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Study of the Behavior Changes in Physical-Chemistry Properties of Diesel/Biodiesel (B2) Mixtures with Residual Oil and Its Quantification by Partial Least-Squares Attenuated Total Reflection-Fourier Transformed Infrared Spectroscopy (PLS/ATR-FTIR) Itaˆnia P. Soares, Thais F. Rezende, and Isabel C. P. Fortes* Laborato´rio de Ensaio de Combustı´Veis, Departamento de Quı´mica, Instituto de Cieˆncias Exatas, UniVersidade Federal de Minas Gerais, AVenida Antoˆnio Carlos, 6627, Campus Pampulha, CEP 31270-901, Belo Horizonte, Minas Gerais, Brazil
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ReceiVed April 14, 2009. ReVised Manuscript ReceiVed June 5, 2009
The aim of the present work was to verify the influence caused by the contamination or the addition of residual oil in the blends commercialized in Brazil [2% (v/v) of biodiesel in petrodiesel B2] in physicalchemistry parameters usually used to monitor the fuel quality, such as viscosity, flash point, distillation curve, specific gravity, and cetane index. To carry out the experiments, a set of samples simulating a system of fuel adulteration, mixing together biodiesel and residual oil, was prepared in concentrations varying from 0.5 to 25% (w/w). Then, these samples were submitted to the physical-chemistry assays cited before. The specific gravity presented itself as the most sensitive to adulteration. It detected the adulteration in concentration values of residual oil above 10% (w/w). Afterward, a calibration model was built using a multivariate calibration tool, partial least-squares (PLS), applied to attenuated total reflection/Fourier transformed infrared spectroscopy (ATR/FTIR) data to quantify the residual oil present in each sample. The PLS technique was shown to be very efficient in the determination of adulteration of B2 with residual oil from 0.5 to 25% (w/w).
1. Introduction Nowadays, biodiesel is one of the biofuels most studied worldwide as a renewable source of energy. There are many different processes to obtain biodiesel, such as homogeneous catalysis, heterogeneous catalysis, pyrolysis, microemusification, and supercritical fluid process. The most used one is the classical catalysis, using an acid or a base as the catalyzer. Some considerations about the biodiesel synthesis is shown in the literature.1-3 In Brazil, the substitution of part of diesel for biodiesel has an economic and social impact because it reduces the amount of diesel imported and creates a productive agriculture chain, which will create new jobs in the countryside. Therefore, biodiesel is a political goal for the Brazilian government. Since July 2008, diesel in Brazil must have a volume fraction of 3% (v/v) biodiesel, referred to as B3. One of the challenges of this program is to eliminate the fuel adulteration. Adulteration of fuel has been observed in Brazil since the end of the monopoly in fuel distribution and market reform.4,5 Although fuel adulteration has decreased in the past few years, it is still a problem in the country. In the last 8 years, the ANP (Brazilian National * To whom correspondence should be addressed. Telephone/Fax: 05531-34995756. E-mail:
[email protected]. (1) Sharma, Y. C.; Singh, B.; Upadhyay, S. N. Fuel 2008, 87, 2355– 2373. (2) Sharma, Y. C.; Singh, B. Renewable Sustainable Energy ReV. 2009, 13, 1646–1651. (3) Sharma, Y. C.; Singh, B. Fuel 2008, 83, 1740–1742. (4) Pereira, R. C. C.; Skrobot, V. L.; Castro, E. V. R.; Fortes, I. C. P.; Pasa, V. M. D. Energy Fuels 2006, 20, 1097–1102. (5) Skrobot, V. L.; Castro, E. V. R.; Pereira, R. C. C.; Pasa, V. M. D.; Fortes, I. C. P. Energy Fuels 2005, 19, 2350–2356.
