Classification of Fuel Blends Using Exploratory Analysis with

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Classification of Fuel Blends using Exploratory Analysis by combined data from Infrared Spectroscopy and Stable Isotope Analysis Victor Hugo Jacks Mendes dos Santos, João Marcelo Medina Ketzer, and Luiz Frederico Rodrigues Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01937 • Publication Date (Web): 25 Nov 2016 Downloaded from http://pubs.acs.org on November 26, 2016

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Classification of Fuel Blends Using Exploratory Analysis with Combined Data from Infrared Spectroscopy and Stable Isotope Analysis Victor Hugo J. M. dos Santos,a João M. M. Ketzer,a Luiz F. Rodriguesa*

a

Institute of Petroleum and Natural Resources, Pontifical Catholic University of Rio Grande do

Sul, Av. Ipiranga, 6681 – Building 96J, 90619-900, Porto Alegre, Brazil.

AUTHOR INFORMATION Corresponding Author * Tel: +55 51 3320-3689. E-mail: [email protected]

ACS Paragon Plus Environment

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Abstract In this paper, chemometric tools were applied for exploratory analysis and classification of fuel blends using the combined information of Fourier Transform Infrared Spectroscopy (FTIR) and stable isotope analysis through Isotope Ratio Mass Spectrometry (IRMS). Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were applied for exploratory analysis, while Support Vector Machine (SVM) was used to classify the biodiesel/diesel blends. All of the chemometric models used present better results from the combination of spectral information with isotopic data for biodiesel contents of over 10% in the mixture, with the best results being obtained from the SVM classification. Therefore, the development presented in this paper could become an important technique to improve the discrimination of the feedstock used in biodiesel production and as a resource for quality control in industries.

KEYWORDS: FTIR; chemometrics; IRMS; biodiesel; stable isotope


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1. Introduction Reducing the consumption of fossil energy sources is one of the principal goals on the world agenda, and biodiesel is one of the hot trends in research on renewable energy.1,2 Chemometric tools are especially good for handling large datasets and can be applied for quality control and exploratory analysis. Several authors have applied chemometric methods to the assessment of biofuel, fossil fuel, and their fuel blends. Milanez and co-authors studied the applicability of classification transfer methods for adulteration control of hydrated ethyl alcohol fuel (HEAF) samples. They successfully developed classification models by applying the multivariate tools linear discriminant analysis coupled to the successive projections algorithm (SPA-LDA) and partial least squares discriminant analysis (PLS-DA).3 With regard to biodiesel and biodiesel/diesel fuel blends, Eide and Zahlsen applied electrospray mass spectrometry (ESI-MS) for identification of methyl ester feedstock and its content quantification in blends with petrodiesel.4 As a result, the method based on ESI-MS was able to identify the different feedstocks, discriminate manufacturers, and quantify the blend composition. In another approach, Costa and co-authors studied the possibility of classifying biodiesel/diesel blends according to the vegetable oil feedstock through an inexpensive method by using digital images and chemometric methods.5 The (B5) fuel blends were prepared from four types of feedstocks (cottonseed, sunflower, corn, and soybean) and the color histograms of the pictures were used as data for the chemometric tools.


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Linked to the production and quality control of biodiesel, chemometric techniques have been used, combined with infrared spectroscopy, for data processing and determination of the quality parameters of biodiesel and diesel/biodiesel blends.6–11 Alves and Poppi used Near Infrared spectroscopy (NIR) and chemometric tools to perform the quantification of biodiesel and hydroprocessed esters and fatty acids (HEFA) contents in diesel fuel blends.12 The model was able to quantify the percentage of HEFA in the fuel with tolerable measurement error values by using the PLS and ν-Support Vector Regression (ν-SVR) model. Until now, only a limited number of studies on discriminant analysis of biodiesel have been conducted. Mueller and coworkers performed an analysis of six different feedstocks used for methyl ester production. They applied the iPCA to determine the optimal spectral regions for the sample classification, following which they found that 100% of samples were correctly classified by soft independent modeling of class analogies (SIMCA).13 In a similar approach, Mazivila and collaborators applied the multivariate technique PLSDA to FTIR data in order to discriminate different B5 biodiesel/diesel blends.14 The samples of ethyl and methyl biodiesels, produced from waste frying oil, jatropha, and soybean, were classified with 100% efficiency. In another approach, Galhardo and Rocha applied the chemometric tool Kohonen neural networks to the infrared spectra of biodiesel/diesel samples. They used a Self-Organizing Map (SOM) and PCA to analyze the infrared data in order to identify the blend samples according to their respective types of feedstocks.15


