Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

Journal of Chromatography A, 1201 (2008) 176–182

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Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing Marcio Pozzobon Pedroso, Luiz Antonio Fonseca de Godoy, Ernesto Correa Ferreira, Ronei Jesus Poppi, Fabio Augusto ∗ Institute of Chemistry, State University of Campinas (Unicamp), CP 6154, 13084-971 Campinas, S˜ ao Paulo, Brazil

a r t i c l e

i n f o

Article history: Available online 6 June 2008 Keywords: GC × GC N-PLS Multivariate analysis Gasoline Adulteration

a b s t r a c t A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GC × GC–FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GC × GC–FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Since its introduction by Phillips and co-workers [1,2], comprehensive two-dimensional gas chromatography (GC × GC) has been shown as an almost unsurpassable technique for analysis of highly complex mixtures, considering its resolution power and compared to other alternatives. Besides the resolution achievable (the peak capacities are typically close to the product of the individual peak capacities of the first and second dimension columns), the main advantages of GC × GC over conventional gas chromatography are the so-called chromatographic structure, as well as a significant improvement on detection limits of individual analytes. The first one allows the identification of the presence of classes of related compounds in a sample, while the second one is a consequence of the band compression in the modulation process and consequent increase in peak height and signal-to-noise ratios [3]. Several analytical problems have been successfully tackled by GC × GC, and among them the analysis of petrochemical samples are the most noteworthy.

∗ Corresponding author. Tel.: +55 1935213057; fax: +55 1935213023. E-mail address: [email protected] (F. Augusto). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.05.092

Petroleum and derivatives are some of the most complex samples known to analytical chemists. The presence of ordered structures in the two-dimensional contour plots, resulting from the predominance of groups of related species in these samples (alkanes, isoalkanes, alkenes, naphthenes, etc.) allow unequivocal identification by simple visual inspection of the chromatograms [4], even when using non-selective detectors such as flame ionization detection (FID). Some of the previously reported applications of GC × GC for petrochemical samples included the characterization of fuels [5] and of specific groups of analytes (aromatics [6], sulfur compounds [7] and oxygenated compounds [8]) on oil products, as well as environmental studies related to identification and monitoring of degradation of spilled oil [9,10]. Compared to conventional GC–FID (or even gas chromatography and mass spectrometry, GC–MS), the amount of information contained on a GC × GC–FID chromatogram is considerably larger. As a consequence, the adoption of chemometric strategies for processing and interpretation of GC × GC data is desirable. Tri-linear generalized rank annihilation method (GRAM) or parallel factor analysis (PARAFAC) deconvolution algorithms were shown to be adequate for quantitation of target analytes from GC × GC–FID [11,12] and GC × GC with time-of-flight mass spectrometry (TOF-MS) [13] chromatograms. Apart from traditional target identification and quantification,

