Petrofuels Quality Control and Origin Determination

Transcription

1 Petrofuels Quality Control and Origin Determination Rodrigo Sequinel, Rafael Rodrigues Hatanaka Institute of Chemistry, CEMPEQC UNESP São Paulo State University, Brazil Danilo Luiz Flumignan IFSP São Paulo Federal Institute of Education, Science and Technology, Brazil José Eduardo de Oliveira Institute of Chemistry, CEMPEQC UNESP São Paulo State University, Brazil

2 1 Petrofuel Quality The chemical composition of any fuel fraction obtained from petroleum industry depends on both the nature of the crude oil from which the products are derived and the refining process they have undergone (Flumignan et al., 2008; Rebouças et al., 2003). At the end of the distillation or refining processes each fuel fraction is characterized by a group of some constituents with narrow boiling ranges and different proprieties (Barman et al., 2001; Gary, 2003; Lois et al., 2003). The petrofuels most used for transportation of cargo and passengers in the world are diesel and gasoline. Three of the major markets that currently have active initiatives for the economy of fuels, United States, Brazil and European Union, have established restricted physicochemical parameters for the quality control of any fuels commercialized in their countries. All these physicochemical parameters must be evaluated by analytical protocols covered by national and international guidelines. In Brazil the quality of any commercial fuel must comply with the guiding principles of the Brazilian Government Petroleum, Natural Gas and Biofuels Agency (in Portuguese, Agência Nacional do Petróleo, Gás Natural e Biocombustíveis (ANP)). In United States the specifications are formerly supported by American Society for Testing and Materials (ASTM) whereas the European Committee for Standardization provides some European Standards (EN) for the technical specifications of commercial fuels in the European Union. Each regulation contains a comprehensive range of tests needed to evaluate and recognize the compliance of a given fuel. In Brazil, for example, the ANP resolution 57 (in force since 10/20/ before that the quality parameters were regulated by ANP resolution 309) establishes 18 different physicochemical parameters to determine the quality of commercial gasoline (Agência Nacional do Petróleo, Gás Natural e Biocombustíveis (ANP), 2011; Flumignan et al., 2008). As shown in Table 1, each parameter is evaluated by analytical protocols covered by international guides, mainly from the American Society for Testing and Materials (ASTM International) or from Brazilian standards called Normas Brasileiras (ABNT - NBR). In this case, to cover all the experiments recommended by the regulation it is necessary a large amount of equipment and a large volume of samples. Notwithstanding, some of listed methods often involve manual operations, which are rather specific, time consuming and prone to operational errors. Besides that, even the determination of all physicochemical parameters may be not efficient for detecting an adulteration since the composition of some adulterant solvents are very similar to fuels (Flumignan et al., 2008; Kaiser et al., 2010; Monteiro et al., 2009). Because of this gap, several studies have demonstrated the feasibility of chemometric methods as an important tool applied to the fast detection of fuel adulteration. Pattern-recognition methods provide definitions of similarity or dissimilarity between diverse groups of data, revealing common proprieties among the objects in a given data set. The technique constitutes a true paradigm based on an empirical relationship that is created for a set of samples from which the main physicochemical parameters are known. This empirical relationship or classification rule may be able to predict some property of samples that are not part of the original database. Spectroscopic or chromatographic techniques are used to supplement the diagnosis (Brereton, 2003; Lavine & Davidson, 2006). This chapter will discuss the use of spectroscopic and chromatographic methods coupled with pattern recognition multivariate algorithms as simple, fast and alternatives tools to be applied in determination of fuel compliance to technical specifications (Flumignan et al., 2010) and origin authentication purposes (Li & Dai, 2012; Rigo et al., 2009).

3 Parameters Unit Common Gasolinline Premium Gaso- Test Method Type Type Type A Type A ABNT NBR ASTM C C COMPOSITION Methanol content (max.) (1) % vol Chromatography Ethanol content % vol. 1 (2) 1 (max.) (1) (max.) (1) (2) Sulfur mg/kg D1266/D2622/D3120 D4294/D5453 Lead (max.) (1) g/l D3237 Benzene (max.) % vol D3606/D5443/D6277 Aromatic hydrocarbons (max.) % vol Olefinic hydrocarbons (max.) % vol D1319 Additives (3) PERFORMANCE Motor octane number (min) (4) 82.0 D2700 Antiknock index (min.) (5) (4) (4) D2699/D2700 COLOR AND APPEARANCE (6) (7) (6) (7) Color (8) Appearance D4176 VOLATILITY Distillation: 10% volume C % volume C % volume C D86 final boiling C point (max.) residue (max.) % vol. 2.0 Vapor pressure at D4953/D5190/D C (9) C 14149/ (max.) 62.0 (max.) D5482/D6378 OTHERS Density at 20 C Kg/m 3 report 7148/14065 D1298/D4052 Solvent-washed mg/100 gum content (max.) ml D381 Oxidation stability (10) (10) 360 at 100 C (min.) D525 Corrosiveness to copper at 50 C/3h (max.) D130 (1) Addition is prohibited by law. Shall be measured when contamination is suspected. (2) The composition and concentration of anhydrous ethanol added to produce Gasoline Type C are specifically limited in other regulatory document.