Agency for Petroleum, Natural Gas, and Biofuels) has developed efforts to avoid fuel adulterations. In the year 2000, data from ANP showed that 12.5% of gasoline and 7.3% of diesel samples collected from different localities of the country were not in conformity to the ANP specifications for fuel quality.6 International standard methods, such as EN14103 and ASTM 6584, among others are used to perform the quality control of biodiesel. Both methods use gas chromatography as the technique. Results from these analyses can supply information about whether or not the sample is adulterated with raw vegetable oil. However, these methodologies have some disadvantages, such as sample preparation, which is time-consuming, the use of more than one internal standard, a longer analysis time, and an expensive technique employed. One of the analytical techniques widely used to monitor the quality of biodiesel and petrodiesel blends is infrared (IR) spectroscopy. This technique has many advantages. It is nondestructive and very reliable and allows direct and fast determination of several properties, without sample pretreatment.7 IR spectroscopy comprises many different types of equipments, which operate in different regions and have different kinds of detectors and accessories. Fourier transform infrared spectroscopy (FTIR) has become one of the major analytical techniques used because of its quality of screening, quickness, and low cost. It can be thought of as a molecular “fingerprinting” method. Middle-infrared (MIR) spectroscopy, in particular, rapidly provides information on a very large number of analytes, (6) Oliveira, F. C. C.; Branda˜o, C. R. R.; Ramalho, H. F.; Costa, L. A. F.; Suarez, P. A. Z.; Rubim, J. C. A. Anal. Chim. Acta 2007, 587, 194–199. (7) Pimentel, M. F.; Teixeira, L. S. G.; Ribeiro, G. M. S.; Cruz, R. S.; Stragevitch, L.; Filho, J. G. A. P. Microchem. J. 2006, 82, 201–206.
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and the absorption bands are sensitive to the physical and chemical states of individual constituents. FTIR can be coupled with accessories, such as attenuated total reflection (ATR), allowing the analysis of a wide range of solid or liquid components.8 This technique showed to be a very good tool for solving problems of identification and quantification in a large number of fields, such as food chemistry,9 biology,10 environment,11 and fuel.4 In recent years, some work has used multivariate analysis applied to near-infrared (NIR) spectroscopy and FTIR. Pereira et al.4 have determined gasoline adulteration using multivariate analysis from FTIR data. Multivariate analysis is important because the IR spectra of the vegetable oils and their respective esters are very similar, causing overlapped signals.7 Che Man and Setiowat12 used FTIR and a calibration applying partial least-squares (PLS) to determine fatty acid in palmitolein. Knothe13 used NIR spectroscopy and PLS regression to monitor the completion of the transesterification reaction of biodiesel. Oliveira et al.14 used FTIR and NIR spectroscopy to design calibration models for the determination of the methyl ester content in biodiesel blends (methyl ester + diesel). Pimentel et al.7 developed multivariable calibration models based on MIR and NIR spectroscopy to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Ghesti et al.15 used data from Raman spectroscopy to build up PLS calibration models. Wavenumber or wavelength selection to establish a calibration model giving the minimum errors in prediction is decided by choosing a subset of spectral channels with the established calibration model and gives the minimum errors in prediction.16 The benefit gained from wavenumber or wavelength selection is not only the stability of the model to the collinearity in multivariate spectra but also the interpretability of the relationship between the model and the sample composition.17 Some elaborate methods of wavenumber and wavelength selection, such as the genetic algorithm,18 moving window PLS