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group-type analysis can be advantageously performed by chemometric or statistic processing of GC × GC data. Recent examples include the quantification of n-alkanes and polycyclic aromatic hydrocarbons in oil [14] and studies on the weathering of the different classes of compounds present on spilled oil [15]. Identification and classification of complex samples through pattern recognition based on GC × GC data has also been shown to be a promising field [16]. For example, after applying principal component analysis (PCA) to sections of GC × GC–FID chromatograms pre-selected by analysis of variance (ANOVA), Johnson and Synovec [17] were able to identify the type and the geographic origin of pure and blended samples of jet fuels. This approach was also adopted by Qiu et al. [18], for the classification of samples of Notopterygium incisum (qianghuo, a plant employed on traditional Chinese medicine) after PCA of GC × GC–FID and –TOF-MS data. Considering its features, GC × GC can be a powerful tool to monitor and detect adulteration of fuels, which is a widespread practice in several countries. For example, in Brazil, it is a major source of public concern and is a felony [19]. The most noticeable problem caused by adulterated fuel is the deterioration on vehicle performance [20]: ∼95% of all vehicle repairs in the city of S˜ao Paulo are directly or indirectly attributed to spoiled fuel. A less evident, but perhaps more serious issue, is the increase on the emission of pollutants. It is estimated that fuel adulteration caused a loss of D 900 million to Brazil in 2006, including D 350 million on evasion of federal and local taxes. Since 1999 the Brazilian National Oil, Gas and Biofuel Agency (Agˆencia Nacional do Petr´ oleo, G´ as Natural e Biocombust´ıveis – ANP) runs a nationwide fuel quality program [21], including 29 contract laboratories in Universities. This program collects and tests gasoline samples from retailers. The official ANP procedure consists of pre-screening of samples by American society for testing and materials (ASTM) tests [22] and chromatographic analysis of all suspect samples, as well as a random set of the samples not flagged as adulterated on the preliminary screening. The GC method is based on the detection of some isotopic markers on the samples. These markers should be added by producers and importers to all solvents sold in the country for non-fuel applications. However, this approach is expensive and demands a substantial network to audit the production and distribution of solvents. Obviously, non-marked adulterants available through black market are undetectable [23]. The evaluation of a GC × GC method to assess adulteration of the gasoline available in the Brazilian market (officially designated as Type C gasoline) should consider its specific composition. It consists of Type A gasoline (mixture of aliphatic and aromatic hydrocarbons with distillation range from 30 ◦ C to 220 ◦ C, not available for final consumers) mixed with (25 ± 1)% (v/v) anhydrous ethanol. The most usual adulterants found are ethanol in excess than the legally prescribed amounts, petrochemical solvents (white spirit, naphtha and raffinates), paint thinner, diesel oil, kerosene and turpentine. The complexity of gasoline and the broad range of possible adulterants (most of them complex mixtures themselves) render the chromatographic identification of its adulteration to be an overwhelming analytical problem, even for GC × GC. Furthermore, some usual adulterants have similar composition to gasoline; and the only regular adulterant which is a single chemical is the major individual component of Type C gasoline itself – ethanol. In a preliminary study [24], quantitative models for the composition of binary blends of Type C gasoline and kerosene were obtained applying chemometric tools (PARAFAC and PARAFAC2) to raw GC × GC–FID chromatograms of synthetic test samples. But the present problem is much more complex, since adulterated gasoline can contain any concentration of a wide range of possible species. Therefore, more sophisticated strategies [25], such as multi-way partial least squares (N-PLS) should be employed. The partial least

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squares (PLS) technique is a general method to find regression models correlating independent (X) and dependent (y) variables [28] and can be extended toward higher orders: the result is N-PLS. The aim of the three-way PLS algorithm is to decompose a three-way array X (I × J × K) into a set of triads. A triad consists of a score vector, t, related to the first model (samples) and two weight vectors wJ and wK related to the other two models (GC × GC chromatograms). The model is described by Eq. (1): Xijk =

F 

J

K tif wjf wkf + eijk

(1)

f =1

where eijk contains the regression residues and F is the number of latent variables. These vectors are calculated to have the maximum covariance with the dependent variable, y. The system is modeled by the reduction of the error in the predictions. The advantage of N-PLS models is that there is no need for a tri-linear data structure. Therefore, the construction of the models is straightforward and no external regression step is involved. The root mean square error (RMSE) is a measure of the variability of the difference between the predicted and reference values for a set of samples and depicts the ruggedness of the model. It is defined as



RMSE =

n (y i=1 /i

n

− yi )2

(2)

where y/i is the estimated concentration for sample i and yi is the real concentration of this sample; n is the number of samples used. GC × GC–FID combined to multivariate data processing can be a powerful tool to solve similar analytical problems where complex mixtures (such as gasoline and the adulterants here employed) should be treated and quantified as individual components of samples. The targets of a typical quantitative method are either single analytes or a limited group of similar species (such analysis for petroleum products as alkanes, isoalkanes, alkenes, naphthalenes and aromatics hydrocarbons, PIONA). This is a result of the large resolution power and high peak capacity typical of GC × GC. If conveniently processed, the amount of data contained on the chromatograms (even not using TOF-MS detection) is enough to retrieve information regarding macroscopic properties of the samples, which is not measurable using conventional chromatographic techniques. This approach can be adopted in several comparable problems, ranging from monitoring of authenticity and purity of essential oils to quality control and identification of ingredients on industrial preparations. In this paper, we describe a method to assess the purity of Type C gasoline. N-PLS regression models generated from GC × GC–FID chromatograms were used to analyze local gasoline. To assess these models, blends of non-adulterated gasoline and type adulterants (white spirit, kerosene and paint thinner) were employed. The models were checked using synthetic samples of gasoline adulterated with the test adulterants, as well as with ethanol (not present in the calibration samples). The method was also checked with nonsynthetic gasoline samples collected previously and analyzed by ANP local monitoring laboratory during routine monitoring of fuel quality of local gas stations. 2. Experimental 2.1. GC × GC–FID The GC × GC–FID prototype employed uses a HP-5890 Series II GC–FID system (Hewlett-Packard, Wilmington, DE, USA) fitted with a split–splitless injector and using H2 (0.52 mL min−1 ) as the car-

