(Bn) Blends by Easy Ambient Sonic-Spray ... - ACS Publications

Oct 19, 2012 - *Telephone/Fax: (+55) 19-35213073. ... Easy sonic-spray ionization mass spectrometry (EASI–MS) allows for direct ... with little or n...
0 downloads 0 Views 971KB Size
Article pubs.acs.org/EF

Quantitation and Quality Control of Biodiesel/Petrodiesel (Bn) Blends by Easy Ambient Sonic-Spray Ionization Mass Spectrometry Ildenize B. S. Cunha,† Anna Maria A. P. Fernandes,† David U. Tega,‡ Rosineide C. Simas,† Heliara L. Nascimento,† Gilberto F. de Sá,§ Romeu J. Daroda,∥ Marcos N. Eberlin,† and Rosana M. Alberici*,† †

ThoMSon Mass Spectrometry Laboratory, and ‡Analytical Center, Institute of Chemistry, University of Campinas, 13083-970 Campinas, São Paulo (SP), Brazil § Department of Fundamental Chemistry, Federal University of Pernambuco, 50590-470 Recife, Pernambuco (PE), Brazil ∥ National Institute of Metrology, Quality and Technology (INMETRO), 25250-020 Duque de Caxias, Rio de Janeiro (RJ), Brazil ABSTRACT: Easy sonic-spray ionization mass spectrometry (EASI−MS) allows for direct and fast MS analysis of samples in ambient conditions with little or no sample preparation, therefore offering unprecedented simplicity, speed, and ease of use. EASI−MS has been shown to access the quality, type, and adulteration of biofuels and vegetable oils. Herein, EASI−MS is shown to quantitate and monitor the quality of soybean biodiesel/petrodiesel (Bn) blends. For adulteration, admixture of the parent oil has been tested and nearly instantaneous and direct EASI(+)−MS detection of as little as 1% (w/w) of soybean oil in biodiesel/ petrodiesel blends was achieved. Linear analytical curves (r > 0.98) were also obtained for the quantitation of Bn blends, and the EASI(+)−MS quantitation results were compared to those obtained by nuclear magnetic resonance (NMR) spectroscopy and mid-infrared (IR) spectroscopy.



INTRODUCTION Biodiesel is currently one of the most promising sources of renewable energy. Worldwide, the use of biodiesel/petrodiesel blends is becoming a common and economically viable practice.1 In Brazil, B2 admixtures were the first to become mandatory in 2008, but B5 is already being used. The determination of the blend level (Bn) and overall quality has therefore become an important key aspect for the commercialization of Bn blends. Identification and quantitation of Bn blends have used1−5 spectroscopic methods,6,7 mainly midinfrared (IR) spectroscopy, and multivariable calibration models based on spectroscopy data.8−11 Spectroscopy methods are often rapid and facile but with reduced selectivity, giving results in which all components of a mixture contribute simultaneously to the resulting spectrum with interfering bands. Quantitation via these methods is possible only when the target component in the mixture exhibits a unique and well-defined band. IR therefore uses the carbonyl band at 1740−1750 cm−1 for the biodiesel [fatty acid methyl esters (FAMEs)] to determine Bn levels.6 In Brazil, the regulatory agency [Agência Nacional do ́ Petróleo, Gás Natural e Biocombustiveis (ANP)] has also recommended the use of IR (ANP Resolution Number 31− Method NBR 15558) for the quantitation of Bn blends. However, the method is unable to detect some adulterations, such as for instance the admixture of the parent vegetable oil. This is a widespread adulteration practice, and several methods have been evaluated for its monitoring.12−14 Vegetable oils are mainly constituted of triacylglycerols (TAGs), which also bear the carbonyl functionality and may therefore produce © 2012 American Chemical Society

overlapping IR bands. This overlapping may lead to failures of IR to detect adulteration. Easy ambient sonic-spray ionization mass spectrometry (EASI−MS)15 is an ambient ionization technique16,17 that allows for direct and fast MS analysis of samples in the open atmosphere with little or no sample preparation, therefore offering unprecedented speed and ease of use. Being based on sonic-spray ionization (SSI),18,19 EASI requires no assistance of voltages, radiation, or heating and operates with the sole assistance of a high-velocity (sonic) nebulizing gas, causing little or no fragmentation. EASI forms minute droplets with unbalanced charge distribution that desorb the analytes from surfaces promoting their ionization and transfer to mass spectrometer. We have extensively tested this technique for the analysis of biodiesel20−23 and vegetable oils.24−26 Herein, we report results of the application of EASI−MS as a fast, simple, and reliable technique able to monitor quality and quantify biodiesel/petrodiesel (Bn) blends at the molecular level.