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Moreover, Rocha and coworkers performed the classification and quantification of binary mixtures of peanut, canola, and corn biodiesel/diesel through the use of NIR spectra. The best results were obtained through the application of the Support Vector Machine (SVM) algorithms, with 100% of the samples being correctly classified.16 As an alternative in quality control, stable isotope analysis through Isotope Ratio Mass Spectrometry (IRMS) has been used for the characterization of fuels because it provides a fingerprint based on the specific stable isotopic signature of the material. Several papers have focused on isotopic analysis of fuel and biofuel samples such as ethanol,17 gasoline,18,19 methane,20–22 crude oil,23 and diesel,24 with IRMS being applied for certification of the fuel and biofuel sources, development of new analytical procedures, and assessment of environmental remediation, among other research topics. Techniques based on IRMS are applied to vegetable oils, and two main approaches can be identified: a) quality control of the purity of vegetable oils,25 and b) authentication of oil samples using a combination of stable isotope analyzes of carbon (13C/12C), oxygen (18O/16O), and deuterium (D/H).26 A combination of information from different sources could enhance the potential of chemometric tools. In this way, Alonso-Salces and coworkers applied a combination of the proton nuclear magnetic resonance technique and the stable isotope ratio (δ13C and δ2H) by PCA, LDA, and PLS-DA for the authentication of virgin olive oil. With this approach, they were able to develop a model with a recognition capacity of above 80%.27 Another approach, presented by Harvey and coworkers, applied the Compound Specific Isotope Analysis (CSIA) methodology for the characterization of δ2H and δ13C of compounds


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from diesel samples.24 As a result, samples from different sources could be clearly differentiated by the application of PCA to the isotopic data. In our previous work, fuel blends were classified through the chemometric techniques PLS-DA, SIMCA, HCA, and SVM using only FTIR, and some limitations in the discriminant analysis of the binary mixtures were observed.28 All models discussed have the potential to classify samples with biodiesel contents of up to 10%; however, the chemometric techniques lost their efficiency and misclassified some sample mixtures when the biodiesel content exceeded 10% (v/v). To enhance the potential of the classification methods proposed by Santos and coworkers,28 the present study investigated the combination of Mid Infrared Spectroscopy (MIR) data (FTIR) and stable carbon isotope data to perform an exploratory analysis and classification of fuel through the chemometric tools PCA, HCA, and SVM. This combination was previously proven to be advantageous for enhancing the potential of the discriminant analysis tool,29,30 and this work aims to present a new and robust application of chemometric tools for the classification of fuel blends, especially those with higher biodiesel contents (10−100% v/v).

2. Brief description of the chemometric tools Chemometrics is the science of applying mathematical or statistical methods in order to transcribe and simplify the observation of the established relationships of the samples and their variables, which will help to understand the chemical system or process for quantitative and/or qualitative analysis.32–34


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The chemometric tools PCA, HCA, and SVM, applied in this paper, are briefly presented in this section. For more information about the chemometric methods and their terminology, the texts written by Hibbert31 (IUPAC Recommendations), Lavine and Workman,32 and Kumar and co-authors33 are recommended. 2.1. Principal Component Analysis (PCA) Principal Component Analysis (PCA) is a chemometric tool that is applied for exploratory analysis that aims to perform graphical projection of the data in order to assist in the understanding of their structure, with the interpretation being mainly conducted through the analysis of loading and scores of the PCA.16 2.1.1. Scores. The scores are an orthogonal projection of the sample onto a principal component (PC) and reveal the relationships among the objects.31,34,35 The properties of the samples are transcribed and presented as a two or three-dimensional graph with a PC on each axis and a unique score coordinate for each input. Samples with a high degree of similarity in a set of variables will be closely positioned on the respective PC that presents high loadings for those respective inputs. 2.1.2. Loading. The loadings translate the hidden information of the input data into mathematical terms of correlation and contribution, with one loading per variable on each PC.31,34,35 The resulting values reflect the influence of each input on a given PC, with the sample coordinates and the magnitude of the loading being very closely related. This means that samples with a positive loading on a given PC have a scattering of the score to the same dimensional space at the same time.