regression,17 and simulated annealing,19 have been developed. However, these methods still tend to be slow and cumbersome compared to the simpler and more intuitive methods as forward and stepwise.20 Soares et al.8 used forward and stepwise variable selection methods (VSMs) to determine biodiesel (B100) adulteration of various oleaginous with raw soybean oil, leading to good results. Models built up using VSMs presented higher concordance between real and predicted values than the models built up without VSMs. (8) Soares, I. P.; Rezende, T. F.; Silva, R. C.; Castro, E. V. R.; Fortes, I. C. P. Energy Fuels 2008, 22, 2079–2083. (9) Sedman, J.; Voort, F. R.; Ismail, A. A. J. Am. Oil Chem. Soc. 2000, 77, 399–403. (10) Nadtochenko, V. A.; Rincon, A. G.; Stanca, S. E.; Kiwi, J. J. Photochem. Photobiol., A 2005, 169, 131–137. (11) Acha, V.; Meurens, M.; Naveau, H.; Agathos, S. N. Biotechnol. Bioenergy 2000, 68, 473–487. (12) Che Man, Y. B.; Setiowaty, G. Food Chem. 1999, 66, 109–114. (13) Knothe, G. J. Am. Oil Chem. Soc. 2001, 78, 1025–1028. (14) Oliveira, J. S.; Montalva˜o, R.; Daher, L.; Suarez, A. Z.; Rubim, J. C. Talanta 2006, 69, 1278–1284. (15) Ghesti, G. F.; Macedo, J. L.; Braga, V. S.; Souza, A. T. C. P.; Parente, V. C. I.; Figueredo, E. S.; Resck, I. S.; Dias, J. A.; Dias, S. C. L. J. Am. Oil Chem. Soc. 2006, 83, 597–601. (16) Du, Y. P.; Liang, Y. Z.; Jiang, J. H.; Berry, R. J.; Ozaki, Y. Anal. Chim. Acta 2004, 501, 183–191. (17) Jiang, J.; Berry, R. J.; Siesler, H. W.; Ozaki, Y. Anal. Chem. 2002, 74, 3555–3565. (18) Bangalore, A. S.; Schaffer, R. E.; Small, G. W.; Arnold, M. A. Anal. Chem. 1996, 68, 4200–4212. (19) Horchner, U.; Kalivas, J. H. Anal. Chim. Acta 1995, 311, 1–13. (20) Spiegelman, C. H.; McShane, M. J.; Goetz, M. J.; Motamedi, M.; Yue, Q. L.; Cote´, G. L. Anal. Chem. 1998, 70, 35–44.
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Other techniques were also already used to study biodiesel blends with diesel using multivariate calibration, such as mass spectrometry with electrospray ionization21,22 and spectrofluorometry,23 but both do not fulfill the qualities of FTIR spectroscopy with attenuated total reflection (ATR) when quality of screening, quickness, and low-cost analysis are put together. Physical-chemistry studies of the properties of diesel mixtures with pure vegetable oils with grades that range up to 5% (w/w) were already addressed by the literature14 and demonstrate that these samples are within specification. Studies about the influence of biodiesel blends in the injection system of motors were already performed by Desantes et al.24 Adulteration problems related to biodiesel and its diesel/ biodiesel mixtures were already addressed by Oliveira et al.6 and Soares et al.8 Both works were focused on developing new methodologies that determine the adulteration of both matrixes with raw vegetable oils. In the first one, studies were performed with low concentrations, less than 5%, which proved not to be detected by the ANP norms. A set of samples was analyzed with FT-NIR spectroscopy and FT-Raman, and these data were used to build models of multivariate calibration with the objective of identifying these adulterations. In the latter, a set of three different sources of biodiesel were mixed with raw soybean oil in various concentrations and analyzed by ATR-FTIR. To determine and quantify the biodiesel adulteration with raw soybean oil, multivariate PLS calibration models based on MIR spectroscopy were developed using classic VSMs: forward and stepwise. This work was developed aiming to study of the behavior of physical-chemistry properties, usually used to monitor the fuel quality, when the diesel/biodiesel mixture (B2) is contaminated or adulterated with residual oil. Residual oil is the oil used to fry food and usually ends up being recycled or discarded. Having said that, it is the kind of product that is broadly available in the market and also has low cost, which gives the product enormous potential and possibility of being used as an