4 (3) Addition is allowed according to federal legislation and regulation. Additives containing heavy metals are prohibited. (4) Tests shall be carried out only after the addition of anhydrous ethanol at a concentration level of one percentage point below the target value established by law in the moment the commercial gasoline A was produced. (5) Antiknock index shall be reported by arithmetic media from values obtained to the determination of MON and RON. (6) Colorless to yellow, without colorant. (7) Colorless to orange, without colorant. Colorant is allowed until 50 ppm, with an exception to blue dye used only to aviation gasoline. (8) Shall be limpid and free from visual impurities. (9) Maximum vapor pressure value shall be adjusted in accordance with specific climate conditions of some States of Brazil. (10) Tests shall be carried out only after the addition of anhydrous ethanol at a concentration level of one percentage point above the target value established by law in the moment the commercial gasoline A was produced. * For complete information see entire document (ANP, 2011). Table 1: Technical specifications for commercial gasoline in Brazil. 2 Introduction The recent suspension by the Brazilian government of the state monopoly of fuel production and distribution has given rise to significant changes in the fuel market in Brazil (Barbeira, 2002). Liberalization has opened up enormous opportunities both for established oil companies and for new entrants in this market. Moreover, the removal of barriers in the retail sector by allowing white flag service stations (i.e. those not operating under the trademark of a particular distributor) and by liberalizing profit margins, has produced a significant increase in the number of fuel dealers and gas stations operated by national and foreign companies (Flumignan, 2006). The ensuing stronger competition for retail business has lead to a substantial increase in the variation of the price of fuel, whilst the quality of the product has not necessarily been maintained (Barbeira, 2002). In this respect, one of the most common adulteration practices involves the dilution of gasoline with a solvent that presents a much lower tax rate. This type of adulteration causes environmental pollution, poor engine performance and, of course, a loss of tax revenue (Kalligeros et al., 2003; Flumignan 2006). In an attempt to overcome this problem, the Brazilian government has recently developed and implemented a program that involves the addition of tracers to solvents thus enabling their easy identification in gasoline (ANP, 2001a; Tanaka et al., 2011). This is a high cost strategy and only few laboratories in the country are authorized by ANP to analyze gasoline for the presence of tracers. Hence, the development of alternative and cheaper analytical methods to identify solvent adulteration in gasoline remains a focus of continuing academic and forensic research (Flumignan et al., 2007; Kalligeros et al., 2003; Wiedemann et al., 2005). Brazilian C gasoline is a complex mixture of varying proportions of C4 C12 hydrocarbons, with different chemical structures and functional groups, to which 25±1% (v/v) of combustible anhydrous ethanol (CAE) has been added other ratios may be used according to national regulations (Ministério da Agricultura Pecuária e Abastecimento (MAPA), 2002; Petrobrás, 2000a).

5 The correct performance of gasoline depends on a balanced combination of their various components. Thus, the commercial production of gasoline follows a rigid quality control process, monitored by the ANP, to ensure that the physicochemical characteristics of the product are compliant with ANP regulation 309 (ANP, 2001b). However, the solvents used in the adulteration of gasoline are themselves petrochemical derivatives and their detection is a challenging task. Moreover, the ANP regulations were not specifically created to identify illegal solvent additions and are not particularly efficient in detecting adulterated gasoline (Flumignan, 2006; Moreira et al., 2003; Skrobot et al., 2005). Gas chromatography (GC), coupled with various detection methods, has been widely used to evaluate the quality of gasoline (Blomberg et al., 2002; Moreira et al., 2003; Philp & Mansuy, 1997; Skrobot et al., 2005; Sojak et al., 2004). Some of these studies have employed GC with flame ionization (FID) or mass spectral (MS) detection in order to determine how the chromatographic profile of the gasoline is modified by the presence of specific adulterating solvents (Moreira et al., 2003; Skrobot et al., 2005). Although a far more detailed profile of a gasoline may be obtained using GC MS, the richness of the data produced complicates considerably the evaluation of gasoline quality. The development of methods for the analysis of gasoline must take into account both the natural variations of composition that can occur and the ease of assessment of fuel quality from the data produced. A number of studies have demonstrated the utility of chemometric methods applied to the analysis of gasoline and to the detection of adulteration. Tan et al. (Tan et al., 2000) employed principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) to create classification models to identify petroleum-based accelerants in fire debris. The K-nearest neighbor (KNN) technique was applied to nuclear magnetic resonance data by Kowalski and Bender (Kowalski & Bender, 1972), in order to classify substances according to subtle differences in structural groups. The origin of three different grades of gasoline samples was established from chromatographic data by Sandercock and Pasquier (Sandercock & Du Pasquier, 2003) who employed both PCA and linear discriminant analysis (LDA). de Oliveira et al. (de Oliveira et al., 2004) gathered data, from gasoline distillation tests, in order to create a SIMCA model that was able to detect samples that did not meet Brazilian specifications. Skrobot et al. (Skrobot et al., 2005) produced a set of adulterated gasoline samples for GC MS analysis by mixing pure gasoline with various amounts of four complex solvents. In this study, hierarchical cluster analysis (HCA) was employed to search for sample distribution patterns according to the solvents added, whilst KNN was used to create a classification scheme to differentiate pure and mixed samples and to indicate the type of solvent present. 3 Concepts, Found In The International Literature, On Chemometric Methods Frequently Used Petrofuel Analysis Pattern recognition is one of the chemometric methods most used in analytical chemistry and this is true for separation data. Pattern recognition can be generally divided into two classes: exploratory data analysis and unsupervised and supervised pattern recognition. Exploratory data analysis aims to extract important information, detect outliers and identify relationships between samples and its use is recommended prior to the application of other chemometric techniques (Brereton, 2003). In supervised methods a set of data describing samples of known characteristics is used to construct models that are then used to classify, unknown samples into a priori constructed groups. Unsuper-