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rier gas. This prototype has a laboratory-made four-jet cryoscopic modulator, based on devices previously described in the literature [26,27]. The cryoscopic fluid was N2 (gas) cooled by LN2 ; N2 flow was toggled by two three-way Asco (Florham Park, NJ, USA) solenoid valves. The command of these valves and digitization of the FID signal was performed by a DAQPad-6015 16 bits AD/DA board controlled by self-made software developed using the LabView v.8.2 programming environment (National Instruments, Austin, TX, USA) and connected to an AMD Athlon 4600 GHz Dual Core personal computer. The column set consisted of a 30 m × 0.25 mm, 0.25 ␮m HP-5 capillary column (Agilent, Avondale, PA, USA) connected to a 1.0 m × 0.10 mm, 0.10 ␮m DB-wax column (J&W Scientific, Folsom, CA, USA) by a press fit connector. The modulation period was set to 6.0 s and the data acquisition rate was 100 Hz. For all runs the injection volume was 0.6 ␮L with 1:300 injector split ratio. The oven temperature program was: 3 min at 40 ◦ C → 4 ◦ C/min → 160 ◦ C → 20 ◦ C/min → 3 min at 240 ◦ C; injector and detector temperatures were 250 ◦ C. Duplicate runs were performed for all samples. 2.2. Reagents and samples Type C gasoline samples were supplied and certified by Unicamp Central Analytical Laboratory, CA-IQ/Unicamp (local ANP contract laboratory) and kept at 4 ◦ C until use. The calibration sample set had 25 blends of non-adulterated Type C gasoline and kerosene, paint thinner and white spirit (obtained at local commerce) and prepared by mixing adequate volumes of the solvents and gasoline, with concentrations selected according to a Greco-Latin square planning (Table 1). The test sample set consisted on 14 blends of non-adulterated Type C gasoline spiked with the same adulterants above and ethanol (Table 1). A second test set with 13 samples of Type C gasoline collected from local gas stations during routine fuel quality inspection and previously analyzed by CA-IQ/Unicamp, was also used. 2.3. Data processing The raw chromatograms were generated and stored as ASCII vector files. All calculations and graph generation were performed by routines running on the MatLab 6.5 platform (MathWorks, Natick, MA, USA) fitted with the N-way toolbox 2.11 [29], to generate N-PLS regression models. 3. Results and discussion The rationale for choosing of N-PLS modeling, as well as the selection of the solvents to be employed in the calibration sample set, should be addressed. The obvious procedure to detect gasoline adulteration would be the use of some fingerprinting or pattern recognition algorithm applied to chromatographic data, in order to determine the presence of foreign materials improperly mixed to the sample. However, in this case it would be necessary to have a training sample set containing all solvents known to be employed to adulterate gasoline. Considering the extensive variety of possible adulterants, such procedure would not be feasible. Therefore, instead of focusing on the detection of individual contaminants, our approach was to develop a generic method to determine how much Type C gasoline is contained in its blends with any adulterant. The basic hypothesis embodied is that the vast peak capacity of GC × GC would result in chromatograms containing peaks or groups of peaks related to the gasoline fraction of the blends that sufficiently separated from all peaks from any adulterant, in order to provide useful data for the modeling algorithm.