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2.2. Hierarchical Clustering Analysis (HCA) Hierarchical Clustering Analysis (HCA) aims to perform similarity recognition by using the appropriate measurement distance and a linkage criterion that specifies the distance between the sets as a function of their dissimilarity.13 The result of HCA is a dendrogram in which the samples, represented by the vertical bars, are closely linked and successively positioned, while the horizontal position of the bar (relative distance) indicates the order of magnitude of the relations between the samples.16,31 The higher the value of the relative distance at which a connection is established, the smaller the similarity between objects. 2.3. Support Vector Machine (SVM) SVM is a pattern recognition technique that is replacing Artificial Neural Networks as a classification tool. This method is applicable to nonlinear data, by using kernel based functions, and aims to find the hyperplane that leads to the greatest spacing between the classes.36,37 3. Experimental Section For the experimental procedures, only binary blends of diesel S10/soybean biodiesel and diesel S10/corn biodiesel were used for the chemometric analyses. Stable carbon isotope analysis was applied to diesel S10, soybean, and corn biodiesel in order to evaluate their isotopic signatures before carrying out the process of combining the information with the infrared data. 3.1. Sample Preparation. The samples used in this paper are the same as those used in our previous work,28 and the procedures used for the biodiesel synthesis and for the preparation of fuel blends are also


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described there. A total of 90 samples, listed in Table 1, were used for chemometric analysis, with 24 samples being used for calibration of each biodiesel class (corn and soybean)38 and 21 for the validation.39 To enhance the discussion of the multivariate techniques, the blends are grouped in three sets based on the volumetric content of biodiesel: SET A (0–10%), SET B (10–30%), and SETC (30–100%).

Table 1. Points used for calibration and validation of the chemometric model

3.2. Acquisition of spectral data The infrared spectra were obtained with a Spectrum One spectrometer (Perkin-Elmer) with the ZnSe crystal of the Horizontal Attenuated Total Reflectance accessory. The spectral range was from 4000 to 650 cm-1, the resolution was 4 cm-1, and 16 scans and triplicate analysis were performed for each sample. Further information about the infrared procedure can be found in our previous work28 and all the transformations performed over the FTIR spectra were carried out using the chemometric software. 3.3. Isotopic Analysis IRMS was used to analyze the ratio of stable isotopes of carbon (13C/12C). The equipment used for the analysis was a trace GC gas chromatograph, GC IsoLink, coupled to IRMS Delta V Plus (Thermo Fisher Scientific Company). The gas chromatograph contains a fused silica


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column of Supelco Carboxen PLOT 1006 (30 m × 0.32 mm) and operates with a heating ramp from 70 to 150 °C. Before each chromatographic run, three pulses of standardized CO2 (δ13C = –32.848; δ18O = –23.164) were injected into the system to perform the isotopic ratio calculation. The isotope ratio calculation is shown in Equations 1 and 2.

Isotope Ratio (R) =

δ#$%&'( =

ℎ ℎ (1) ℎ ℎ

)#$%&'( −)#+$,-$.∗ 1000 (2) )#+$,-$.-

For the analysis of samples, approximately 200 µL of sample was put on ceramic platforms containing a thin layer of adsorbent (COM-Aid). After weighing, the samples were inserted into a Leco SC-632 elemental analyzer. The gas from sample combustion was collected in special quartz vials (500 mL), previously purged with helium and adapted to collect the product of combustion. The gas collection was performed for 50 seconds, by allowing a continuous flow of gas through the quartz vial. At the end of this period, the vial valve was closed and the gas was sampled through a rubber septum. For each sample, a duplicate of the elemental analyzer combustion was conducted and each ampule was also analyzed in duplicate by IRMS. After gas sampling, 50 µL was injected into the gas chromatograph coupled with the isotope ratio mass spectrometer (GC/IRMS).


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The δ13C results obtained for the samples are expressed in per mille units (‰) and are values relative to the Vienna Pee Dee Belemnite (VPDB) international standard for CO2. 3.4. Chemometric analysis All data were processed by the chemometric tools using the software Unscrambler X 10.4® (CAMO Company). 3.4.1. Infrared spectra processing. The application of spectral windows to the chemometric analysis of infrared spectra (3699–2974 cm‐1 and 1841–925 cm‐1) was first proposed by Oliveira and coworkers for the determination of the methylic biodiesel contents in fuel blends by FTIR.8 This spectral range comprises the most important infrared regions for performing the DA proposed in this work as confirmed by the loadings of the PCA and PLS-DA model developed previously.28 For the chemometric analysis, the spectra were transformed using the first order derivative, smoothing (11 point window), and a polynomial order of 2 and applying the Savitzky-Golay algorithm. For the PCA, the data were mean centered and the NIPALS algorithm was used. 3.4.2. Combination of the FTIR and IRMS data. All the chemometric procedures applied to the combined data were performed as described below. PCA was applied for the dimension reduction of FTIR spectra, and after that, only two PCs (PC1-MIR and PC2-MIR) were applied for the combination of infrared (MIR) and IRMS data (δ13C). For combinations, the data were mean centered and transformed through scaling (block weighting), with each block being divided by the respective standard deviation.