6 vised pattern recognition techniques uncover patterns within a data set without a priori class assignment of samples. Here, the objective is to find patterns in the data which allow grouping of similar samples. When supervised pattern recognition is used, the classes of samples in a training set are known and used to calibrate a model, which is then used to predict class assignments of unknown samples (Ferreira et al., 1999). 3.1 Hierarchical Cluster Analysis (HCA) HCA, is a method of pattern recognition that attempts to identify relatively homogeneous groups of cases (or variables) based on selected characteristics. In analytical chemistry, when there are a great number of samples, in order to minimize costs and time of analysis, they can be clustered by applying HCA to reduce the number of samples in the whole dataset. (Sharma, 1996). It starts with each sample in a separate cluster, and then, the clusters are combined sequentially, reducing their number at each step until only one cluster is left. Therefore, when there are N cases, this involves N 1 clustering steps. Two criteria must be chosen to perform HCA, the distance measurement between samples or groups and the criterion to link samples and groups. Among the most popular distance measurements are Euclidean, which corresponds to the geometric distance between two objects in multidimensional space. The linkage criteria state how close the groups are. This information is used to decide how each pair of a group will be joined in each step of a formation of the dendrograms. The criteria most commonly used are single, complete and centroid. In single linkage, the dissimilarity between two clusters is the minimum dissimilarity between the objects in the clusters; while for complete linkage, the maximum dissimilarity is considered, and in the centroid method, clusters are merged based on the minimal squared Euclidean distance between their centroids (Van Gyseghem et al., 2006). Between the most used centroidal linkage methods we found centroid, median, flexible, group average and incremental; all attempt to find a central region of a cluster from which to construct distances; they differ in the way that the cluster center is computed. In the incremental criterion, the groups are linked in such way that each step of joining causes a minimum loss of information, employing the sum of square approach in calculating inter-cluster distances. The incremental link works better than other methods in instances where two groups of samples differ only slightly (Infometrix, 2003). 3.2 Principal Component Analysis (PCA) PCA is a chemometric pattern recognition method used to achieve a reduction of dimension whilst retaining the maximum amount of variability present in the data, and also to a!ord a primary evaluation of the between-class similarity. The sample may be described by a number of variables. Geometrically, this corresponds to a point in a multidimensional space where the variables define the axes. If many objects share this space, correlations between variables may be observed. This method produces a new set of variables, the principal components (PC), as a linear combination of the original variables. The contributions of individual variables to the component are expressed as loadings. Sample points are projected along the component, and these positions make up a vector of samples scores. For each component, a loadings vector and a scores vector are obtained. These PC provide a simple representation of the data in the form of a 2D diagram from which patterns hidden in the dataset may be elucidated by visual inspection (Meloun & Forina, 1992).

7 3.3 Soft Independent Modeling Of Class Analogy (SIMCA) SIMCA is a multivariate supervised pattern recognition method that describes different classes of samples based on classification rules defined by the values of distinct measurements provided for a set of samples belonging to known, but different, classes (the training set) (Mellinger, 1987). In other words, SIMCA is a method of pattern recognition based on the principle of analogy between samples belonging to the same class, and is used for calculation of distance scores determined by Principal Component Analysis (PCA). The SIMCA method calculates a PCA model for each class or category in the system under study and then integrates each of the classes and calculates its frontiers or boundaries with a given probability, typically 95% (Morales et al., 2008). These rules are, then, used to classify external samples (the prediction set) on the basis of the same m measurements presented for each of the new samples. The number of samples correctly classified by the model is a measure of the quality of the criteria employed. If appropriate, the model can then be used to classify unknown samples according to the same rules (De Maesschalck et al., 1999; Derde & Massart, 1986; Sjostrom & Kowalski, 1979; Wold et al., 1987). 3.4 Linear Discriminant Analysis (LDA) Linear Discriminant Analysis (LDA) is a supervised parametric method of multivariate classification. It is assumed that the distribution of the samples in the classes is Gaussian. It is also a linear method. LDA, classifies the samples by establishing a linear discriminant function, based on the original variables, which separates the classes. LDA seeks a linear combination or function, D, of the independent variables that maximizes the between-class variance relative to the within-class variance. The obtained latent variable is called a canonical variate. For k classes, k 1 canonical variates can be calculated. Each time, LDA will select a direction leading to a maximum discrimination between the given classes. Some disadvantages of LDA are pointed: LDA only perform well when all classes are strictly homogeneous, LDA cannot be used if the number of variables (n) is higher than the total number of samples (m). If a PCA is run prior to LDA, reducing the number of variables, this problem can be overcome. Stepwise regression can also be used to select the most important variables from the n subject descriptors and the response vector y. The LDA and classification model indicate specific subject descriptors responsible for the classification (Dejaegher et al., 2011). 3.5 PLS-DA (Partial Least Square Discriminant Analysis) PLS (Partial Least Squares) regression is an unsupervised chemometric projection method for relating two blocks of variables, X and Y, to each other via a linear multivariate model. PLS is a linear method in the sense that the new components are linear combinations of the original variables It has many advantages over more traditional regression methods because it can handle, among other things: noise in both blocks of variables, missing data, co-linearity among variables and more variables than samples. In practice, the predictive ability of a PLS model typically increases with the number of X-variables because of the intuitive observation that many variables tend to carry more information than few. Analyzing the entire dataset also tends to result in more robust models with the added advantage that no data are thrown away. PLS has become the standard method for multivariate calibration in spectroscopy because it readily handles the large number of correlated variables inherent with such methods.

