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Journal ArticleDOI

Chemometric Study of Excitation–Emission Matrix Fluorescence Data: Quantitative Analysis of Petrol–Kerosene Mixtures

01 Jul 2008-Applied Spectroscopy (Society for Applied Spectroscopy)-Vol. 62, Iss: 7, pp 753-758
TL;DR: EEMF spectroscopic data is processed using chemometric multivariate methods to develop a reliable calibration model for the quantitative determination of kerosene fraction present in petrol and a very good degree of accuracy of prediction was achieved.
Abstract: Products of petroleum crude are multifluorophoric in nature due to the presence of a mixture of a variety polycyclic aromatic hydrocarbons (PAHs). The use of excitation–emission matrix fluorescence (EEMF) spectroscopy for the analysis of such multifluorophoric samples is gaining progressive acceptance. In this work, EEMF spectroscopic data is processed using chemometric multivariate methods to develop a reliable calibration model for the quantitative determination of kerosene fraction present in petrol. The application of the N-way partial least squares regression (N-PLS) method was found to be very efficient for the estimation of kerosene fraction. A very good degree of accuracy of prediction, expressed in terms of root mean square error of prediction (RMSEP), was achieved at a kerosene fraction of 2.05%.
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors present a review article that gives an overview on conceptual and analytical aspects of EEMF, SFS and TSFS for general as well as specialized fluorescence scientific community.
Abstract: Over the years mainly three unconventional fluorescence techniques, Excitation–emission matrix fluorescence (EEMF), synchronous fluorescence spectroscopy (SFS), and total synchronous fluorescence spectroscopy (TSFS) are introduced for the analysis of multifluorophoric mixtures. Application of EEMF, SFS and TSFS are conceptually different. The existing literature lacks a review article that gives an overview on conceptual and analytical aspects of EEMF, SFS and TSFS for general as well as specialized fluorescence scientific community. The present review article attempts to address these issues and discusses various conceptual and practical aspects of EEMF, SFS and TSFS spectroscopy. The present article contains numerous novel fluorescence parameters, concept of concentration dependent red shift, protocol for finding the optimum wavelength offset for SFS data acquisition is introduced, various practical aspects of integrating chemometric methods with TSFS and number of successful applications of EEMF, SFS and TSFS for the analysis of complex and simple multifluorophoric mixtures is discussed.

60 citations

Journal ArticleDOI
TL;DR: In this paper, a new approach for qualitative and quantitative analysis of adulterated milk is proposed by combining two-dimensional correlation spectroscopy (2DCOS) with multi-way partial least squares (N-PLS).
Abstract: A new approach for qualitative and quantitative analysis of adulterated milk is proposed by combining two-dimensional correlation spectroscopy (2DCOS) with multi-way partial least squares (N-PLS). Forty pure milk and 40 adulterated milk samples with melamine were prepared and the infrared absorption spectra were measured. The two-dimensional (2D) correlation spectra were calculated to construct the multi-way partial least squares discriminant analysis (NPLS-DA) model for milk adulterated with melamine. Our study showed the higher accuracy of predicting adulterated milk of 95% using NPLS-DA, versus 85% using conventional one-dimensional spectra and partial least squares discriminant analysis (PLS-DA). Also, the quantitative analysis models were constructed to determine concentration of melamine in milk. The root mean square error of prediction (RMSEP) was 0.106 g L−1 using the N-PLS method and 0.16 g L−1 using conventional one-dimensional spectra and partial least squares (PLS). Comparison results show that the proposed new method is superior to the traditional method and the use of 2DCOS with N-PLS is promising to quantify the adulterants in pure milk.