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4. Results and Discussion This section presents, separately, the results of the isotopic analysis of the fuel blend components (soybean biodiesel, corn biodiesel, and diesel S10) from the multivariate analyses of the fuel blend samples. Detailed discussion of the chemometric application to biodiesel by mid-infrared spectroscopy analysis can found in our previous work.28 4.1. Isotopic analysis of biodiesel and fuel blends Isotopic analysis was first applied to the diesel S10 and to the biodiesel from soybean and corn (Figure 1). The result for diesel S10 showed a typical signature from fossil sources (δ13C = –23.730‰), while for corn and soybean biodiesel, the results showed a typical signature for the C4 photosynthetic cycle (δ13C = –15.483‰) and C3 photosynthetic cycle (δ13C = –29.357‰), respectively.

Figure 1. Isotopic ratio of diesel S10, soybean biodiesel, and corn biodiesel

Subsequently, the isotopic analysis was applied to the corn biodiesel/diesel and soybean biodiesel/diesel fuel blends. The correlation between the isotopic results and the compositions of the biofuel blends is plotted in Figure 2. When the biodiesel content of the pure diesel S10 increased, there were changes in the isotope values. i) On adding corn biodiesel, an enrichment in the 13C content was observed and the values of δ13C ranged from –23.730 to –15.483‰ (Figure 2a). ii) On adding soybean biodiesel, a depletion in the

13

C content was observed, and the values of δ13C ranged from –


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23.730 to –29.357‰ (Figure 2b). The results of the isotopic analysis of the calibration fuel blends are shown in Table 2

Table 2. Results of isotopic analysis of calibration fuel blends

In the same way, an independent set of samples used for the validation of the model was put through the same analytical procedure and, as presented in Table 3, the behaviors of the calibration and validation set were nearly the same, which demonstrated the reproducibility of the applied methodology.

Table 3. Results of isotopic analysis of validation fuel blends

Agreement of the results obtained for the independent calibration and validation sets was obtained (Figure 2).

Figure 2. Linear regression of the isotopic ratio versus percentage of biodiesel in the fuel blend: a) corn blends and b) soybean blends


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The analysis of stable isotopes by IRMS is a very sensitive technique and was able to find significant isotopic differences between the fuel blends prepared with biodiesel from different plant sources and with different biofuel contents.

4.2. Multivariate Analysis of the Combined Data from MIR+IRMS In our previous work, fuel blends were classified by chemometric techniques using only FTIR and all the models discussed were capable of discriminating the blends with biodiesel contents of up to 10%.28 When a higher content of ester was found in the mixture, the infrared spectra lost their capability to find the difference between the blends made from distinct feedstocks (corn biodiesel and soybean biodiesel). Therefore, a combination of different analytical techniques was proposed in order to improve the discriminant potential of the chemometric classification model. Due to the difference between the isotope ratio signatures of the soybean biodiesel, corn biodiesel, and diesel S10, combining the isotopic data with the infrared spectra was proposed to improve the model in order to classify the fuel blend samples with biodiesel contents exceeding 10%.

4.2.1. Development of the PCA of the fuel blends The exploratory analysis was applied simultaneously for fuel blends from soybean and corn biodiesel. First, the entire set of samples (SETs A, B, and C) was processed (Figure 3) and subsequently a model was built for each set (Figure 5). PCA was also applied for dimensional


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reduction of the large data set from infrared spectra into a small number of variables (PCs). Posteriorly, the PCA scores were used for the analysis of PCA, HCA, and SVM. Figure 3 shows the score graph resulting from the PCA of fuel blends obtained from infrared spectra (MIR) and the combined data (MIR+IRMS). In Figure 3, the score graphs of PCA of corn and soybean fuel blends can be observed, where the graphs of PC1 versus PC2 are presented for all of the respective groups analyzed. The PCA graphs are presented side by side to compare the results obtained for the infrared spectrum (MIR), SETs A, B, and C (Figs. 3a, 3c, and 3e, respectively), with their corresponding charts obtained from the combined data of FTIR and stable isotope analysis (Figs. 3b, 3d, and 3f). The term “lower blends” refers to mixtures with low percentages of biodiesel in their compositions, while “higher blends” refers to mixtures with high percentages of biodiesel in their compositions. . Enriched or Higher (less negative) delta values (δ13C) indicate increases of 13C in the sample or enrichment of the sample with

13

C. Depleted or Lower (or more negative) delta

values (δ13C) indicate decreases of 13C in the sample or depletion of the sample with 13C. Figure 3. PCA score of biodiesel fuel blends: a) PCA of corn blends (MIR), b) PCA of corn blends (MIR+IRMS), c) PCA of soybean blends (MIR), d) PCA of soybean blends (MIR+IRMS), e) PCA of corn versus soybean (MIR), and f) PCA of corn versus soybean (MIR+IRMS)