8 PLS DA is a variant of standard PLS regression in which the block of Y-variables consists of a set of binary indicator variables (one for each class) denoting class membership. A sample has a 1 if it is a member of a given class and 0 otherwise. The objective of PLS DA, therefore, is to find directions in the X-space that separate the classes based on a training set of samples with known class membership (Whelehan et al., 2006). 3.6 Successive Projections Algorithm (SPA) The successive projections algorithm (SPA) is appropriate to select variables with minimum multicollinearity and maximum information. The procedure for classification includes two steps. In the first step the variables are projected onto a subspace orthogonal to one variable (the reference), which is selected for each iteration. In the second step, the best subset of variables is selected in order to minimize a cost function associated with the average risk (Pontes et al., 2011). 4 Applications 4.1 Screening The Quality Of Gasoline By Gas Chromatography And Pattern Recognition In the first reported application of chemometric techniques to chromatographic data derived from commercial gasoline samples, obtained directly from gas stations, for quality purposes, Flumignan et al. (2007) studied the influence of the quality of gasoline on their chromatographic profile by applying a discriminatory SIMCA model classification method, associated with a sample discriminatory process (screening). The main goal was to build a model that would allow the detection of gasoline that was not in accordance with Brazilian regulation ANP 309 (that establishes the quality parameters for Brazilian gasoline). The authors concluded that the chromatographic profiles associated with the physicochemical parameters were satisfactory for screening Brazilian C gasoline quality. Commercial gasoline samples were collected from gas stations in the Sao Paulo state, Brazil, and analyzed using the standard methods established by ANP regulation 309 (Associação Brasileira de Normas Técnicas (ABNT), 1997; ASTM Internacional (ASTM), 1996, 2000, 2001a), motor octane number (MON), research octane number (RON) and anti-knock index (AI) (correlation to ASTM D2699/D2700) (ASTM, 2001d, 2001e), percentages - v/v - of hydrocarbons (olefins, aromatics and saturated correlation to ASTM D1319 (ASTM, 2001c)) and CAE (content of anhydrous ethanol) (ASTM D5845 (ASTM, 2001b)) were determined by infrared spectroscopy), resulting in 16 physicochemical parameters. These parameters were submitted to an HCA selection process in order to reduce the original number of collected samples (2400) to a representative set of 150 samples. HCA was applied to each monthly, 400 samples, spreadsheet of results (16 variables 400 samples). The independent variables were autoscaled, which means that each variable was mean centered and its variance set to 1, and logarithmic transformed in order to select those samples that presented minimal similarity. The similarity line, which establishes clusters, presented values between 0.5 and 0.6. The six monthly dendrograms, each constituted of 400 samples, resulted in 25 clusters. Each month, the 25 samples that exhibited least similarity, on the basis of Euclidean distance measurements between samples or groups and incremental linkages between samples or groups, were selected for GC-FID analysis. The selection by HCA reduced the number of samples minimizing the total time and cost of the chromatographic analyses, maintaining the representativity of the data sets. Seventy one (47%) of the

