Simultaneous determination of Solvent Yellow 124 and Solvent Red 19 in diesel oil using fluorescence spectroscopy and chemometrics

Orzel, J.; Daszykowski, M.; Grabowski, I.; Zaleszczyk, G.; Sznajder, M.; Walczak, Beata

doi:10.1016/j.talanta.2012.08.031

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…The N-PLS is a generalization of the classic PLS regression for higher order data arrays, 28 using the multi-dimensional structure of the data for model building and prediction, which potentially gives increased interpretability and better prediction. 29 N-PLS model is fitted in such a way that it maximizes the covariance between dependent and independent variables.…”

Section: N-plsmentioning

confidence: 99%

N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of multiple compounds

Zhang

Feng

et al. 2019

Journal of Near Infrared Spectroscopy

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Because of the complexity of near infrared spectral data, effective strategies are necessary proposed for accurate quantitative analysis purpose. This work explores a new self-construction strategy for the arrangement of conventional near infrared two-dimensional spectra into new self-constructed three-dimensional spectra, and investigate the feasibility of N-way partial least squares combined with the new self-constructed three-dimensional near infrared spectra for obtaining accurate quantitative determination results. A proof-of-concept model system, the quantitative analysis of four components (moisture, oil, protein, and starch) in corn samples, was applied to evaluate the performance of the proposed strategy. The ability of the newly proposed approach to predict the target compounds was checked with test samples. The established models have good predictive power for the target compounds with acceptable values of Rp (range from 0.82 to 0.997) and RMSEP (range from 0.03 to 0.47). Compared with partial least squares method on pretreated near infrared spectra and N-way partial least squares method on the basis of near infrared self-constructed three-dimensional spectra, the proposed method is competitive.

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Section: N-plsmentioning

confidence: 99%

N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of multiple compounds

Zhang

Feng

et al. 2019

Journal of Near Infrared Spectroscopy

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“…The fluorescence methods were being increasingly recognized because of their excellent sensitivity, selectivity, non-invasiveness, rapidity and lowcost equipment [9]. However the usage of spectrofluorimetry in real analyses is hampered by the complexity of real sample matrices, which may have a great variety of inherently fluorescent compounds, whose spectra appear overlapped [10]. Biodiesel and diesel show native fluorescence [11,12], and they are examples of very complex matrix, a mixture of thousands of individual compounds with carbon numbers between 9 and 23 (number of carbon atoms per hydrocarbon molecule).…”

Section: Introductionmentioning

confidence: 99%

Determination of 2-ethylhexyl Nitrate in Diesel Oil Using a Single Excitation Emission Fluorescence Spectra (EEF) and Chemometrics Analysis

Insausti¹,

B²

2017

J Fundam Renewable Energy Appl

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A highly sensitive spectrofluorimetric method has been developed for the determination of 2-ethylhexyl nitrate in diesel fuel. Usually, this compound is used as an additive in order to improve cetane number.The analytical method consists in building the chemometric model as a first step. Then, it is possible to quantify the analyte with only recording a single excitation-emission fluorescence spectrum (EEF), whose data are introduced in the chemometric model above mentioned. Another important characteristic of this method is that the fuel sample was used without any pre-treatment for EEF.This work provides an interest improvement to fluorescence techniques using the rapid and easily applicable EEF approach to analyze such complex matrices. Exploding EEF was the key to a successful determination, obtaining a detection limit of 0.00434% (v/v) and a limit of quantification of 0.01446% (v/v overlapping of the spectral information might be overcome by using multicomponent analysis such as partial least squares (PLS) [21]. This method allowed an approach using the full spectral region rather than unique and isolated analytical bands. PLS performs data decomposition into spectral score and loading matrices prior to model building with the aid of these new variables, using spectral and concentration data. The algorithm was based on the ability to mathematically correlate spectral data to a property of interest while simultaneously accounting for all other significant spectral factors that interfered in the spectrum.The full cross-validation leave-one-out procedure was applied, which consists of systematically removing one of the training samples in turn, and using only the remaining ones for the construction of the latent factors and regression models [22].The predictive capability of calibration model was expressed by the determination coefficient (R-square), root mean square error of cross validation (RMSECV), and standard error of prediction (SEP).The optimal number of principal components (PCs) was chosen in function of the first local minimum based on the RMSEC curve after cross validation in the calibration set. Predictive capability of model was evaluated by predicting the external prediction set with commercial formulation. Results and DiscussionConventional fluorescence may be preferred to determine one fluorophor concentration or when the mixture being analyzed contains only a limited number of compounds with fluorescence properties and a quenching effect are not to be expected. Ignoring the excitation spectra by using only the emission spectra could not be enough to obtain the solutions of the problem. So, besides the emission spectra, it seems essential to include also the analysis of excitation spectra possessing abundant information. The determination in diesel oil may contain many fluourophors. This task is further complicated by the fact that the fluorophors of interest have different characteristic excitation and emission ranges in the different samples. An inherent problem of many fluorimetric ...

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“…Detection of fuel laundering specifically requires an analytical technique that is capable of revealing chemical changes in the composition of the fuel, since the removal of the excise duty components does not influence its physicochemical parameters. In our previous studies, we developed an analytical methodology to detect any chemical changes before and after a simulated laundering process by using diesel fuel fingerprints that were obtained using fluorescence spectroscopy [ 2 , 3 ]. It was confirmed that genuine samples can definitely be discriminated from samples after the laundering process.…”

Section: Introductionmentioning

confidence: 99%

“…Differences between these two groups of samples were studied using the partial least squares discriminant analysis, PLS-DA [ 12 ]. The removal of baseline and the alignment of peaks were performed to the sample chromatographic fingerprints using penalized asymmetric least squares approach (PAsLS) [ 1 ], and correlation optimized warping (COW) [ 2 ], respectively. In order to identify the key regions that are related to the chemical differences of sample groups, the PLS-DA approach was extended with variable selection.…”

Section: Introductionmentioning

confidence: 99%

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints

et al. 2014

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In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel “laundering”, is an illegal action that causes a substantial loss in a government’s budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets.

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Simultaneous determination of Solvent Yellow 124 and Solvent Red 19 in diesel oil using fluorescence spectroscopy and chemometrics

Cited by 19 publications

References 21 publications

N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of multiple compounds

N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of multiple compounds

Determination of 2-ethylhexyl Nitrate in Diesel Oil Using a Single Excitation Emission Fluorescence Spectra (EEF) and Chemometrics Analysis

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints

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