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The fuel blend samples (Figs. 3a and 3c) are arranged along PC1 according to their ester content, with the higher blends being positioned on the positive side of the axis. For both kinds of blends, there is an initial displacement along PC2 to positive values for the samples of SET A and B. From SET B to SET C, there is an inversion of this displacement, which means that other variables of the infrared spectra have become more significant for the PCA. For PCA analysis of both types of blends, by combining the information of MIR and IRMS (Fig. 3b and 3d), there is a similar tendency that has been observed previously but, in that case, showed greater dispersion between samples. When plotted together, the scores of the corn and soybean fuel blends (Figs. 3e and 3f) clearly showed the effect of the stable isotope analysis in modifying the chart obtained (MIR+IRMS), displacing the blends of soybean and corn onto opposite side in the dimensional space. From Figure 3f, it can be seen that the blends with higher contents of biodiesel in the composition are shifted to the positive direction of the Y axis, mainly due to the influence of the score of PC1-MIR, while they are displaced along the X axis mainly due to the influence of the δ13C value, and this behavior is confirmed by the loading of the PCA (Figure 4). The blends that are most depleted in

13

C, corresponding to soybean samples, are displaced along the X axis to

negative values while the blends most enriched in

13

C, corresponding to the corn samples, are

observed to be shifted to the positive values of the X axis. In Figure 4, we can observe the contribution of each input variable (PC1-MIR, PC2-MIR, and δ13C) to the respective PC1 and PC2 resulting from the PCA analysis, combining the information of MIR+IRMS. PC1-MIR contributes strongly to the PC2 value, PC2-MIR has little


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influence on the explained variance of the samples, and finally

13

C has a great influence on

obtaining PC1.

Figure 4. Loading of combined data from MIR+IRMS for PCA of corn and soybean blends.

In order to enhance the discussion about the effect of the isotopic data in the data structure, a PCA was performed for each set. The obtained results could help to optimize the range of biodiesel content wherein the data combination is advantageous. Figure 5 shows the scores of the PCA applied to the sample analyzed only by MIR (Figs. 5a, 5c, and 5e) and analyzed by the combination of MIR+IRMS (Fig. 5b, 5d, and 5f). The same criteria as in Figure 3 were used for the biodiesel percentage and carbon isotope content.

Figure 5. PCA analysis of corn versus soybean fuel blends: a) SET A (MIR), b) SET A (MIR+IRMS), c) SET B (MIR), d) SET B (MIR+IRMS), e) SET C (MIR) and f) SET C (MIR+IRMS)

The biodiesel blends of SET A (0–10% biodiesel content) could be differentiated by the application of PCA only to MIR data (Fig. 5a). The higher blends could be found on the positive side along PC1, while the sample classes are split along PC2, with the corn blends positioned on the positive values.


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For the biodiesel blends of SET B (10–30% biodiesel content) and SET C (30–100% biodiesel content), shown in Figures 5c and 5e respectively, the PCA was not capable of efficiently separating the sample classes by using only the MIR data. When the combined information of FTIR and isotopic analysis was used, it was observed that the samples were separated according to the types of raw materials along the X axis, with the corn blends enriched in δ13C being displaced toward the positive side. For SET A (Fig. 5b), there was a loss of resolution in the separation of the fuel blends compared to the PCA based on the infrared data (Fig. 5a). Interference in the stable isotope analysis could influence the results, and new studies will be necessary to improve the model. Meanwhile for SETs B and C (Figs. 5d and 5f), there was a clear separation between the soybean and corn groups, with the mixture with an elevated percentage of methyl ester being displaced to the positive values of the Y axis. Through PCA analysis, it is possible to better understand the effect of the combination of the stable isotope data for the classification of fuel blends. From the score graphic, it can be observed that as the percentage of biodiesel in the mixture increases, the samples of soybeans and corn become increasingly distant in dimensional space, leading to the conclusion that the isotopic analysis is very effective in promoting the separation of groups of samples for biodiesel percentages above 10%. 4.2.2. HCA of the Biodiesel/Diesel Blends. A specific HCA model was built using only the MIR data and a second model was built for the combined data from MIR+IRMS in order to visualize the data structure. The results of HCA are presented in Figure 6 and the dendrograms