9 selected samples were not in compliance to ANP regulation 309; 35 of which failed in only one of the physicochemical parameters. The most common causes of non-compliance were related to the values of MON (47 samples; 66% of the non-compliant set), CAE content (31 samples; 43%) and AI (23 samples; 33%). Then, the selected samples were analyzed by GC-FID (Flumignan et al., 2008) that resulted in complex chromatograms (more than 400 peaks) typical of compliant and non compliant gasoline, from which could be seen that many of the compounds present in the adulterating solvents are also present in gasoline. Visual inspection of profiles was not efficient in identifying the presence of adulterant solvents in the samples analyzed. Any attempt to distinguish between gasoline samples must consider numerous peaks, so the chromatographic profiles were mean centered scaled pre-processed, later the data were normalized and then logarithmic transformed and submitted to multivariate classification technique by means of the SIMCA method (Mellinger, 1987) in order to build a screening model. Only well-separated peaks that could be detected in all of the gasoline samples were considered, since the chemometric tools employed required that the matrices under study should be complete, that is, all samples should have values for all variables. The experimental data were analyzed as a data matrix, representing 458 variables (the integrated chromatographic peak areas) and 150 samples selected by HCA. The calculations were performed on the complete dataset using chemometric software. To apply SIMCA pattern recognition model the chromatographic profile data of 150 gasoline samples (each of which had been evaluated using 12 of the official ANP physicochemical parameters) were employed, from which 100 (comprising 54 compliant and 46 non-compliant to ANP regulation 309), the training set, and the remaining 50 samples (comprising 25 compliant and 25 non- compliant to ANP regulation 309), the prediction set. The samples present in the training and prediction sets were those selected from the exploratory analysis HCA of their chromatographic profiles. The SIMCA method was selected since it allows the classification of an unknown sample on the basis of rules defined by a training set. A SIMCA model was developed based on the chromatographic information and using a mean center scaled data pre-processing. The selection of the prediction set was performed through exploratory analysis (HCA) of 150 samples. The categories (compliant and non compliant) were defined through 12 physicochemical parameters established by ANP regulation 309. In the training set, six principal components accounted for 70% of the total within-set variance. The highest percentage of samples correctly classified in the training set was obtained using a mean center scaled data pre-processing. Later the data were normalized (0 100) and then logarithmic transformed. This form of data pre-processing gave very good segregation in these samples and, indeed, was the best that could be obtained. All of the samples in the training set were clearly classified. Four samples of the training set that had been previously classified as non compliant to ANP specifications (to one or more of the 16 official ANP physicochemical parameters) were identified by SIMCA as compliant. Hence, 96% of the samples in the training set were correctly classified by application of the SIMCA method. To the four samples misclassified by SIMCA only 1 of 16 evaluated parameters, namely, that relating to the percentage of CAE, was not within ANP specifications. On the basis of these findings, the authors concluded that the modeling power of the training set was satisfactory. The SIMCA model was then applied to the 50 gasoline samples of the prediction set. Whilst the samples were clearly well segregated, eight of them were apparently misclassified according to the SIM- CA model: seven non compliant samples were classified as compliant to ANP regulations and one sam-

10 ple that are compliant to ANP specifications was classified as non-compliant by SIMCA. The noncompliant samples were not in accordance with ANP specifications with respect of either the percentage of CAE or the values of MON, AI or FBP (final boiling point). However, in most cases the physicochemical results obtained were not conclusive, since the measured values were very near to the limits established by ANP regulations and were certainly within the confidence limits considering the accuracy of the used methods. None of these measured parameters can be considered significantly different from the limiting values when the confidence limits associated with the assays are taken into account. Hence, the authors concluded that the 50 samples in the prediction set were well discriminated by the applied model, and that 94% of them were correctly classified according to ANP regulations. In comparison with previous studies involving gasoline samples adulterated in the laboratory by addition of petrochemical solvents (21; 25; 26; 34), the model described herein can be considered perfectly acceptable in its ability to classify commercial gasoline samples obtained directly from gas stations. Following the publication in Analytica Chimica Acta Flumignan et al. (2007) analyzed a set of Brazilian commercial gasoline representative samples from São Paulo State, selected by HCA, plus six samples obtained directly from Brazilian refineries (Revap, Recap, Replan, RPBC, Reduc and Manguinhos). The samples were analyzed by a high-sensitive gas chromatographic (GC) method ASTM D6733 and the identification of the saturated hydrocarbons (paraffins, iso-paraffins and naphthenics) was made by comparison of their retention times with those established in the ASTM protocol. The concentration of CAE was determined from a calibration curve constructed using standard gasoline samples containing five different concentrations from 20 to 30% v/v of absolute ethanol, under ASTM D6733 conditions. The levels of saturated hydrocarbons and anhydrous ethanol obtained by GC were correlated with the quality obtained from ANP specifications through exploratory analysis (HCA and PCA) using 80 variables, representing the percentage of the identified saturated hydrocarbons, and the 31 gasoline samples (13 compliant and 18 non-compliant; Table 2). In the resulting dendrogram (Figure1), based on similarities in their saturated hydrocarbon compositions (as determined from the chromatographic profiles), samples were separated into four distinct groups (a, b, c and d; similarity index 0.487). Group a consisted of the standard gasoline samples obtained directly from southeastern Brazilian refineries; group b contained type C gasoline samples compliant with the ANP specifications, together with the non-compliant samples 8, 15, 16 and 22; groups c and d comprised the non-compliant gasoline samples in which various physicochemical parameters were outside the required specifications. In the three cases of non-compliant samples classified in group b (8, 16 and 22), only the MON parameters were outside the ANP limits. However, since the MON values of these samples (81.6, 81.7 and 81.7, respectively) were very close to the accepted minimum level (82.0) (ANP, 2001b), and certainly within the confidence limits considering the accuracy of the infrared method used (0.5 for MON) (Grabner Instruments, 2005), these samples were considered to be correctly classified according to their GC profiles. In contrast, sample 15 was also classified within group b although it failed to attain the levels required with respect to five of the ANP parameters, namely, T10 (in degrees Celsius is the distillation temperature where 10%, of the gasoline is distillated), FBP (final boiling point of the distillation), MON, AI and CAE. This sample had been diluted with large quantities of CAE and such adulteration is readily detected by GC and does not demand statistical analysis.