Table 1 Composition of synthetic blends of Type C gasoline and adulterants, in % (v/v) (E = anhydrous ethanol; W = white spirit; K = kerosene; T = paint thinner and G = nonadulterated Type C gasoline) used as calibration (C samples) and prediction (P samples) sets Sample

W

K

T

G

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25

E 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 30 30 0 5 10 20 10 0 10 20 0 5 5 20 10 0 20 20 10 30 30 5 5 30

0 10 30 20 0 10 10 0 10 30 30 30 5 10 5 20 5 20 0 5 0 20 20 30 5

0 5 0 5 20 0 20 5 30 30 10 20 0 10 5 10 10 0 30 20 10 20 30 5 30

100 55 40 75 75 80 50 85 60 30 40 50 90 75 70 60 85 60 50 65 60 30 45 60 35

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14

0 0 0 5 15 30 0 0 0 10 50 20 0 5

5 10 0 0 0 0 12 10 20 11 0 22 10 0

10 0 5 0 0 0 6 10 10 13 0 15 7 20

0 5 10 0 0 0 5 10 15 11 0 13 21 0

85 85 85 95 85 70 77 70 55 55 50 30 62 75

Therefore, a representative generic model for gasoline content applicable to non-synthetic samples depends on selecting proper solvents to be used in the calibration set, which should be as representative as possible of the range of adulterants found on the inspection samples. We choose two petrochemical products with different compositions – kerosene and white spirit. In addition, we used paint thinner which consists on a mix of oxygenated and aromatic compounds. The chemical species present in these materials are likely to be found in many of the possible adulterants. Fig. 1 shows typical GC × GC–FID chromatograms for neat samples of the solvents employed in the calibration sample set, as well as certified non-adulterated Type C gasoline. The GC × GC–FID operational parameters were adjusted in order to maximize the use of the separation space available (i.e., to spread the bands as much as much as possible on the 1 tR × 2 tR plane), to improve the likelihood of obtain functional models from these data as mentioned before. The modulation period was set to 6.0 s to avoid wrap-around of the broad and tailed ethanol peak on Type C gasoline, as well as peaks from other alcohols and oxygenated compounds found in paint thinner. These species are more polar than those with the same retention on the low polar first dimension column present in gasoline, but are separated from them in the second dimension column. A short modulation period could cause them to overlap with the saturated hydrocarbon band. The typical roof-tile structuration expected on

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179

Fig. 1. Typical GC × GC–FID chromatograms of pure (a) Type C gasoline, (b) white spirit, (c) kerosene and (d) thinner. Band identification: B = benzene, T = toluene, E = ethylbenzene, X = xylene isomers, C3 = benzene C3 -substituted, C4 = benzene C4 -substituted, C5 = benzene C5 -substituted and N = naphthalenes.

GC × GC chromatograms of petrochemical samples is clearly perceptible (identification of the structural groups was performed by inspection of chromatograms obtained from samples spiked with small amounts of pure standards of the suspect species). For the Type C gasoline chromatogram, the most distinctive feature is the band enclosing peaks of aliphatic, alicyclic and cyclic hydrocarbons with 1 tR from ∼3 min to ∼26 min and 2 tR from 1.0 s to 2.0 s (roughly corresponding from n-C5 to n-C12 ); species with 1 tR < 15 min (up to C8 ) predominate. Structural groups for alkyl-substituted monoaromatic compounds (from benzene to C6 -benzenes) are also present at 2 tR from 2.0 s to 3.5 s. Peaks for naphthalenes and substituted naphthalenes were also identified. Ethanol is observed as a large and tailed peak at 1 tR = ∼3 min and 2 tR > 2.0 s. The same structural groups are also visible on the GC × GC–FID chromatograms for kerosene and white spirit. However, the range of compounds in the structural series is different. For kerosene – which is a heavier distillate than gasoline – the band of aliphatic, alicyclic and cyclic hydrocarbons ranges from n-C8 to n-C16 and the detected monoaromatic structural groups extended from C2 - to C7 -benzenes. Finally, a larger number of more intense peaks corresponding to substituted naphthalenes can be detected. The GC × GC–FID profile for white spirit is more similar of that of gasoline. White spirit is richer in saturated and monoaromatic hydrocarbons with 1st dimension retention corresponding from that of n-C9 to n-C12 . Paint thinner has a considerably simpler profile: it is a synthetic mixture of solvents, containing aromatic hydrocarbons (especially toluene and xylenes), ethanol and butyleneglycol (wrapped band at 1 tR = 13.2 min and 2 tR = 5.8 s). For the chemometric analysis, the raw chromatograms (400 × 600 matrixes; each line corresponds to a modulation period) were arranged on a tri-dimensional array. Preliminary tests showed that previous data treatment (e.g., peak alignment or noise filtering) was not necessary. The tri-dimensional array incorporated the chromatograms for the 25 samples on the calibration set (C1