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are obtained by using the Euclidean distance algorithm and the hierarchical average-linkage clustering method. Figure 6. HCA of corn versus soybean fuel blends: a) SET A (MIR), b) SET A (MIR+IRMS), c) SET B (MIR), d) SET B (MIR+IRMS), e) SET C (MIR) and f) SET C (MIR+IRMS) For the parameters used for HCA, it was observed that the models based on MIR were not able to separate the samples into two clusters of soybeans and corn blends, respectively. For SET A (Fig. 6a), based on MIR data, it can be observed that the samples are grouped by percentage of biodiesel in the blend and split between the respective soybean and corn blends. The same behavior can be observed when the combined data of MIR+IRMS are applied to the HCA analysis of SET A (Fig. 6b). For the SETs B and C (Figs. 6c and 6e), no grouping tendency can be recognized and the MIR data are insufficient for classification purposes. However, the stable isotope data increase the capability of the HCA model to clearly divide the samples into two clusters of blends of soybean and corn biodiesel (Figs. 6d and 6f). Inside each cluster, the samples tend to be grouped depending on the concentration of biodiesel in the blend. 4.2.3. Support Vector Machine (SVM). A specific classification model based on SVM was built for classification using only the MIR data and a second one was built for the combined data from MIR+IRMS; there was no need to create a specific model for each concentration range.


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The optimization of the C-SVM are performed through several grid searches with different values of C and γ (ranging between 10-2 and 102) by using the Radial Basis Function kernel.40 Table 4 presents the parameters used for the C-SVM model.

Table 4. Parameters obtained from cross-validation by C-SVM

The confusion matrices of the SVM classification is presented in Table 5. The classes are organized horizontally for the samples, while the classes to which they were attributed are arranged vertically.

Table 5. Confusion matrix of the SVM classification

From the results obtained, it is clear that the combination of isotopic data is effective in improving the classification capability of the SVM model, especially for higher concentrations of biodiesel blends with more than 10% biodiesel in the fuel mixture. The classification capacity for blends of SET A remained approximately the same for soybean blends and presented only a slight decrease for corn blends with biodiesel contents of up to 10%. From this, it is concluded that for the discriminant analysis of SET A (blends with biodiesel contents of 0–10%) there is no need to perform the combination of MIR and IRMS data. For the subsequent concentration ranges of SETs B and C (grouping the samples with biodiesel contents of 10 to 100% in the mixture), an improvement in the classification ability of


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the SVM model was obtained. The results shown that, from the combination of isotopic data, the SVM model could classify all of the test samples of corn blends from SETs B and C into their respective classes. This improvement may be associated with the change in the isotope ratio in corn and soybean blends (Tables 2 and 3 and Figure 2). When the percentage of these blends increased in the mixture, the delta variation for the isotope ratio results also increased (δ13C B(x) corn blend – δ13C B(x) soybean blend).

This behavior is illustrated in Figure 3.

Table 6 shows the improvement of the model based on the sensitivity and specificity, discussed by Ellison et al.41 and López et al.42, and calculated according to Equations 3 and 4.41

Sens =

TP (3) TP + FN

Spec =

TN (4) TN + FP

The terms TN, TP, FP, and FN denote the number of observations that are true negatives, true positives, false positives, and false negatives, respectively. The results are divided into the classification obtained by the infrared spectroscopy data and that obtained from the combined data from MIR+IRMS. In order to better visualize the data structure, the results specify the classification of the samples from Sets A, B, and C. Table 6. Sensitivity and specificity of the model based on SVM

The combination of stable isotope data and infrared spectra increases the potential of the model, mainly for blends of SETs B and C, which present high percentages of methyl ester in


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their compositions. The C-SVM classified all fuel blends samples in a single model using MIR and IRMS data with good global efficiency (95.2% sensitivity and specificity for both classes). 5. CONCLUSIONS This work has discussed the potential of the chemometric tools PCA, HCA, and SVM to perform an exploratory analysis and the classification of fuel blends. For this aim, the combined information from infrared spectroscopy (FTIR) and stable isotope analysis by Isotope Ratio Mass Spectrometry (IRMS) was used. The multivariate models used (PCA, HCA, and SVM) gave better results through the combination of spectral information with the isotopic ratio data for blends with biodiesel contents of over 10%, which solves the difficulties found in our previous work, where blends with biodiesel contents exceeding 10% were more difficult to classify. The model showed an appreciable recognition capability with a global efficiency of 95.2% for both classes and indicates that SVM is a good chemometric tool for fuel blend classification; however its their application to other biodiesel feedstocks and large data sets is still required. To our knowledge, this work by our group offers the first report of the application of the carbon isotope ratio for biodiesel characterization and it can be concluded that it is possible to develop robust multivariable models to perform classification of fuel blends. The combination of infrared spectra with the isotopic data proved to be perfectly possible and advantageous and the IRMS could be applied as a tool to perform quality assessment of fuel blends. ACKNOWLEDGMENTS


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The authors thank to the Institute of Petroleum and Natural Resources - IPR of the PUCRS for the financial support and to the REFAP for diesel samples donating. ABBREVIATIONS PC, Principal Component; PCA, Principal Component Analysis; HCA, Hierarchical Clustering Analysis; SVM, Support Vector Machine; FTIR, Fourier Transform Infrared Spectroscopy; MIR, Mid Infrared Spectroscopy; IRMS, Isotope Ratio Mass Spectrometry.