11 Sample No. Classification a Saturated hydrocarbons (%) CAE (%) Sample No. Classification a Saturated hydrocarbons (%) 8 NC NC Reduc C NC NC NC Replan C NC C NC NC NC C NC Mang. 21 C NC C NC Recap C NC C NC RPBC 13 C NC C NC C NC C NC Revap C CAE (%) a ANP-compliant (C); ANP-non-compliant (NC) Table 2. Quantification of saturated hydrocarbons and CAE by CG-FID using the D6733 method and their classification according to the ANP specifications. A large variability in the gasoline samples was detected by PCA, which results are shown in Figure 2 Such variability is explained by PC factors 1 3 (approximately 70% PC1, 20% PC2 and 5% PC3), which separate the gasoline samples according to the chromatographic profiles of the saturated hydrocarbons. In the PC1 versus PC2 plot (Figure 2A), it is possible to observe a tendency towards the formation of clusters, although some samples overlap slightly. Samples containing levels of saturated hydrocarbons within the range % (clear points in Figure 2) exhibit high PC1 and PC2 scores. It is not possible to establish any relationship between samples in the PC1 versus PC3 plot (Figure 2B) since the points overlap considerably. However, the formation of distinct clusters are clearly discernible in the PC2 versus PC3 plot (Figure 2C) in which gasoline samples containing saturated hydrocarbons within the range % (clear points) present positive scores, whilst those exhibiting levels below or above this range (dark points) have negative scores. According to the scores presented in Figure 2C, samples 15 and 22 would be classified as ANP non-compliant (as confirmed by ANP physicochemical parameters) although the hydrocarbon compositions of both were within accepted limits (isolated clear points). In contrast, sample 8, in which the saturated hydrocarbons were within the range of %, was classified as a compliant gasoline although the physicochemical parameters did not support this finding.

12 Figure 1. The partial dendrogram constructed following the chemometric analysis of 80 gas chromatographic peaks of 31 gasoline samples of different provenances. Note: Samples labeled Manguinhos, RPBC, Replan, Recap, Revap and Reduc were obtained directly from the respective Brazilian refineries, C represents an ANP compliant commercial gasoline, and NC an ANP non-compliant commercial gasoline. The figure was obtained from Pirouette The loadings of the variables associated with PC 1 3 scores are presented in 2D to 2F plots. The loading on PC1 (Figure 2D and 2E) was driven mainly by the n-paraffins, with n-pentane (nc5) and n- hexane (nc6) contributing to positive loadings, typical of gasoline containing saturated hydrocarbons levels within the range %, and n-propane (nc3) contributing to negative loading, typical of gasoline containing saturated hydrocarbons levels below or above this range. The positive loading on PC2 (Figure 2D and 2F) was attributed to di- and tri-substituted iso-paraffins from 7 to 9 carbon atoms 2,2-dimethylhexane (22DMC6) and 2,2,3-trimethylbutane (223TMC4) and to n-paraffins (nc12 and nc13), typical of gasoline containing saturated hydrocarbons levels within the range %. The negative loading on PC2 (Figure 2D and 2F) was driven by di- and tri-substituted iso-paraffins containing 8 to 9 carbon atoms 2,4,4-trimethylhexane (244TMC6) and 2,3,4-trimethylpentane (234TMC5) and by n-paraffins (nc3, nc4, nc5, nc6 and nc7), typical of gasoline containing saturated hydrocarbons levels below or above that range. All of these characteristics are visible in PC3 (Figure 2E and 2F) in which the clusters are clearly discernible.

13 Figure 2. Scores (A to C plots) and loadings (D to F plots) of the first three principal component factors. Note: Clear points represent gasoline samples containing concentrations of saturated hydrocarbons ranging between 34.6 and 46.6%. Dark points represent gasoline samples containing concentrations of saturated hydrocarbons above 46.6% or below 34.6%. The figure was obtained from Pirouette 3.11.

14 The correlation presented above showed that the GC method, together with HCA and PCA, could be employed as a screening technique to determine compliance with the prescribed legal standards of Brazilian gasoline. Likewise,Gomes et al. (2009) combined ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis to achieve an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method was recommended for rapid quality-control monitoring of gasoline samples. In other paper, Hatanaka et al. (2009) employed chromatograms obtained from optimized ASTM D6729 method, analyzed by SIMCA, to predict the quality of Brazilian commercial gasoline representative samples. To build the classification model, 125 samples were monthly selected from an universe of 1450 by HCA (Figure 3) using the values of the physicochemical parameters established by Brazilian regulation ANP 309 as variables. Fingerprints of these 125 samples were obtained using an automated chromatograph coupled to an autosampler and flame ionization detector (Figure 4). Figure 3. Dendrograms for selection of the 25 representative monthly gasoline samples that exhibited least similarity: (a) March, (b) April, (c) May, (d) June and (e) September. The figure was obtained from Pirouette 3.11.