42 citations

Journal ArticleDOI
TL;DR: In this article, a calibration model for the quantification of ethanol in the ethanol-petrol and biodiesel in the biodiesel-diesel blends of a particular batch were made using the combination of synchronous fluorescence spectroscopy (SFS) with principal component regression (PCR) and partial least square (PLS) with excitation emission matrix fluorescence (EEMF) with N-way Partial least square and unfolded-PLS.
Abstract: Ethanol blended petrol and biodiesel blended diesel are being introduced in many countries to meet the increasing demand of hydrocarbon fuels. However, technological limitations of current vehicle engine do not allow ethanol and biodiesel percentages in the blended fuel to be increased beyond a certain level. As a result quantification of ethanol in blended petrol and biodiesel in blended diesel becomes an important issue. In this work, calibration models for the quantification of ethanol in the ethanol-petrol and biodiesel in the biodiesel-diesel blends of a particular batch were made using the combination of synchronous fluorescence spectroscopy (SFS) with principal component regression (PCR) and partial least square (PLS) and excitation emission matrix fluorescence (EEMF) with N-way Partial least square (N-PLS) and unfolded-PLS. The PCR, PLS, N-PLS and unfolded-PLS calibration models were evaluated through measures like root mean square error of cross-validation (RMSECV), root mean square error of calibration (RMSEC) and square of the correlation coefficient (R2). The prediction abilities of the models were tested using a testing set of ethanol-petrol and biodiesel-diesel blends of known ethanol and biodiesel concentrations, error in the predictions made by the models were found to be less than 2%. The obtained calibration models are highly robust and capable of estimating low as well as high concentrations of ethanol and biodiesel.

28 citations

Journal ArticleDOI
TL;DR: In this article, a 2D correlation infrared spectroscopy combined with multivariate methods were used for the classification of adulterated milk and pure milk in the mid-infrared range of 900-1700 cm−1.
Abstract: The discrimination analysis of adulterated milk has been carried out based on two-dimensional (2D) infrared correlation spectroscopy along with multivariate methods like kernel orthogonal projection to latent structure (K-OPLS), multi-way partial least squares discriminant analysis (NPLS-DA), and unfolded partial least squares discriminant analysis (PLS-DA). 2D correlation spectroscopy, due to high spectral resolution and good spectral interpretation capabilities, is suitable for the analysis of complex biological data. 64 pure milk samples and 64 adulterated milk samples were measured in the mid-infrared range of 900–1700 cm−1. Then, the synchronous 2D correlation spectra of all samples were calculated in the region of between 900–1200 cm−1 and 1200–1700 cm−1. Finally, the K-OPLS, NPLS-DA, and unfolded PLS-DA models were developed based on the synchronous 2D correlation spectra of adulterated milk and pure milk. The classification accuracy rates of unknown samples for K-OPLS, NPLS-DA, and unfolded PLS-DA models were 95%, 92.5%, and 92.5%, respectively. The results indicated that 2D correlation infrared spectroscopy combined with multivariate methods were feasible and efficient for discrimination of adulterated milk.

26 citations

Journal ArticleDOI
15 Nov 2012-Talanta
TL;DR: A novel analytical approach for a qualitative and quantitative determination of two diazo compounds that are usually added to diesel oil that combines the use of excitation-emission matrix fluorescence spectroscopy and partial least squares regression as a multiple modeling tool is described.

17 citations

References
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Journal ArticleDOI
TL;DR: A new multiway regression method called N‐way partial least squares (N‐PLS) is presented, which is superior to unfolding methods, primarily owing to a stabilization of the decomposition.
Abstract: A new multiway regression method called N-way partial least squares (N-PLS) is presented. The emphasis is on the three-way PLS version (tri-PLS), but it is shown how to extend the algorithm to higher orders. The developed algorithm is superior to unfolding methods, primarily owing to a stabilization of the decomposition. This stabilization potentially gives increased interpretability and better predictions. The algorithm is fast compared with e.g. PARAFAC, because it consists of solving eigenvalue problems. An example of the developed algorithm taken from the sugar industry is shown and compared with unfold-PLS. Fluorescence excitation—emission matrices (EEMs) are measured on white sugar solutions and used to predict the ash content of the sugar. The predictions are comparable by the two methods, but there is a clear difference in the interpretability of the two solutions. Also shown is a simulated example of EEMs with very noisy measurements and a low relative signal from the analyte of interest. The predictions from unfold-PLS are almost twice as bad as from tri-PLS despite the large number of samples (125) used in the calibration. The algorithms are available from World Wide Web: hhtp:\ ewton.foodsci.kvl.dk\foodtech.