adulterant of fuel (diesel/biodiesel) in any proportion. The residual oil, however, has a different chemical composition from the pure vegetable oil and from the diesel/biodiesel mixture. Usually it is a degraded product, and dependent upon the extent of the period in which it was used, it can present polymeric chain formation. Therefore, it is important to verify how the addition of it in the diesel/biodiesel mixture (B2) will affect the physicalchemistry properties, such as viscosity, flash point, distillation curve, specific gravity, and cetane index of this final fuel. Furthermore, with the objective of quantifying the grade of residual oil in adulterated samples, starting from the ATR-FTIR data, models of multivariate calibration were built using PLS. To build the models, VSMs, forward and stepwise, were used. It is important to point out that, by the time this work was carried out, the established blend in Brazil, biodiesel/petrodiesel, was 2% (v/v). 2. Experimental Section 2.1. Samples. B2 samples were obtained from assays at the Laboratory of Fuel at Federal University of Minas Gerais. The residual oil (from used cooking vegetable oil) was donated by the University’s cafeteria. The samples were prepared by mixing B2 (21) Catharino, R. R.; Milagre, H. M. S.; Saraiva, A. S.; Garcia, C. M.; Schuchardt, U.; Eberlin, M. N. Energy Fuels 2007, 21, 3698–3701. (22) Eide, I.; Zahlsen, K. Energy Fuels 2007, 21, 3702–3708. (23) Corgozinho, C. N. C.; Pasa, V. M. D.; Barbeira, P. J. S. Talanta 2008, 76, 479–484. (24) Desantes, J. M.; Payri, R.; Garcı´a, A.; Manin, J. Energy Fuels 2009, 23, 3227–3235.
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Table 1. Physical-Chemistry Parameters for B2 Samples Adulterated with Residual Oil samples physical-chemistry assay
specification ANP
method uncertainty
B2
B2-05a
B2-10a
B2-15a
B2-20a
B2-25a
viscosity flash point specific gravity distillation (50%) distillation (85%) cetane index
2.5-5.5 (cSt) min at 38 (°C) 820-865 (kg m-3) 245-310 °C max at 360 °C min at 42
0.011% 1.3% 0.04% 0.5% 0.7% 0.18%
3.700 77.6 861.00 289.1 342.6 46.40
4.000 81.3 862.20 289.9 350.5 46.00
4.000 79.3 865.00 293.9 352.1 45.20
4.200 83.3 869.20 298.9 346.2 43.30
4.400 84.3 871.90 304.7 337.8 41.50
4.600 84.3 873.70 308.0 310.4 41.90
a
These values are referred to as a percentage (w/w) of the residual oil.
samples with residual oil in different concentrations varying from 0.5 to 25% (w/w) with 0.5% (w/w) increments, totaling 50 samples. From this total, 35 samples were used as a calibration set, while 15 samples were used as an external validation set. 2.2. Physical-Chemistry Assays. All samples were submitted to physical-chemistry assays, usually used to monitor the fuel quality. They were carried out using the appropriate standard method defined by the Brazilian Government Agency, ANP, as showed in Table 1. Physical-chemistry assays were performed using the following equipment according to their respective standard methods. The specific gravity assay at 20 °C was carried out in a digital densitometer, model Anton Paar DMA 4500 (ASTM D4052). The viscosity assay was performed using a 150 mL capillary tube in a Thermo Haake bath at 40 °C (ASTM D445). The flash point was carried out with closed-vessel flash point equipment, model ISL FP93 5G2 (ASTM D93). The distillation curves were performed with a automatic distillatory model, Herzog HDA 627 (ASTM D86). Only two points of 50 and 85% distillated volumes were used. Cetane indexes were calculated using some points at the distillation curve, such as 10, 50, and 90% distillated volumes, and the specific gravity (ASTM D4737). 