Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy (original) (raw)
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Fuel, 2008
In the present work, Fourier transform infrared spectroscopy (FTIR) in association with multivariate chemometrics classification techniques was employed to identify gasoline samples adulterated with diesel oil, kerosene, turpentine spirit or thinner. Results indicated that partial least squares (PLS) models based on infrared spectra were proven suitable as practical analytical methods for predicting adulterant content in gasoline in the volume fraction range from 0% to 50%. The results obtained by PLS provided prediction errors lower than 2% (v/v) for all adulterant determined. Additionally, Soft Independent Modeling of Class Analogy (SIMCA) was performed using all spectral data (650-3700 cm À1) for sample classification into adulterant classes defined by training set and the results indicated that undoubted adulteration detection was possible but identification of the adulterant was subject to misclassification errors, specially for kerosene and turpentine adulterated samples, and must be carefully examined. Quality control and police laboratories for gasoline analysis should employ the proposed methods for rapid screening analysis for qualitative monitoring purposes.
Gasoline classification by source and type based on near infrared (NIR) spectroscopy data
Fuel, 2008
In this paper, we have tried to classify 382 samples of gasoline and gasoline fractions by source (refinery or process) and type. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3 or 6 classes. We have compared the abilities of three different classification methods: linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA), and multilayer perceptron (MLP)-to build effective and robust classification model. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes. MLP technique was found to be the most effective method of classification model building.
Analytica Chimica Acta, 2010
Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP)-for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000-8000 cm −1 NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems.
Chromatographia, 2009
The combination of ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.
Talanta, 2010
The combination of ASTM D6733 gas chromatographic fingerprinting data with patternrecognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.
Energy & Fuels, 2007
Chemometric data analysis was applied to chromatographic data as a modeling tool to identify the presence of solvents in gasoline obtained at gas stations in the Minas Gerais state. As a training set, 75 samples were formulated by mixing pure gasolines with varying concentrations of four solvents and analyzed by gas chromatography-mass spectrometry. Selected chromatographic peak areas were used in chemometric analysis. Sample distribution patterns were investigated with principal component analysis (PCA). Score graphics revealed a clear sample agglomeration according to the solvents added. Classification models were created with linear discriminant analysis (LDA). Because gasoline presents a very complex profile and the chromatographic data contains too many variables, two approaches were tested to reduce the dimensionality of the data before LDA. Fisher weights were used as an exclusion criterion of lesser variables, and the original variables were substituted for a few principal components obtained from the covariance matrix. To test the quality of the models, a test set with a total of 31 new samples was prepared using certified gasolines mixed with the same solvents used in the training set. Both models indicated the presence of solvent in gasoline effectively, failing only for samples whose solvent concentrations were low. The PCA plus LDA model was more efficient in signaling solvent-free samples, which reduced the number of false positive cases.
2012
Due to dependence on the use of gasoline, traders see an opportunity to increase their profit by adding solvents such as ethanol, kerosene, turpentine and diesel. Attenuated total reflectance with Fourier transform infrared spectroscopy (ATR-FTIR) is a fast and nondestructive technique and requires little sample. In this work, the applicability of ATR-FTIR for detection of adulteration of Brazilian gasolines using liquid-liquid aqueous separation and chemometric tools was evaluated. Gasolines from 15 gas stations from the cities of Tietê, Ibaté and São Carlos (State of São Paulo) were acquired. Three data sets were generated: pure gasolines, polar phase and aqueous phase. The partition procedure aids in the identification of possible adulterants, since ethanol, present in Brazilian gasoline, presents strong peaks in the IR spectra. By IR spectra analysis methanol was found in the polar fraction of a sample, a novelty in the "gasoline tampering procedure". The same partition procedure was applied on samples spiked with methanol (MTH), ethanol (ETH), turpentine spirit (TUS) and toluene (TOL) from 0 to 25% yielding twelve datasets. It was found that identification of adulterated gasolines can be performed using spectra without partition but the quality of PCA separation is increased when pure, nonpolar and polar fraction spectra are concatenated, due the increase of chemical information. PLS models presented standard errors of cross-validation of 1.36%, 4.10%, 1.92% and 2.34% for MTH, ETH, TUS and TOL, respectively. The tampered sample presented 8.93% of methanol, using the developed model.
Journal of AOAC INTERNATIONAL, 2019
In this work, transform-infrared spectroscopy (FTIR) was associated with chemometric tools, especially principal component analysis (PCA) and partial least squares regression (PLSR), to discriminate and quantify gasoline adulteration with diesel. The method is composed of a total of 100 mixtures were prepared, and then FTIR fingerprints were recorded for all samples. PCA was used to verify that mixtures can be distinguished from pure products and to check that there are no outliers. As a result of using just PC1 and PC2, more than 98% of the general variability was explained. The PLSR model based on infrared spectra has shown its capabilities to be suitable for predicting gasoline adulteration in the concentration range of 0 to 98% (w/w), with a high significant coefficient of determination (R2 = 99.25%) and an acceptable calibration and prediction errors (root mean squared error of calibration = 0.63 and root mean square of external validation and/or prediction = 0.69).
Chromatographia, 2009
ASTM D6729 gas chromatographic fingerprinting coupled to pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality. SIMCA, was performed on gas chromatographic fingerprints to classify the quality of representative commercial gasoline samples selected by hierarchical cluster analysis and collected over a 5 month period from gas stations in São Paulo State, Brazil. Following an optimized ASTM D6729 gas chromatographic-SIMCA algorithm, it was possible to correctly classify the majority of commercial gasoline samples. The method could be employed for rapid monitoring to discourage adulteration.
The criminal act of fuel (gasoline) adulteration still remains a global worry due to its environmental, health and economic effect. Current methods for the detection of fuel adulteration have not been effective in most developing countries due to the associated cost of implementation. Therefore, there is the need for a fast, reliable and cheaper approach for screening of adulterants in fuel. This study combined FTIR analyses with Chemometric (multivariate) techniques for qualitative and quantitative determination of four possible adulterants: kerosene, diesel, naphtha and premix in gasoline. Synthetic admixtures prepared by mixing the gasoline with varying proportions of the adulterants were obtained and used for the model calibration. Soft Independent Modeling Class Analogy (SIMCA) classification and Partial Least Square (PLS) regression methods were the Chemometric techniques employed. The SIMCA classification model developed predicted the type of adulterant present at an error rate of 6.25% for Kerosene and naphtha, and 12.5% for premix. However, no prediction error was recorded for classifying samples contaminated with diesel. The PLS regression model was able to predict the concentrations of adulterant with prediction errors lower than 5% for all adulterants ABOUT THE AUTHORS