EXPERIMENTAL SECTION

Chemical Reagents and Samples. High-performance liquid chromatography (HPLC)-grade methanol was purchased from Merck SA (Rio de Janeiro, Brazil) and used without further purification. Soybean-based biodiesel was obtained by the transesterification reaction in basic medium, via the methanol route, and was supplied Received: June 27, 2012 Revised: October 2, 2012 Published: October 19, 2012 7018

dx.doi.org/10.1021/ef3010866 | Energy Fuels 2012, 26, 7018−7022

Energy & Fuels

Article

by producers authorized by the ANP. The petrodiesel was supplied by Petrobras, coded as S1800 and sampled under the acronym TQ-4729. Samples were prepared by mixing biodiesel with petrodiesel to define the levels of the blends in the following proportions: 0.2, 0.5, 1.0, 2.0, 3.0, 5.0, and 7.0% (w/w). Thus, seven blends called B0.2, B0.5, B1, B2, B3, B5, and B7 were used to construct the analytical curve. The biodiesel/petrodiesel blend samples were diluted 10 times in methanol. Pure biodiesel and petrodiesel were called B100 and B0, respectively. Methyl ricinoleate (R-12-hydroxy-cis-9-octadecenoic acid methyl ester, C19H36O3, Sigma Aldrich) was used as an internal standard (IS) to quantify the biodiesel/petrodiesel blends. Stock solution of 11.5 mg/mL was prepared in HPLC-grade hexane and diluted 100 times. Four B5 blends were adulterated with the addition of commercial soybean oil and analyzed by EASI−MS. The soybean oil was added in the range of 0−3% (w/w), namely, 0, 1, 2, and 3%, resulting in a biodiesel percentage of 5, 4, 3, and 2% (w/w), after the adulteration with oil. General Experimental Procedures. EASI−MS was performed in the positive-ion mode using a single-quadrupole mass spectrometer (Shimadzu) equipped with a homemade EASI source, which is described in detail elsewhere.15 The main experimental parameters were as follows: methanol flow rate of 20 μL min−1, N2 nebulizing gas of 3 L min−1, and paper-entrance angle of ∼30°. A tiny droplet of the biodiesel/petrodiesel blend (2 μL) was placed directly onto the paper surface (brown Kraft envelope paper), and mass spectra were accumulated over 60 s, being scanned over the m/z 300−400 range for analytical curves.



RESULTS AND DISCUSSION As Figure 1 illustrates, EASI(+)−MS of pure soybean biodiesel (B100), which was selected for this study because of the

Figure 2. EASI(+)−MS of representative Bn blends. Note that the ion of m/z 317 is the major [FAME + Na]+ ion for soybean biodiesel, whereas the ion of m/z 335 is from the IS. These spectra correspond to analyst 2.

Figure 3. Analytical curves obtained by three analysts from EASI(+)− MS data of soybean biodiesel/petrodiesel blends.

Brazilian biodiesel program that is highly focused on this feedstock, and pure petrodiesel (B0) are very different and characteristic. The spectrum of a B0 (Figure 1A) displays a diverse set of ions corresponding to the ionization of characteristic markers of petrofuels, that is, a homologous series of alkylpyridines.21,27,28 Panels B and C of Figure 1 show B5 and B100, respectively. EASI(+)−MS detects FAMEs mainly as their sodium adducts [FAME + Na]+ with predominance of linoleic acid (m/z 317) and minor ions from esters of oleic acid (m/z 319) and linolenic acid (m/z 315), therefore being a method of high selectivity and specificity.

Figure 1. Typical EASI(+)−MS for (A) petrodiesel (B0), (B) biodiesel/petrodiesel blend (B5), and (C) pure biodiesel (B100) samples. 7019

dx.doi.org/10.1021/ef3010866 | Energy Fuels 2012, 26, 7018−7022

Energy & Fuels

Article

Table 1. Figures of Merit for Quantitative Analysis of Biodiesel/Petrodiesel Blends by EASI−MS

a

analyst

linear range (%, w/w)

equation

R2 (n = 3)

LOQ (%, w/w)

LOD (%, w/w)

Sa

1 2 3

0.2−7.0 0.2−7.0 0.2−7.0

0.2478x − 0.0578 0.2195x − 0.0585 0.3763x − 0.0493

0.9839 0.9971 0.9926

0.2 0.2 0.2

0.1 0.1 0.1

0.0877 0.0376 0.0952

S = square root of the mean square error.