2.3. ATR-FTIR Analysis. ATR-FTIR spectra were obtained using an ABB Bomen IR spectrometer model MB 102 equipped with an ATR sampling accessory with a deuterated triglycerine sulfate detector. All spectra were colleted at 16 ( 1 °C using an average of 16 scans, with spectral resolution of 2 cm-1. The background spectra were obtained using a clean ATR accessory. After each spectrum was recorded, the cell was cleaned by successive treatments with heptane. The average spectra from triplicate analysis ranging from 4000 to 665 cm-1 were treated chemometrically using MINITAB software, version 14. To develop a good PLS calibration model for FTIR spectra data, it was necessary to eliminate spectra regions, which do not give enough information. These regions are those in which changes in the concentration of residual soybean oil in B2 did not cause substantial changes in the absorbance values. Furthermore, the noise associated with each spectral channel is also eliminated. The spectra region chosen was between 1500-2750 and 3000-4000 cm-1. 2.4. Data Analysis by Multivariate Calibration. PLS is a powerful model for the analysis of mixtures and is the current means of choice when prediction is the main objective. In PLS regression, the objective is to assess the degree of relationship between a set of x-predictor variables and a set of y-outcome variables.7 The predictive residual error sum of squares (PRESS) and root-meansquare error of calibration (RMSEC) are calculated, and both together define the criterion for latent variable (LV) number selection.16 The LV number is determined to be the number where the RMSEC begins to decrease insignificantly with the increase of the LV number. The forward VSM adds variables to the model one at a time. The first variable included in the model is the one that has the highest correlation with the independent variable y. The variable that enters the model as the second variable is the one that has the highest correlation with y, after y has been adjusted to the first variable. This process finishes when the last variable entering the model has an insignificant regression coefficient or all of the variables are included in the model.25 (25) Xu, L.; Zhang, W. Anal. Chim. Acta 2001, 446, 477–483.
Figure 1. Viscosity for B2 samples mixed with residual oil.
In a stepwise procedure, a variable that entered the model in the early stages of selection may be deleted at later stages. That is, the stepwise method is essentially a forward selection procedure, but at each stage, the possibility of deleting a variable, as in backward elimination, is considered. The number of variables retained in the model is based on the levels of significance assumed for inclusion and exclusion of variables from the model.25 Several stepwise selection schemes have been proposed to select wavelengths from small data sets faster and more methodically than the search procedures. The advantages of these methods are speed and simplicity.16
3. Results and Discussion 3.1. Physical-Chemistry Assays. According to ANP, this blend commercialized in the country must follow the same specifications as petrodiesel to be commercialized. To study the behavior changes in the physical-chemistry properties of the blend of petrodiesel/biodiesel (B2) adulterated with residual oil, assays were carried out by the standard methods already established for petrodiesel. Table 1 presents the results of physical-chemistry assays, such as viscosity, flash point, specific gravity, distillation, and cetane index for B2 samples, referred to conform, and its blends with residual oil with concentrations varying from 0.5 to 25% (w/w). The changes in the physical-chemistry properties with the increase of the residual oil in the blends are showed graphically in Figures 1-6. Viscosity results are presented in Figure 1. It is observed that the viscosity values tend to increase as the residual oil concentration increases. This change is higher when the residual oil concentration varies from 0 to 5% (w/w). In this case, viscosity increases from 3.7 to 4.0 Cst. From this point on, the viscosity values increase in a smooth and constant way until the final value of 4.6 Cst, which corresponds to the concentration (26) Wenzel, G.; Lammers, P. S. J. Agric. Food Chem. 1997, 45, 4748– 47452.
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Figure 2. Flash point for B2 samples mixed with residual oil.
