Discriminant analysis of biodiesel fuel blends based on combined data from Fourier Transform Infrared Spectroscopy and stable carbon isotope analysis (original) (raw)
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In this work, a multivariate approach was used to classify diesel/biodiesel fuel blends among 0% to 100% of biodiesel content on fuel mixture through discriminant analysis and cluster analysis associated with Fourier transform infrared spectroscopy (FTIR). The multivariate statistical techniques used in this work were partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), hierarchical clustering analysis (HCA), and support vector machine (SVM). Multivariate analysis was performed on the following oil samples: soybean biodiesel, corn biodiesel, diesel S10, and fuel blends prepared from 0% to 100% (v/v) of biodiesel content. All multivariate statistical techniques were able to discriminate between the oil source and the ester percentage in the mixture. It was possible to develop robust multivariate models associated with the FTIR to allow for simultaneous discrimination of the types of oils used for biodiesel production and their content in fuel blends.
Analytical Letters, 2012
In this paper, three different types of biodiesel, which were synthesized from peanut, corn, and canola oils, were characterized by positive-ion electrospray ionization (ESI) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Different biodiesel/diesel blends containing 2–90% (V/V) of each biodiesel type were prepared and analyzed by near infrared spectroscopy (NIR). In the next step, the chemometric methods of hierarchical clusters analysis (HCA), principal component analysis (PCA), and support vector machines (SVM) were used for exploratory analysis of the different biodiesel samples, and the SVM was able to give the best classification results (correct classification of 50 peanut and 50 corn samples, and only one misclassification out of 49 canola samples). Then, partial least squares (PLS) and multivariate adaptive regression splines (MARS) models were evaluated for biodiesel quantification. Both methods were considered equivalent for quantification purposes based on the values smaller than 5% for the root mean square error of calibration (RMSEC) and root mean square of validation (RMSEP), as well as Pearson correlation coefficients of at least 0.969. The combination of NIR to the chemometric techniques of SVM and PLS/MARS was proven to be appropriate to classify and quantify biodiesel from different origins.
Vibrational Spectroscopy, 2018
There are still few initiatives studying applications for the classification of biodiesel derived from a mixture of raw materials, whether pure (B100) or mixed with diesel. The present study aims to conduct a preliminary assessment on the classification of pure biodiesel blends, and their mixtures with diesel at a proportion of 10%, based on FTIR spectroscopy and multivariate analysis. The work is contextualized around the monthly raw material consumption of Brazilian regions along a year. From this work, it is possible to verify that FTIR analysis, combined with multivariate methods, can be applied to classify both pure biodiesel blends and those mixed with diesel. The PCA showed great potential for recognizing the data patterns, while the HCA are able to discriminate the B100 blends from different Brazilian regions and cluster the samples according to the region biodiesel profile. For the B10 fuel blends, the OSC-PLS-DA achieved 100% sensitivity and specificity and can be applied for the classification procedure of biodiesel/diesel blends.
Journal of the American Oil Chemists' Society, 2015
Multivariate calibration models based on data from mid-infrared spectroscopy of biodiesel/diesel blends were obtained. The blends were prepared from diesel oil and esters of soybean oil, waste cooking oil, and hydrogenated vegetable oil in proportions ranging from 0 to 100 % biodiesel. The results showed that the multivariate regression models with interval partial least squares (iPLS), backward interval partial least squares (biPLS), and synergy interval partial least squares (siPLS) were able to determine the fractions of the infrared spectrum that contain the relevant information for estimating the values of physicochemical properties, flash point, specific gravity, and cetane number, which are used in quality control of the blends. In the best models, the values of determination coefficients were greater than 0.9500, proving their efficiency as an alternative to traditional analytical methods.
Journal of the Brazilian Chemical Society, 2015
This work aimed at employing partial least square discriminant analysis (PLS2-DA), allied to mid-infrared (MIR) spectroscopy as an analytical method for simultaneous classification of biodiesels from different oils (soybean and used frying oil) and routes (methylic and ethylic). The evaluation of the model was verified through values of sensitivity and specificity for each parameter, in the interest class. PLS2-DA model showed 100% correct classification in the discrimination of types of biodiesels. Therefore, the proposed methodology is fast, because it allows simultaneous classification of different types of biodiesels. Consequently, it can be used in quality control of this type of biofuel.
This study aims to identify the biodiesel feedstock (cottonseed, sunflower, corn or soybean oil) in biodiesel/diesel blends using digital images and chemometric methods. For this purpose, colour histograms (extracted from digital images) coupled with supervised pattern recognition techniques: Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA) and the Successive Projections Algorithm for variable selection associated with Linear Discriminant Analysis (SPA-LDA) were used. SPA-LDA coupled with intensity histograms provided better results by selecting 12 variables alone, achieving only one error of classification in the external validation (test) set. Thus, the proposed methodology presents a noteworthy eco-friendly approach for identifying the biodiesel feedstock in biodiesel/diesel blends using a simple, fast, inexpensive and non-destructive analytical tool.
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.
Fuel, 2011
Partial least-squares (PLS), interval partial least squares (iPLS) and synergy partial least squares (siPLS) regressions were used to simultaneous determination of quality parameters of biodiesel/diesel blends. Biodiesel amount, specific gravity, sulfur content and flash point were evaluated using spectroscopic data in the mid-infrared region obtained with a horizontal attenuated total reflectance (HATR) accessory. Eighty-five binary blends were prepared using biodiesel and two types of diesel, in concentrations from 0.2 to 30% (v/v). Fifty-seven samples were used as a calibration set, whereas 28 samples were used as an external validation set. All samples were characterized using the appropriated standard methods. The specific gravity values at 20°C were in the range of 848.2-866.2 kg/m 3 . Flash point values lay between 47.0 and 79.5°C. Sulfur content values varied from 312 to 1351 mg/kg. Raw spectra of the samples were corrected by multiplicative scatter correction (MSC) and were pre-processed using a mean-centered procedure. Algorithms iPLS and siPLS were able to select the most adequate spectral region for each property studied. For all the properties studied, the siPLS algorithm produced better models than the full-spectrum PLS, selecting the most important bands. The quantification of biodiesel was performed using two spectral regions between 650-1909 cm À1 and 2746-3165 cm À1 , and an excellent correlation coefficient of R 2 = 0.9996 was obtained. The specific gravity was determined from the spectral region from 650 to 1070 cm À1 , which yielded a very good correlation coefficient of R 2 = 0.9987. The sulfur content was evaluated from the spectral regions of 1070-1491 cm À1 and 2746-3165 cm À1 . A very good correlation coefficient of R 2 = 0.9995 was obtained, regardless of whether the samples were formulated with metropolitan or countryside diesel. Finally, the flash point was determined from the spectral region between 756 and 968 cm À1 and a very good correlation coefficient of R 2 = 0.9982 was obtained.
Talanta
This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant.