and effluent organic matter (EfOM): Characterized by fluorescence excitation emission matrix coupled with parallel factor analysis

Determination of adulterants in adulterant-fruit spirit blends using excitation-emission matrix fluorescence spectroscopy

Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment

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