15 Figure 4. Typical ASTM D6729 HRGC-FID chromatographic fingerprints of all representative commercial gasoline samples. High resolution gas chromatography with flame ionization detector (HRGC-FID) fingerprints of gasoline, like commented before, are very complex, showing peaks in almost all the chromatogram. From these chromatograms is not possible distinguish the many compounds present as adulterating solvents since they may also be present in the gasoline. Also, all chromatographic fingerprints (Figure 2) are very similar from one gasoline to another, because the basic refinery processes used in their production are similar. Clearly, visual inspection of the fingerprint chromatogram is not efficient in identifying the presence of adulterant solvents in gasoline and, therefore, to distinguish those that meet or not the gasoline specifications. Any attempt to distinguish between gasoline samples must consider numerous peaks and requires the application of a chemometric classification technique. To apply SIMCA modeling, HRGC-FID fingerprints of 100 gasoline samples (collected for a period of four months) were employed in the training set, and the remaining 25 samples (collected within one further month) formed the prediction set. The sample quality from physicochemical parameters established by ANP 309 regulation was established as a supervised class in the data matrix. Several pretreatments and preprocessing parameters were tested and the best classifications were obtained when autoscaled and logarithmic parameters were applied. Therefore, 80.0% of the prediction set samples was correctly classified by application of the SIMCA method in chromatograms, considering the confidence limits of the physicochemical methods. 4.2 Screening The Quality And Origin Of Gasoline By Nmr Spectrometry And Pattern Recognition Not only chromatography, but also nuclear magnetic resonance (NMR) can be coupled with pattern recognition chemometric models as an analytical tool to determine the quality of gasoline. NMR spectroscopy has especially become a powerful tool for gasoline analysis without pre-treatment, mainly due to the fact that measurements are fast and can be automated, allowing the analysis of a large number of

16 samples in a short period of time. In general, the typical chemical shifts in the spectrum are subdivided into regions and each one is associated with a specific molecular substructure, for example, aromatic, olefinic, and aliphatic hydrogen. A huge amount of data in NMR fingerprinting is produced, and chemometric analysis is frequently needed to extract the desired information (Meusinger, 1999; Monteiro et al., 2009; Rigo et al., 2009) In another approach, related to origin determination purposes, Rigo et al. (2009) combined hydrogen nuclear magnetic resonance (1H NMR) fingerprinting of gasoline with pattern-recognition analyses to distinguish Brazilian commercial gasoline, processed in different states of Brazil. Hierarchical cluster analyses (HCA) and principal component analyses (PCA) were carried out on chemical shifts in order to observe any natural grouping feature, while soft independent modeling of class analogy (SIMCA) was performed to classify external samples into previously origin-defined classes. PCA demonstrated that a small number of variables dominate the total data variability since the first three principal components (PCs) accounted for 64.9% of total variability; whereas a HCA dendrogram shows five natural cluster grouping features. Following optimized 1H NMR-SIMCA algorithm, sensitivity values in the training set with leave-one-out cross-validation (86.0%) and external prediction set (77.3%) were obtained. The method was recommended for rapid quality-control monitoring of gasoline samples for origin authentication related to tax evasion purposes. Flumignan et al. (2010) combined Hydrogen Nuclear Magnetic Resonance (1H NMR) spectroscopic fingerprinting with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis to obtain an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. The overall data set was composed of 2400 gasoline samples, collected randomly from different gas stations in São Paulo state, Brazil, over six months, were stored in polyethylene terephthalate flasks and transported in refrigerated boxes, following official procedures (ABNT, 2002; ASTM, 2006). All gasoline samples were previously analyzed by several physicochemical parameters established in Regulation ANP nº 309 and 150 sample representative samples (79 meeting Brazilian specification and 71 failing Brazilian specification) were selected using HCA analysis. 1 H NMR spectroscopic fingerprinting was acquired for each one of these 150 representative samples. 1 H NMR spectrum fingerprinting of gasoline is very complex, showing peaks almost in all spectral regions, as shown in Figure 5. Clearly, visual inspection of spectroscopic profiles is not efficient to identify the presence of adulterant solvents. Any attempt to distinguish different gasoline sample must take into account subtle changes among a large number of peaks confirming that this kind of problem requires the application of a chemometric classification technique. In the development of SIMCA algorithm, the authors used one hundred gasoline samples to compose the training set and the remaining fifty samples were used as external prediction set. In SIMCA model training set, three principal components accounted approximately 70.0% of the total within-set variance and correctly classified all samples, except for a few ones. According to the authors, the misclassifications may be specifically associated to the results of two physicochemical parameters: the final boiling point (FBP) and motor octane number (MON) both of them measured and correlated according to ASTM standard procedures. Since 94.0% of the samples were correctly classified by application of the SIMCA method, the authors concluded that the modeling power of the training set was satisfactory.