to C25 on Table 1), as well as for the 14 laboratory-made gasoline/solvent test samples (P1 to P14) and 13 non-synthetic gasoline samples (not listed in Table 1). Calibration models correlating the GC × GC–FID chromatograms and the content (%, v/v) for each test adulterant and Type C gasoline on the blends were determined applying N-PLS algorithm to the data; the models were validated using leave-one-out cross-validation and estimating root mean square error of cross-validation (RMSECV). RMSECV for models with a different number of latent variables and for each component of the blends are shown in Table 2. To avoid overfitting, the selected models had as few latent variables as possible to minimize the RMSECV: three for white spirit, four for thinner and five for the remainder. For these optimum models, the RMSECV were 5.7% (v/v) (gasoline), 2.7% (thinner) and 2.8% (white spirit, kerosene). The correlation coefficients r2 for predicted versus real concentrations curves corresponding to the N-PLS model curves are 0.985 (gasoline), 0.991 (kerosene), 0.986 (thinner) and 0.982 (white spirit). These RMSECV and r2 values suggest that performance of the GC × GC–FID + N-PLS method is adequate to quantify the components of gasoline/adulterant blends.

Table 2 RMSECV for calibration models with different numbers of latent variables, obtained for each component of blends Type C gasoline + adulterants (E = anhydrous ethanol; W = white spirit; K = kerosene; T = paint thinner and G = non-adulterated Type C gasoline) Number of latent variables

G T K W

1

2

23.3 11.5 10.3 9.4

12.5 5.8 4.4 6.3

3 6.9 3.2 3.1 2.8

Bold values correspond to the selected models.

4

5

6

6.0 2.7 3.1 2.8

5.7 2.9 2.8 3.7

5.9 3.0 3.0 3.7

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Table 3 Predicted and real concentrations of Type C gasoline (%, v/v) for the test sample set, and relative errors  (%). Predicted

 (%)

Sample

Real

Blends without ethanol P1 P2 P3 P7 P8 P9 P13

85 85 85 77 70 55 62

88 91 86 79 75 54 62

+3.5 +7.1 +1.2 +2.6 +7.1 −1.8 +0.0

Blends containing ethanol P4 95 P5 85 P6 70 P10 55 P11 50 P12 30 P14 75

100 86 80 57 68 30 79

+5.3 +1.2 +14 +3.6 +36 +0.0 +5.3

Table 3,which compares predicted and real concentrations of gasoline on test set samples, allows the assessment of the accuracy and suitability of this GC × GC–FID + N-PLS method to quantify gasoline on its blends with adulterants. The samples included in this table were divided in two subsets: (1) mixtures containing only solvents used on the calibration set and (2) mixtures containing these solvents plus ethanol (a frequent adulterant intentionally excluded from the calibration step). The accuracy was evaluated through the root mean square error of prediction (RMSEP). The overall RMSEP for the test set was 6.2% (v/v). For the individual subsets, RMSEP were 3.3% (v/v) (without ethanol) and 8.2% (v/v) (with ethanol). For the first subset, the accuracy can be regarded as adequate – especially considering that the target analyte is not a single species or a mixture of few substances, but a complex mixture itself where even a definition of “purity” is not straightforward. Also, inspection of the chromatograms on Fig. 1 indicates that two of the adulterants in the calibration set – kerosene and white spirit – share a large number of components with gasoline, and even with this similarity it was possible to obtain low RMSEP. As expectable, RMSEP for the second subset is considerably higher. A closer examination of the results indicates that all gasoline concentrations are overestimated, with larger errors for samples containing only ethanol (P4, P5, P6 and P11). In particular, those with larger contents of adulterant (P6: 30% (v/v) ethanol and  = +14%; P11: 50% (v/v) ethanol and  = +36%) produced large errors. However, this may be deemed as not important for assessment of fuel purity: the overestimation on gasoline concentrations will be less than the additional concentration of ethanol on these highly adulterated samples, which therefore will be detected as adulterated. Finally, the GC × GC–FID + N-PLS was applied to gasoline samples of Type C gasoline collected from local gas stations during routine monitoring of fuel quality and previously examined by CAIQ/Unicamp contract laboratory using ANP standard procedures (Table 3), to check the equivalence of the proposed approach and the official procedure. For eight samples flagged as adulterated (A1–A8) by CA-IQ/Unicamp, the content on Type C gasoline measured using GC × GC–FID and N-PLS calibration ranged from 48% (A2) to 85% (v/v) (A8). As % (v/v) gasoline obtained were significantly less than 100% it is a strong indication of the validity of the former approach. Visual examination of the corresponding chromatograms pointed out that for most of these samples the adulterant was ethanol. As for the five “non-adulterated” samples, three had Type C gasoline concentrations larger than 95% (v/v). Considering the RMSEP and

