Near-Infrared Spectroscopy Coupled with Multivariate Methods for the Characterization of Ethanol Adulteration in Premium 91 Gasoline (original) (raw)
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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.
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.
Monitoring Biodiesel Fuel Quality by near Infrared Spectroscopy
Journal of Near Infrared Spectroscopy, 2007
Biodiesel is produced mainly by a transesterification reaction which involves the reaction of vegetable oils, animal fats or waste oils with an alcohol (such as methanol) in the presence of a catalyst (such as sodium hydroxide or methoxide). Since the presence of contaminants can cause severe engine problems, the assessment of the biodiesel quality is very important. This work reports the use of near infrared (NIR) spectroscopy to determine the content of water and methanol in industrial and laboratory-scale biodiesel samples. A qualitative analysis of the spectra by principal components analysis was carried out and partial least squares regression was used to develop calibration models between spectral and analytical data. The results indicate that the use of NIR spectroscopy, in combination with multivariate calibration, is a promising technique to assess the biodiesel quality in both laboratory-scale and industrial-scale samples.
Talanta, 2011
This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0 mm (8814-3799 cm −1), 10 mm (11,329-5944 cm −1 and 5531-4490 cm −1) and 20 mm (11,688-5952 cm −1 and 5381-4679 cm −1). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20 mm. Otherwise, PLS-DA shows the best results for classification of 10 mm cell data, which achieved a correct prediction rate of 100% in the test set.
Forensic Science International, 2015
The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed. ß
Journal of Near Infrared Spectroscopy, 1998
This paper describes a near infrared spectroscopic method developed for determination of ethanol and methyl tert-butyl ether (MTBE) as additives in gasoline. The methodology employs data collected from a near infrared spectrophotometer whose monochromator is an Acousto-Optic Tunable Filter (AOTF) operating in the 1500–2400 nm range. Genetic Algorithm variable selection was used in the multiple linear regression (MLR) modelling. Seven wavelengths were selected by the algorithm and the results obtained by MLR revealed that the method produces improved results, when compared with the PLS regression method, as confirmed by the lower RMSEP obtained for ethanol and MTBE determination. Besides the improvement achieved in the analytical results, the variable selection allows a reduction in the time necessary for data acquisition. This fact has special importance when AOTFs are being used as the monochromator element. The AOTF's capability of random access to the selected wavelengths can...
Analytica Chimica Acta, 2008
This work presents a comparative study of calibration transfer among three near infrared spectrometers for determination of naphthenes and RON (Research Octane Number) in gasoline. Seven transfer methods are compared: direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), reverse standardization (RS), piecewise reverse standardization (PRS), slope and bias correction (SBC) and model updating (MU). Two pre-treatment procedures, namely standard normal variate (SNV) and multiplicative scatter correction (MSC), are also investigated. The choice of an appropriate number of transfer samples for each technique, as well as the effect of window size in PDS/PRS and OSC components, are discussed. A broad set of gasoline samples representative of the Northeastern states of Brazil is employed in the investigation. The results show that the use of calibration transfer yields prediction errors comparable to those obtained with complete recalibration of the secondary instrument. Overall, the results point to RS as the best method for the analytical problem under consideration. When storage and/or physical transportation of transfer samples are impractical, MU is more appropriate. The comprehensive investigation carried out in the present work will be of value for practitioners involved in networks of fuel monitoring.
Talanta, 2008
Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.
Fuel, 2008
A set of 160 gasoline samples was collected from commercial stations in five Brazilian states and analyzed by ASTM methods for 13 properties. Principal component analysis (PCA) was employed to investigate the effect of infrared spectral region (near or middle), calibration algorithm (principal component regression, partial least squares or multiple linear regression) and pre-processing procedure (derivative, smoothing and variable selection) in the resulting root-mean-square error of prediction (RMSEP). The PCA score plots revealed that all properties can be satisfactorily predicted by multiple linear regression in the 1600-2500 nm region, with variables selected by a genetic algorithm, using any pre-processing technique.
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).