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Table 1. Points used for calibration and validation of the chemometric model a

Calibration Set ID % Biodiesel B0 0 B0.25 0.25 B0.5 0.5 B1 1 B2.5 2.5 B5 5 B7.5 7.5 B10 10 B12.5 12.5 B15 15 B17 17 B20 20 B25 25 B30 30 B50 50 B70 70 B75 75 B80 80 B90 90 B95 95 B97 97 B99 99 B99.8 99.8 B100 100 a

b

Validation Set ID % Biodiesel B0.75 0.75 B1.5 1.5 B3 3 B4.5 4.5 B6 6 B7 7 B8 8 B9 9 B11 11 B13 13 B16 16 B19 19 B22.5 22.5 B27.5 27.5 B40 40 B55 55 B67.5 67.5 B77.5 77.5 B85 85 B92 92 B98 98

Recommendation of ASTM D7371;41 b 21 validation blends, minimum necessary as recommended by the ASTM

E2056.42


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Table 2. Results of isotopic analysis of calibration fuel blends

Blend B0 B0.25 B0.5 B1 B2.5 B5 B7.5 B10 B12.5 B15 B17 B20 B25 B30 B50 B70 B80 B90 B95 B97 B99 B99.8 B100

Corn Blends Soybean Blends Average Standard Average Standard Blend (‰) dev. (‰) (‰) dev. (‰) -23.730 0.143 B0 -23.730 0.143 -24.178 0.260 B0.25 -23.561 0.175 -23.345 0.178 B0.5 -23.887 0.475 -24.018 0.644 B1 -23.960 0.157 -22.966 0.390 B2.5 -24.177 0.091 -24.059 0.513 B5 -24.158 0.349 -23.576 0.736 B7.5 -24.666 0.120 -22.786 0.620 B10 -23.944 0.306 -23.080 0.322 B12.5 -24.480 0.303 -21.114 0.008 B15 -24.635 0.142 0.129 -23.006 0.861 B17 -24.940 -21.649 0.276 B20 -25.272 0.385 -20.858 0.248 B25 -25.795 0.112 -20.445 0.411 B30 -25.508 0.359 -20.175 0.084 B50 -26.116 0.089 -17.846 0.095 B70 -27.397 0.110 -17.681 0.037 B75 -27.552 0.036 -16.764 0.232 B80 -27.578 0.128 -16.204 0.240 B90 -28.798 0.064 -16.676 0.018 B95 -29.217 0.018 -15.781 0.203 B97 -29.173 0.142 -16.526 0.067 B99 -29.248 0.059 -15.483 0.282 B99.8 -29.481 0.062 B100 -29.357 0.208


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Table 3. Results of isotopic analysis of validation fuel blends

Blend B0.75 B1.5 B3 B4.5 B6 B7 B8 B9 B11 B13 B16 B19 B22.5 B27.5 B40 B55 B67.5 B77.5 B85 B92 B98

Corn Blends Average Standard (‰) dev. (‰) -24.057 0.308 -23.448 0.592 -23.451 0.676 -22.879 1.007 -23.338 0.316 -23.536 0.168 -23.626 0.733 -23.008 0.480 -22.507 0.169 -23.519 0.543 -22.814 0.198 -22.501 0.086 -21.186 0.553 -20.180 0.411 -21.521 0.157 -19.071 0.555 -18.161 0.119 -17.535 0.123 -16.505 0.326 -16.994 0.036 -16.326 0.074

Soybean Blends Average Standard Blend (‰) dev. (‰) B0.75 -24.448 0.072 B1.5 -24.463 0.047 B3 -23.930 0.039 B4.5 -23.601 0.019 B6 -23.659 0.035 B7 -23.827 0.127 B8 -23.906 0.134 B9 -24.409 0.099 B11 -24.264 0.331 B13 -24.565 0.155 B16 -24.658 0.215 B19 -24.253 0.049 B22.5 -25.201 0.081 B27.5 -25.174 0.024 B40 -26.161 0.099 B55 -26.686 0.353 B67.5 -27. 828 0.153 B77.5 -28.126 0.089 B85 -28.205 0.350 B92 -29.803 0.131 B98 -28.809 0.260