660 citations

Book
01 Jan 1998
TL;DR: In this paper, a basic approach creating some data classical least squares inverse least squares factor spaces principal component regression PCR in action partial least squares PLS in action appendix A - matrices and matrix operations appendix B - errors -some definitions of terminology appendix C - centering and scaling appendix D - F-tests for reduced eigenvalues appendix E - leverage and influence.
Abstract: Basic approach creating some data classical least-squares inverse least-squares factor spaces principal component regression PCR in action partial least squares PLS in action appendix A - matrices and matrix operations appendix B - errors -some definitions of terminology appendix C - centering and scaling appendix D - F-tests for reduced eigenvalues appendix E - leverage and influence.

602 citations


"Chemometric Study of Excitation–Emi..." refers methods in this paper

  • ...When the model is applied to a new set of data it is possible to calculate a root mean square error of prediction (RMSEP), provided the reference values for the new data set are known.(22)...

    [...]

Journal ArticleDOI
J. B. F. Lloyd1
17 May 1971-Nature
TL;DR: In this article, the fluorescence emission of complex mixtures of fluorescent compounds sometimes cannot be satisfactorily resolved by the usual technique of excitation at various fixed wavelengths selected specifically for individual components.
Abstract: THE fluorescence emission of complex mixtures of fluorescent compounds sometimes cannot be satisfactorily resolved by the usual technique of excitation at various fixed wavelengths selected specifically for individual components. Considerable improvement in such spectra often can be made when excitation and emission wavelengths are varied together, so that the fluorescence contributed by each component is restricted to that excited at wavelengths synchronously trailing the plotted emission.

541 citations

Journal ArticleDOI
TL;DR: A review of synchronous fluorescence scan (SFS) methods for analysis of multi-component systems can be found in this paper, where the authors discuss the use of SFS in the analysis of complex multichannel mixtures.
Abstract: The ability to analyse complex multi-component mixtures without resorting to tedious separation procedures is extremely useful for routine analysis. Single-wavelength fluorescence measurement is limited in its ability to analyse complicated multi-component samples when they have severely overlapping emission and/or excitation spectra. This can be overcome by using synchronous fluorescence scan (SFS), where overlapping of spectra can be minimized. The selectivity of SFS can still be increased by taking derivative spectrum, applying different multivariate methods, selective fluorescence quenching, three-dimensional synchronous measurement or using some of these procedures in combination. Recent developments in various synchronous fluorescence methods for analysis of multi-component systems are discussed in this review.

277 citations

Journal ArticleDOI
TL;DR: The present work focuses on the analysis of fluorescence data from 1986 to 1991, which revealed clear trends in the direction of growth in eight major food categories: meat, fish, fruit, vegetables, dairy products, cereal, fruit and vegetables, and sugar.
Abstract: 3. Data Analysis 1985 3.1. Fluorescence Data Structure 1985 3.2. Chemometrics 1985 3.3. Multivariate Analysis of Fluorescence Data 1985 3.4. Multiway Analysis of Fluorescence Data 1985 4. Food Studies 1986 4.1. Meat 1987 4.2. Fish 1989 4.3. Dairy Products 1989 4.4. Edible Oils 1990 4.5. Cereals 1990 4.6. Beer 1991 4.7. Fruit and Vegetables 1991 4.8. Sugar 1991 5. Conclusions and Perspectives 1992 6. Acknowledgments 1992 7. References 1992

259 citations