Figure 3. Distillation curve (T50) for B2 samples mixed with residual oil.
of 25% (w/w). Although the samples have been adulterated with a large amount of residual oil, they would still be considered in agreement with the specification because according to ANP this value cannot exceed 5 Cst. Apparently, the residual oil forms a homogeneous mixture with the diesel/biodiesel mixture, and there are no considerable viscosity variations in this system, even for higher concentrations (25%, w/w). Flash point results are showed in Figure 2. The flash point values suffer a small variation with the gradual addition of residual oil. Therefore, the samples would never be out of specification because its values increase as the residual oil concentration also increases. It is because the residual oil, which came from raw vegetable oil, is less inflammable than petrodiesel and biodiesel. The specification demands only the minimum temperature. Thus, a very high value in the flash point can be indicative of the presence of not only esterified oil but also residual and/or raw vegetable oil. Figure 3 shows the results of the distillation curve at T50 point (50% of distilled volume) for adulterated samples. It is observed that, as the residual oil concentration increases in the blend, the distillation temperature at this point also increases. The sample with a larger amount of residual oil, 25% (w/w), presented a temperature next to the superior limit allowed for the distillation temperature at T50, but it is still within the demanded specification, between 245 and 310 °C. The results presented in Figure 4 are relative to the assay of the distillation curve at T85 (85% of distilled volume). It is observed that this point of the distillation was also affected by the adulteration, nevertheless in a distinct way of the T50 distillation point. There is an ascendant trend in temperature
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Figure 4. Distillation curve (T85) for B2 samples mixed with residual oil.
Figure 5. Specific gravity for B2 samples mixed with residual oil.
Figure 6. Cetane index for B2 samples mixed with residual oil.
when the samples with the addition of 5 and 10% (w/w) are compared to the sample of B2. At 10% (w/w), a decrease in the temperature of distillation can be observed. This behavior can be explained by the degradation process of the residual oil at higher temperatures. According to Wenzel’s work,25 there is a remarkable difference in the distillation curve shape between petrodiesel and biodiesel. Thus, the blends of petrodiesel/ biodiesel will also present differences because of the difference in their thermal degradation process. The presence of residual oil will increase the difference in this process even more, because it has a very different composition of petrodiesel. Although it demonstrated the alteration at the T85 point of the
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Figure 7. PLS calibration models for B2 samples adulterated with residual oil: (a) without variable selection, (b) stepwise method, and (c) forward method.
distillation curve, it did not generate nonconformity for these samples of B2 blends. Figure 5 presents the results of the specific gravity assay. This assay was the most affected with residual oil addition. When the residual oil addition reaches the concentration of 15% (w/w), the sample B2 already presents nonconformity; that is, it is already out of specification because the maximum value accepted is 865 kg m-3. The specific gravity value reaches 873.7 kg m-3 as the residual oil concentration increases. It is important to point out that the percentage of residual oil leading to nonconformity is directly correlated to the residual oil density. It means that in some cases this nonconformity could be verified before or after 15% of residual oil. It only depends upon the origin of the residual oil. The cetane index results are showed in Figure 6. These values are calculated using only three points withdrawn from the distillation curve (10, 50, and 90% distillate volumes) and the values of the specific gravity assay. Thus, it also leads to nonconformity of the blends analyzed but only when the residual oil addition reached the concentration of 20% (w/w). The cetane index decreases as the concentration of residual oil increases. As observed in Table 1 and Figures 1-6, even though the residual oil addition modifies the values of the physicalchemistry assays of viscosity, flash point, distillation at T50, and distillation at T85, in relation to the sample of B2, the adulterated fuel with 25% (w/w) of residual oil continues within specification established for commercialization. The most sensitive assay for adulteration with residual oil was specific gravity, although it classifies as out of specification only when the concentration of the residual oil reaches 10% (w/w). This reinforces the importance of the development of methods associated with chemometric tools to assist in the analysis of the quality of the commercialized fuel. 3.2. Residual Oil Quantification in B2 Using PLS. Figure 7 shows the analytical curves of the real values versus predicted values for the adulterated samples of B2 with residual oil of
Table 2. Results for the PLS Calibration Model for Blends of B2 Adulterated with Residual Oil variable selection