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Table 4. Parameters obtained from cross-validation by C-SVM C-SVM Parameters

MIR

MIR + IRMS

Classes 2 2 Training Accuracy 92.37% 100% Validation Accuracy 89.31% 98.4% SVM Type C-SVM C-SVM C (capacity factor) 59.95 59.95 γ (gamma) 4.6116 2.783 Kernel type Radial basis function Radial basis function Transformation C C+S C- Mean Centered; S- Scaling (Block weighting - Standard Deviation)


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Table 5. Confusion matrix of the SVM classification MIR Samples Soybean Corn

Class Soybean Corn Soybean Corn

SET A 23 1 0 24

SET B 14 4 6 12

SET C 17 3 5 16

Total 54 8 11 52

SET B 15 3 0 18

SET C 20 0 0 21

Total 59 3 3 60

MIR + IRMS Samples Soybean Corn

Class Soybean Corn Soybean Corn

SET A 24 0 3 21


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Table 6. Sensitivity and specificity of the model based on SVM MIR Samples Soybean Corn

Parameters Sens (%) Spec (%) Sens (%) Spec (%)

SET A 95.8 100 100 95.8

SET B 77.8 66.7 66.7 77.8

SET C 85.0 76.2 76.2 85.0

Total 87.1 82.5 82.5 87.1

Parameters SET A SET B SET C Sens (%) 100 83.3 100 Spec (%) 87.5 100 100 Sens (%) 87.5 100 100 Spec (%) 100 83.3 100 Sens-Sensitivity, Spec-Specificity

Total 95.2 95.2 95.2 95.2

MIR + IRMS Samples Soybean Corn


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Figure 1. Isotopic ratio of diesel S10, soybean biodiesel, and corn biodiesel


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Figure 2. Linear regression of the isotopic ratio versus percentage of biodiesel in the fuel blend: a) corn blends and b) soybean blends


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Figure 3. PCA score of biodiesel fuel blends: a) PCA of corn blends (MIR), b) PCA of corn blends (MIR+IRMS), c) PCA of soybean blends (MIR), d) PCA of soybean blends (MIR+IRMS), e) PCA of corn versus soybean (MIR), and f) PCA of corn versus soybean (MIR+IRMS)


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Figure 4. Loading of combined data from MIR+IRMS for PCA of corn and soybean blends.


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Figure 5. PCA analysis of corn versus soybean fuel blends: a) SET A (MIR), b) SET A (MIR+IRMS), c) SET B (MIR), d) SET B (MIR+IRMS), e) SET C (MIR) and f) SET C (MIR+IRMS)


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Figure 6. HCA of corn versus soybean fuel blends: a) SET A (MIR), b) SET A (MIR+IRMS), c) SET B (MIR), d) SET B (MIR+IRMS), e) SET C (MIR) and f) SET C (MIR+IRMS)


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Figure 1. Isotopic ratio of diesel S10, soybean biodiesel, and corn biodiesel. page 12, line 38 200x104mm (96 x 96 DPI)


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Figure 2. Linear regression of the isotopic ratio versus percentage of biodiesel in the fuel blend: a) corn blends and b) soybean blends. Page 13, line 49-52 135x200mm (96 x 96 DPI)


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Figure 3. PCA score of biodiesel fuel blends: a) PCA of corn blends (MIR), b) PCA of corn blends (MIR+IRMS), c) PCA of soybean blends (MIR), d) PCA of soybean blends (MIR+IRMS), e) PCA of corn versus soybean (MIR), and f) PCA of corn versus soybean (MIR+IRMS). page 15, line 43-50 742x724mm (96 x 96 DPI)


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Figure 4. Loading of combined data from MIR+IRMS for PCA of corn and soybean blends. page 17, line 3-6 360x233mm (96 x 96 DPI)


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Figure 5. PCA analysis of corn versus soybean fuel blends: a) SET A (MIR), b) SET A (MIR+IRMS), c) SET B (MIR), d) SET B (MIR+IRMS), e) SET C (MIR) and f) SET C (MIR+IRMS). page 17, line 37-42 787x726mm (96 x 96 DPI)


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Figure 6. HCA of corn versus soybean fuel blends: a) SET A (MIR), b) SET A (MIR+IRMS), c) SET B (MIR), d) SET B (MIR+IRMS), e) SET C (MIR) and f) SET C (MIR+IRMS). page 19, line 9-14 712x635mm (96 x 96 DPI)


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Classification of Fuel Blends Using Exploratory Analysis with

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