Fig. 2. GC × GC–FID chromatograms for typical non-adulterated Type C gasoline (a), test sample N1 (b) and test sample N2 (c). Data for 1 tR > 30.0 min not shown.

RMSECV, these can be considered as non-adulterated, which is in concordance with the official results. For samples N1 and N2, the concentration on Type C gasoline was 44% and 80% (v/v) gasoline, respectively. This apparent inconsistency does not challenge the reliability of the GC × GC–FID approach, since the ANP protocol specifies that only part of the samples approved on preliminary evaluation by ASTM methods (distillation range, color and density) will be analyzed by GC. Previous studies found even Type C gasoline adulterated with up to 50% (v/v) of common adulterants will not be detected by these ASTM tests, according to ANP criteria [30]. A closer visual inspection of the chromatograms for these samples (Fig. 2) can be helpful to further evaluate these results. The GC × GC–FID profile for sample N1 (44% (v/v) gasoline) is clearly distinct from that of unadulterated gasoline: the peaks on the band corresponding to the saturated and cyclic alkanes (1 tR from ∼2.5 min to ∼30 min, 2 tR from ∼1.0 s to ∼1.5 s) are much less intense compared to model Type C gasoline, as opposite to peaks on the structural groups attributed to C5 - and C6 -monoaromatics (1 tR from ∼22 min to ∼30 min, 2 tR from ∼2.2 s to ∼3.2 s) as well as methylnaphthalenes (1 tR = ∼30 min, 2 tR = 5.0 s) and naphthalene (single peak at 1 tR = 24.5 min, 2 tR = 5.4 s). Therefore, sample N1 is clearly different from non-adulterated gasoline; the same does not happen

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181

Fig. 3. Expanded view (17.5 min ≤ 1 tR ≤ 25.0 min, 155 ms ≤ 1 tR ≤ 350 ms) of GC × GC–FID chromatograms for non-adulterated Type C gasoline (a) and test sample N2 (b).

for sample N2. However, a closer inspection shows some remarkable differences not present in chromatograms for other samples. The peaks corresponding to saturated and cyclic alkanes in sample N2 chromatogram are less intense as compared to non-adulterated samples (especially for those in the range 10 min ≤ 1 tR ≤ 20 min). Also, several peaks that are not present in model gasoline or in any other sample are present. An example is shown on Fig. 3, which compares a zoomed section of chromatograms for nonadulterated gasoline and for the test sample N2. For this sample there is an intense peak at 1 tR = 19.57 min and 2 tR = 2.23 s which does not appear in model gasoline, as well as several other smaller peaks such as a pair of unresolved species at 1 tR = 18.00 min and 2 t = 2.25 s. Therefore, it is highly possible that sample N2 was R also adulterated. However, the validity of this supposition, made through simple visual inspection of chromatograms, depends on samples N1 and N2 not being outliers (for example, due to their content surpassing the limits anticipated in the initial model). This is necessary since N-PLS does not present second-order properties. The testing for outliers in the calibration sample set, synthetic prediction sample set (Tables 2 and 3) and ANP prediction sample set (Table 4) were performed by calculating the corresponding T2 and Q values [31]. Fig. 4 shows these values along with the 95% confidence limit. No sample in all sets, including N1 and N2, can be considered as outliers since their T2 and Q are below the confidence limit – which confirms the considerations above. Finally, the ANP testing results are presented as “non-adulterated” or “adulterated”, when the method here proposed gives a numeric value related to the concentration of Type C gasoline measured on the samples.