Table 2. Precision and Recovery for the Three Analysts at Low, Middle, and High Levels (n = 3) mean recovery from three analysts low level B 0.50% (w/w)

analyst 1 2 3 mean of the analysts SD CV

medium level B 3.00% (w/w)

high level B 7.00% (w/w)

mean mean mean mean recovery mean recovery mean recovery (%) (%) (n = 3) (%) (n = 3) (n = 3) 0.58 0.58 0.53 0.56

116.19 115.04 105.60 112.27

3.01 2.82 2.86 2.90

100.39 93.86 95.42 96.56

6.74 7.26 6.72 6.91

96.32 103.69 96.00 98.67

0.05 8.34

9.37 8.34

0.18 6.37

6.15 6.37

0.35 5.02

4.95 5.02

Figure 4. EASI(+)−MS of (A) pure B5 soybean biodiesel/petrodiesel sample and (B) B5 blend adulterated with just 1% (w/w) of soybean oil.

Figure 2 shows the spectra for B0.5, B5, and B7 blends in the m/z 300−400 range with the addition of methyl ricinoleate (m/z 335) as the IS. Note that the addition of as little as 0.5% (w/w) of biodiesel to petrodiesel is readily identified by EASI(+)−MS via the biodiesel ion [FAME + Na]+ of m/z 317 (Figure 2A). Note also the increase of the relative abundance of the ion of m/z 317 as a function of the increase of the biodiesel concentration within petrodiesel, as compared to the IS (m/z 335). The use of the IS permitted quantitation in quite a precise way, for a direct infusion method, by compensating for the variations in ionization efficiency of each measure.23 The ratio of the ion abundances of m/z 317 and 335 were then used to build the calibration curves (Figure 3). To investigate the precision of the method for the analysis of soybean biodiesel/petrodiesel blends by EASI−MS, calibration curves obtained by different analysts (1, 2 and 3) were constructed from seven points in triplicate, showing that indeed EASI(+)−MS with a proper IS produces calibration curves with quite high correlation coefficients (r2 > 0.98) and adequate coefficients of variation. Table 1 shows the main figures of merit for quantitative analysis of biodiesel/petrodiesel blends by EASI−MS. The method precision and recovery are quite good,29 as can be seen in Table 2. The coefficient of variation obtained was at the maximum of 8.3% at the low level that can be considered very good. The contribution of different operators was studied running an analysis of variance (ANOVA) test to compare the data among the three different analysts. Table 3 shows the statistical results obtained for the different levels of concentrations, where one can notice that there is no difference between analyst means because the F calculated value is under its critical value and, of course, the p value is higher than 0.05% for these concentrations; therefore, the null hypothesis is accepted. To warrant that possible differences among analysts do not interfere with the results, each analyst must build his own curve each day of analysis and then analyze the unknown samples. To evaluate the accuracy of the method for real samples, two commercial B5 samples were quantitated by IR, nuclear

magnetic resonance (NMR) and EASI−MS (Table 4). Note the very similar results presented by all three techniques, with p > 0.05% when the Student one-sample t test was applied.29 EASI(+)−MS does not monitor a specific ion or a set of ions but provides instead a quite comprehensive monitoring of the chemical composition of the sample. It therefore offers a powerful approach for Bn quality control. To test this feature of Bn overall quality monitoring, Bn admixtures with the parent vegetable oil was used as an example for adulteration monitoring. Figure 4 shows typical EASI(+)−MS of the B5 blend (Figure 4A) and a B5 blend adulterated with as little as 1% (w/w) of soybean oil (Figure 4B). Note the extraordinary ability of the technique to detect this contamination via the very abundant cluster of [TAG + Na]+ (mainly those of m/z 877, 901 and 903) together with the less abundant but also typical set of Bn ions.24,27 To test the selectivity of EASI−MS to properly quantitate Bn blends, as compared to that of the official IR technique, biodiesel blends were prepared by spiking 1, 2, and 3% (w/w) of soybean oil, resulting in four adulterated biodiesel samples A−D (Table 5). Note that the results from EASI(+)−MS displayed no significant deviation from the real Bn values for the blends (Student t test; p > 0.05%). However, IR, because of the monitoring of the carbonyl band, was unable to detect the adulteration and, therefore, fails to discriminate the vegetable oil from biodiesel; hence, values close to the unreal B5 value were obtained for all of the adulterated samples.