RMSEC
RMSEP
VCM
LV
R2
none stepwise forward
0.33 0.28 0.49
0.37 0.74
562 35 29
8 7 8
0.998 0.998 0.995
soybean, from which the efficiency in the use of the spectrometry of infrared ATR-FTIR associated with chemometric tool PLS is noticed. The samples were prepared using the blockage technique. Bliodiesel/petrodiesel blends (B2) were prepared using biodiesel from three different sources and, afterward, were added the residual oil. Neither biodiesel source nor the amount of residual oil caused interference in the calibration models, as demonstrated in Figure 7. The error between real and predicted values varies in an aleatory pattern throughout the analytical curves. Three models of calibration have been constructed: without variable selection and with variable selection stepwise and forward. The three showed analytical curves sufficiently similar. It is observed in panels a-c of Figure 7 that the dispersion of data throughout the analytical curves are similar in the three models. Table 2 shows the results of the correlation coefficient (R2), RMSEC, root-mean-square error of prevision (RMSEP), latent variables (LVs) for the PLS calibration model with and without variables selection, and the variable number of the calibration model (VCM). The models were built using MINITAB software, version 14. There was a limitation in the software algorithm in relation to the variable numbers to be used. It is not possible to execute external validation and, consequently, to calculate RMSEP values for the PLS calibration model built using all variables. The maximum variable number accepted by the software is 1000, and using all variables will give a total number of 1124 (562 calibration variables and 562 predicted variables). A small difference in the errors between the models is observed in Table 2. The model built with all variables had a similar efficiency to the model constructed with variable
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Table 3. Comparison among Models Using the F Test variable selection method
F test
F value
without variable selection/stepwise stepwise/forward
1.39 3.06
2.47
selection stepwise. The model built with the selection forward presented RMSEC of 0.49 against 0.28 of stepwise and RMSEP of 0.74 against 0.37 of stepwise. The coefficient of correlation R2 was 0.998 for the model without variable selection and model with variable selection stepwise and 0.995 for the model with variable selection forward. A similar work was already performed by Corgozinho et al.23 among our research group using another spectroscopic technique, spectrofluorometry, to study adulteration with raw vegetable oils differently from ours. In this case, the RMSEP values obtained were 7 times greater than those obtained in the present work. To verify the efficiency of the built-up models, the F test was applied, considering the values of RMSEC. The results are presented in Table 3. It is observed in Table 3 that the value obtained in the F test for the model with all variables and the model with stepwise VSM is lower than the F value in the table. Thus, one can infer with reliability of 95% that the differences between the models are not statistically significant. The same test applied to both methods, stepwise and forward, showed that the calibration models are statistically different; that is, for the set of samples used, the selection method stepwise was more efficient. 4. Conclusions Although the residual oil has a very distinct chemical composition from vegetable oil “in nature” and the blend of
petrodiesel/biodiesel (B2), it seems that the vast majority of physical-chemistry assays were not sensitive enough to detect the adulteration problem, even within high concentrations. The samples analyzed were considered in conformity, which means within the specification even with the addition of 25% (w/w) of residual oil. This shows that the standard methods used to monitor this kind of fuel are not efficient in determining the adulteration with residual oil even in large quantities. This fact is alarming when there is a concern with the quality of the fuel to be commercialized. Thus, new methodologies that are able to determine this kind of adulteration without errors should be sought. For that reason, methodologies developed in the present work become more efficient in the direction of assisting the determination of adulterations that are not perceivable to the methodologies stipulated by the ANP. Among the performed assays, the specific mass assay revealed itself to be more sensitive to the adulteration with residual oil. Those samples with a quantity of residual oil equal or higher than 10% (w/w) were considered out of specification. The PLS chemometric tool applied together with ATR-FTIR data was efficient in the determination of adulteration of B2 with residual oil of 25% (w/w). Despite the methods of variable selection not increasing the efficiency of the models, the use of these methods was interesting because it made possible the validation of the built models, which was not possible using all of the spectrum data. The use of multivariate calibration to quantify the presence of residual oil can be performed in any biodiesel blend. Thus, this could be applied to other countries with the same kind of adulteration problems. EF900302Q