An accurate verification of equivalence of the results provided by these methods would demand a larger data set, as well as more sophisticated statistical treatments (not available at the moment). However, a preliminary estimate of the correspondence between ANP and GC × GC–FID methods, in addition to the probabilities of false positive or negative results from the later can be made by examining Table 4. All samples with %Type C gasoline under 80% (v/v), according to GC × GC–FID + N-PLS, were pointed as adulterated by ANP tests; furthermore, the samples with %Type C gasoline of at least 95% (v/v) were classified by ANP as non-adulterated. Therefore, for %Type C gasoline less than 80% or over 95%, the chance of a false positive or a false negative can be considered as negligible – except for sample N1 (where problems related to the conservation of the sample specimen are more probable). Samples in the range 80% ≤ % Type C gasoline <95% were detected either as adulterated or non-adulterated; therefore, within this concentration range – and taking into account only this sample set – there would be a considerable possibility of either false positive or negative results. It must be mentioned that these considerations are preliminary, and based on a very limited sample set. A more accurate determination of the values of %Type C Gasoline defining the status of the sample (adulterated or non-adulterated) with a definite degree of confidence would demand application of the GC × GC–FID + N-PLS method to a larger set of samples.

Table 4 Concentrations of Type C gasoline (%, v/v) found for non-synthetic samples collected and inspected by ANP Sample

%G

Adulterated according to ANP A1 A2 A3 A4 A5 A6 A7 A8

66 48 55 64 81 74 82 85

Non-adulterated according to ANP N1 N2 N3 N4 N5

44 80 95 97 98

Fig. 4. T2 and Q values for all gasoline samples. (—) 95% confidence limit; () calibration samples; (+) test samples; () adulterated samples by ANP; () non-adulterated samples by ANP.

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4. Conclusions The results show that the combination of GC × GC–FID and multivariate calibration can be employed as a general tool to assess adulteration of gasoline, using the whole raw chromatograms as input data (without need of noise filtering or peak alignment). The procedure was successfully tested with lab-made blends of pure Type C gasoline and model adulterants, as well as with nonsynthetic samples collected from local gas stations and previously evaluated using the officially adopted procedure. The tests with synthetic blends pointed out that the content in Type C gasoline can be determined with good accuracy: for samples spiked with the same solvents used to build the model, the RMSEP was 3.3% (v/v). Even for gasoline blends prepared using ethanol only (a solvent not used on the calibration, as well as a component of the “analyte” Type C gasoline itself), the results can be regarded as acceptable: the RMSEP was 8.2% (v/v). For all samples in this subset, the magnitude of the error was not enough to lead to misidentification of the sample purity. As for the equivalence of this GC × GC–FID + N-PLS method and the sanctioned procedures for fuel quality monitoring, the results were also conclusive. For all non-synthetic samples labeled as adulterated according to the official method, the content on Type C gasoline varied from 48% to 80% (v/v); these values can be unequivocally assigned as result of adulteration of the sample. For two of the five non-adulterated samples, the concentration found was in this same range. This does not necessarily invalidate the GC × GC–FID + N-PLS approach, since the ANP criteria for pre-screening of adulteration through physico-chemical tests was already reported to be excessively permissive. The results suggest that GC × GC–FID combined to multi-way calibration techniques here employed, can be a powerful tool to solve similar analytical problems where complex mixtures should be treated as a single analyte. Finally, this method can be directly applied to several other relevant fuel-related problems, such as the quantification of biodiesel on its blends with mineral diesel – which we are currently studying with good preliminary results.

Unicamp Central Analytical Laboratory, for kindly supplying the gasoline samples. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

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Acknowledgements

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This work was funded by FAPESP (Foundation for Research Support of the State of S˜ao Paulo). M.P.P., L.A.F. de G. and E.C.F. thank CNPq (Brazilian National Council for Research and Technological Development) and CAPES (Brazilian Ministry of Education Agency for Improvement of Graduate Personnel) for scholarships. We also thank Edmilson Raldenes and Msc. Jose´ Roberto Riston (CPT/BSB/ANP) and Dr. Daniela Prates, technical manager of IQ-

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