CONCLUSION The EASI(+)−MS technique, which requires just a tiny droplet of the samples, when performed directly on the untreated sample with no preseparation, has been shown to provide comprehensive compositional profile of Bn blends. When a proper IS was added, nearly instantaneous, direct EASI(+)−MS analysis is also able to quantitate Bn blends in a precise and accurate way. Moreover, this technique seems to be an efficient 7020

dx.doi.org/10.1021/ef3010866 | Energy Fuels 2012, 26, 7018−7022

5.143253

p value

0.074774

F

2 6 8

4.120962

MS

0.278249 0.067521

DF SS

0.556498 0.405124 0.961622 6.91 3 3.76 0.45507

p value F

2 6 8

NMR (%, w/w) (n = 2)

1 2

5.1 6.8

5.22 ± 0.63 6.71 ± 0.65

4.6 6.9

sample

real BD (%, w/w)

soybean oil (%, w/w)

A B C D

5 4 3 2

0 1 2 3

IR (%, w/w) 4.63 4.84 4.43 4.29

± ± ± ±

0.02 0.05 0.04 0.07

EASI−MS (%, w/w) 5.07 4.20 3.47 1.87

± ± ± ±

0.34 0.30 0.07 0.17

AUTHOR INFORMATION

Corresponding Author

*Telephone/Fax: (+55) 19-35213073. E-mail: rmalberici@ hotmail.com.

0.900275

MS

0.031436 0.034918

DF

EASI−MS (%, w/w) (n = 3)



Notes

The authors declare no competing financial interest.

■ ■

ACKNOWLEDGMENTS The authors are grateful to the Brazilian National Research Council (CNPq) for the financial support. REFERENCES

(1) Monteiro, M. R.; Ambrozin, A. R. P.; Lião, L. M.; Ferreira, A. G. Fuel 2009, 88, 691−696. (2) Knothe, G. J. Am. Oil Chem. Soc. 2001, 78, 1025−1028. (3) Knothe, G. J. Am. Oil Chem. Soc. 2006, 83, 823−833. (4) Scherer, M. D.; Oliveira, S. L.; Lima, S. M.; Andrade, L. H. C.; Caires, A. R. L. J. Fluoresc. 2011, 21, 1027−1031. (5) Prates, R. G. D.; Augusti, R.; Fortes, I. C. P. Energy Fuels 2010, 24, 3183−3188. (6) Lôbo, I. P.; Ferreira, S. L. C. Quim. Nova 2009, 32, 1596−1608. (7) Monteiro, M. R; Ambrozin, A. R. P.; Lião, L. M.; Ferreira, A. G. Talanta 2008, 77, 593−605. (8) Guarieiro, L. L. N.; Pinto, A. C.; Aguiar, P. F.; Ribeiro, N. M. Quim. Nova 2008, 31, 421−426. (9) Aliske, M. A.; Zagonel, G. F.; Costa, B. J.; Veiga, W.; Saul, C. K. Fuel 2007, 86, 1461−1464. (10) Gaydou, V.; Kister, J.; Dupuy, N. Chemom. Intell. Lab. Syst. 2011, 106, 190−197. (11) Pimentel, M. F.; Ribeiro, G. M. G. S.; Cruz, R. S. C; Stragevitch, L.; Pacheco Filho, J. G. A.; Teixeira, L. S. G. Microchem. J. 2006, 82, 201−206. (12) Cruz de Vasconcelos, F. V.; Barbosa de Souza, P. F., Jr.; Pimentel, M. F.; Pontes, M. J. C.; Pereira, C. F. Anal. Chim. Acta 2012, 716, 101−107. (13) Soares, I. P.; Rezende, T. F.; Pereira, R. C.; dos Santos, C. G.; Fortes, I. C. P. J. Braz. Chem. Soc. 2011, 22, 1229−1235. (14) Oliveira, F. C. C.; Brandao, C. R. R.; Ramalho, H. F.; da Costa, L. A. F.; Suarez, P. A. Z.; Rubim, J. C. Anal. Chim. Acta 2007, 587, 194−199. (15) Haddad, R.; Sparrapan, R.; Eberlin, M. N. Rapid Commun. Mass Spectrom. 2006, 20, 2901−2905.