17 Figure 5. 1H NMR spectroscopic fingerprinting of a typical Brazilian commercial gasoline sample (CDCl3, 500 MHz). The other 50 sample used as external prediction set were clearly well segregated, seven of them were apparently misclassified according to the SIMCA model: six non-compliant samples were classified as compliant to ANP regulations and one sample compliant to ANP specifications was classified as noncompliant by SIMCA. In this case, the misclassifications may be associated mainly to MON and FBP values. Misclassifications associated to values from distillation temperature required to reduce 90% of the original volume of the sample (T90) and concentration of anhydrous ethanol (CAE) were also found. In respect to MON values, the physicochemical results were not conclusive, because three samples showed values between 81.4 and 81.9; whilst the minimum permitted value for this parameter is Therefore, none of these parameters can be considered significantly different from the limiting values when the confidence limits of each method are taken into account. Based on these findings, 92.0% of the samples in the prediction set were correctly classified by application of the SIMCA method. Therefore, the authors asserted that 1 H NMR fingerprinting and pattern-recognition SIMCA multivariate chemometric analysis supplied enough information to identify the slight differences between compliant and noncompliant gasoline, allowing the evident distinction between these two groups. The chemometric method was recommended for routine applications in Quality-Control Monitoring Programs, and also, in police laboratories for fast screening analysis to discourage adulteration practices. After the promising results obtained using 1 H NMR, Flumignan et al. (2010) proposed the application of pattern-recognition multivariate analysis coupled to 13 C NMR fingerprinting for quality control of commercial automotive gasoline. 13 C NMR chemical shifts follow the same principles as those of 1 H NMR, although the typical range of chemical shifts is much larger than for 1 H (by a factor of about 20). In general, classes of compounds (not individual ones) are associated with typical spectral regions, i.e., aromatic chemical shifts can be associated with peaks at ppm, while ppm can be associated to alcohol functionality or presence of an alcoxy group (R-CH 2 -O) (Silverstein et al., 1991). The authors have used the same overall data set of 2400 samples from which were obtained 150 representative samples using HCA. In SIMCA model training set, three principal components accounted for approximately 70.0% of the total within set variance and all samples were correctly classified, except

18 for one sample. The misclassification was attributed to the result of determination of CAE; according to NBR (ABNT, 1997) standard procedure. Hence, 99.0% of the samples were correctly classified by application of the SIMCA method. In respect to the external prediction set, all samples were clearly well segregated, although eight of them were apparently misclassified according to the SIMCA model: five non-compliant samples were classified as compliant to ANP regulations and three samples compliant were classified as non-compliant by SIMCA. These misclassifications were attributed to the close frontier between the results of some physicochemical parameters and the corresponding limit value established in the ANP regulation. As mentioned by the authors in the previous paper, none of these parameters can be considered significantly different when the confidence limits of each method are taken into account. In contrast, three samples, that were fully compliant to ANP Regulation 309, were misclassified by the SIMCA model. Based on these findings, 92.0% of the samples in the prediction set were correctly classified by application of the SIMCA method. The final conclusion is that the modelling power was in agreement with the results provided by physicochemical analyses proving that 13 C NMR-SIMCA algorithm allows the segregation of commercial gasoline based on its quality. 4.3 Screening The Quality And Origin Of Diesel Oil By Instrumental Chemical Analysis And Pattern Recognition Despite most of papers found in the literature using analytical methods associated to chemometrics be related to gasoline, some papers referring to diesel were also published. Commercial production of diesel also shall follow a rigid quality control process, to ensure that the physicochemical characteristics of the product are in agreement to those demanded by official regulation. In Brazil, for example, the quality control of commercial diesel is regulated by ANP resolution 42, which determines the evaluation of all parameters listed in Table 3 (ANP, 2010). Diesel oil is constituted basically by paraffinic, olefinic, naphthenic and aromatic hydrocarbons with carbon chains ranging from C 8 to C 40 corresponding to boiling points ranges from 160 to 380 ºC. Other substances containing elements like sulfur, nitrogen and oxygen are also found in low concentrations (Chevron, 2007). Furthermore, Brazil, USA, EU and some Asian countries have internal policies to support the global trade of biofuels. In these countries is commonly known that conventional diesel fuel is commercialized with the addition of different quantities of biodiesel. Most of these countries have a mandatory proportion of B5 (5% of biodiesel to be added to commercial diesel fuel) (Conselho Nacional de Política Energética (CNPE), 2009; Engelen, B. 2009; Oguma et al., 2012; Tripartite Task Force Brazil, European Union United States of America, 2007). Like gasoline, detection of diesel adulteration is a challenging task. The analysis of complex mixtures such as diesel is a great challenge due to the huge number of hydrocarbon compounds present in these matrices. A detailed analysis of hydrocarbon groups in diesel can be obtained coupling a supercritical fluid extraction (SCF) with GC, due to their good compatibility (Baerncopf et al., 2010). The chromatographic analysis of diesel sometimes requires complex temperature programs (Baerncopf et al., 2010; Santos et al., 2012) and complex systems, such a two-dimensional gas chromatography (GC x GC), to achieve better resolution and selectivity in comparison with traditional techniques (Pierce et al., 2005). In an example of pattern-recognition methods applied to diesel fuel, Aleme et al. (2010) performed some studies involving the prediction and classification of diesel samples commercialized in the state of Minas Gerais Brazil, using PCA and LDA. The authors used 200 diesel samples from different origin (refineries) and type (metropolitan diesel and diesel for interior region). They submitted each sam-