0.062872 0.209509 0.272381 2.90 3 6.45

SS p value

IR (%, w/w) (n = 2)

tool for monitoring parent oil adulterations. As an example, only 1% (w/w) of soybean oil addition has also been readily detected in Bn blends. EASI(+)−MS therefore seems to offer a powerful technique for the quality control and quantitation of Bn blends.

0.360563 between groups within groups total mean n= pooled CV =

real sample

Table 5. Quantitation by Techniques IR and EASI−MS of Soybean Biodiesel/Petrodiesel B5 Blends Adulterated with Soybean Oil

1.214968

F MS

Table 4. Quantitation of B5 Soybean Biodiesel/Petrodiesel B5 Blends by IR, EASI−MS, and NMR

0.00253 0.002082

SS

0.005059 0.012493 0.017552 0.56 3 8.13

source of variation

DF

Article

2 6 8

medium level B 3.00% (w/w) low level B 0.50% (w/w)

Table 3. Comparison among Three Analysts (3 Replicates Each) Using ANOVA at the Levels Low, Middle, and High

high level B 7.00% (w/w)

F critical value

Energy & Fuels

7021

dx.doi.org/10.1021/ef3010866 | Energy Fuels 2012, 26, 7018−7022

Energy & Fuels

Article

(16) Alberici, R. M.; Simas, R. C.; Sanvido, G. B.; Romão, W.; Lalli, P. M.; Benassi, M.; Cunha, I. B. S.; Eberlin, M. N. Anal. Bioanal. Chem. 2010, 398, 265−294. (17) Harris, G. A.; Galhena, A. S.; Fernandez, F. M. Anal. Chem. 2011, 83, 4508−4538. (18) Hirabayashi, A.; Sakairi, M.; Koizumi, H. Anal. Chem. 1995, 67, 2878−2882. (19) Hirabayashi, A.; Hirabayashi, Y.; Sakairi, M.; Koizumi, H. Anal. Rapid Commun. Mass Spectrom. 1996, 10, 1703−1705. (20) Alberici, R. M.; Simas, R. C.; Abdelnur, P. V.; Eberlin, M. N.; de Souza, V.; de Sá, G. F.; Daroda, R. J. Energy Fuels 2010, 24, 6522− 6526. (21) Eberlin, L. S.; Abdelnur, P. V.; Passero, A.; de Sá, G. F.; Daroda, R. J.; de Souza, V.; Eberlin, M. N. Analyst 2009, 134, 1652−1657. (22) Abdelnur, P. V.; Eberlin, L. S.; de Sá, G. F.; de Souza, V.; Eberlin, M. N. Anal. Chem. 2008, 80, 7882−7886. (23) Fernandes, A. M. A. P.; Tega, D. U.; Jara, J. L. P.; Cunha, I. B. S.; de Sá, G. F.; Daroda, R. J.; Eberlin, M. N.; Alberici, R. M. Energy Fuels 2012, 26, 3042−3047. (24) Simas, R. C.; Catharino, R. R.; Cunha, I. B. S.; Cabral, E. C.; Barrera-Arellano, D.; Eberlin, M. N.; Alberici, R. M. Analyst 2010, 135, 738−744. (25) Cardoso, K. C.; Da Silva, M. J.; Grimaldi, R.; Stahl, M.; Simas, R. C.; Cunha, I. B. S.; Eberlin, M. N.; Alberici, R. M. J. Am. Oil Chem. Soc. 2011, 89, 67−71. (26) Simas, R. C.; Barrera-Arellano, D.; Eberlin, M. N.; Catharino, R. R.; Souza, V.; Alberici, R. M. J. Am. Oil Chem. Soc. 2012, 89, 1193− 1200. (27) Alberici, R. M.; Simas, R. C.; de Souza, V.; de Sá, G. F.; Daroda, R. J.; Eberlin, M. N. Anal. Chim. Acta 2010, 659, 15−22. (28) Haddad, R.; Regiani, T.; Klitzke, C. F.; Sanvido, G. B.; Corilo, Y. E.; Augusti, D. V.; Pasa, V. M. D.; Pereira, R. C. C.; Romão, W.; Vaz, B. G.; Augusti, R.; Eberlin, M. N. Energy Fuels 2012, 26, 3542−3547. (29) Ellison, S. L. R.; Barwick, V. J.; Farrant, T. J. D. Practical Statistic for the Analytical Chemist; The Royal Society of Chemistry: Cambridge, U.K., 2009.

7022

dx.doi.org/10.1021/ef3010866 | Energy Fuels 2012, 26, 7018−7022