Comprehensive Two-Dimensional Gas ChromatographyâMass

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Comprehensive Two-Dimensional Gas Chromatography – Mass Spectrometry / Selected Ion Monitoring (GC × GC – MS/SIM) and Chemometrics to Enhance Inter-Reservoir Geochemical Features of Crude Oils Guilherme Lionello Alexandrino, and Fabio Augusto Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00230 • Publication Date (Web): 17 Jul 2018 Downloaded from http://pubs.acs.org on July 19, 2018

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Comprehensive Two-Dimensional Gas Chromatography – Mass Spectrometry / Selected Ion Monitoring (GC × GC – MS/SIM) and Chemometrics to Enhance InterReservoir Geochemical Features of Crude Oils

Guilherme L. Alexandrino* and Fabio Augusto Institute of Chemistry, State University of Campinas, Cidade Universitária Zeferino Vaz, 13083-970, Campinas – SP, Brazil.

*Corresponding author: Guilherme L. Alexandrino, Ph.D. Institute of Chemistry – State University of Campinas P.O. Box 6154 13084-971 Campinas, SP, Brazil Phone: +55 19 3521-3105 E-mail: [email protected] ORCID: 0000-0002-2007-378X

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Abstract The enhanced analytical performance of the comprehensive two-dimensional gas chromatography – mass spectrometry (GC × GC – MS) has demonstrated significant advantages for geochemical investigations of crude oils. However, the conventional geochemical analysis of crude oils based on diagnostic ratios from specific petroleum biomarkers can be more laborious and time-consuming for GC × GC. Additionally, the extraction of biomarker patterns from the crude oils is more analyst-dependent; therefore, the discovery of less evident and/or new patterns can be more challenging. This work introduces the use of GC × GC – MS/SIM and chemometrics for selected monitoring of the petroleum biomarkers and efficient discovery of the most relevant geochemical features that discriminate inter-reservoir crude oils. Pixels-based analysis using principal component analysis (PCA) extracted the multivariate patterns in the biomarkers fingerprints that successfully distinguished inter-reservoir geochemical properties that could also be obtained by the conventional diagnostics ratio approach, such as; i) the predominant marine or lacustrine organic matter depositional environment of the source rocks, ii) oil’s thermal maturity and iii) biodegradation level. Furthermore, a biomarkers fingerprint that distinguishes similar crude oils among specific reservoirs was obtained using variable selection into partial least squares – discriminant analysis (PLS-DA). The discriminant power of the selected biomarkers was statistically ranked using one-way ANOVA F-test and null distribution analysis. GC × GC – MS/SIM alongside multi-step chemometrics can potentially enhance the geochemistry features that distinguish crude oils according to their origins and/or degrees of chemical similarities.


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Keywords: Comprehensive two-dimensional gas chromatography, selected ion monitoring - mass spectrometry, inter-reservoir, chemometrics, geochemistry

1. Introduction Comprehensive two-dimensional gas chromatography – mass spectrometry (GC × GC - MS) has been increasingly adopted as analytical tool to reveal geochemical features of crude oils and source rocks.1,2,3,4 In GC × GC, two capillary columns are connected through a modulator, which periodically concentrates a fraction of the eluate coming from the first column (first dimension, 1D) and next reinjects this fraction as a narrower band into the head of the shorter second column (second dimension, 2D). The modulation enhances peak detectability, and the two columns containing stationary phases with orthogonal separation properties can separate overlapped peaks in 1D on the two-dimensional chromatographic space.5 GC × GC can overcome strong coelutions occurring in conventional GC-MS analysis of crude oils that persists even in the selected ion monitoring (GC-SIM/MS) of biomarkers for geochemical purposes, e.g.; m/z 123 (sesquiterpanes), m/z 191 (tri-, tetraand pentacyclic terpanes), m/z 217, 218 (ααα and ααβ steranes, respectively), m/z 259 (diasteranes and tetracyclic polyprenoids).6,7 Geochemical investigations of crude oils usually describe the organic matter depositional environment of the source rocks and the levels of thermal maturity and biodegradation of the oils.8 GC and GC × GC analysis of the saturates and the polycyclic aromatic hydrocarbons (PAH) of crude oils has been preferably used for geochemical purposes.6,7,9,10,11,12,13 Mello and coworker have already reported the geochemical properties


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of Brazilian crude oils majorly extracted offshore.14,15 The particular hydrocarbon composition of these crude oils could be successful related to their predominant lacustrine or marine origins of the organic matter deposition environments. The conventional geochemical analysis calculates target diagnostic parameters using peak picking and/or manual integration from the chromatographic signals of target saturates and aromatic biomarkers, e.g. tri-, tetra- and pentacyclic terpanes, steranes, pristane, phytane, gammacerane, phenanthrenes, chrysenes.8 Although this well-established approach provides unambiguous geochemical information, there are some drawbacks inherent to the overwhelming chemical complexity of the crude oils even after the SARA (i.e. Saturates, Aromatics, Resign and Asphaltenes) fractionation: i) the geochemical information is restricted to the interpretation of the specific biomarkers described by the corresponding diagnostic ratio, ii) the identification of additional patterns in the biomarker fingerprints is more difficult and analyst-dependent, and iii) handling a large amount of samples can be very time-consuming.16 The possibility to extend the petroleum analysis beyond the conventional approach can be interesting when handling the more complex data provided by the GC × GC-MS. Chemometrics have already been successfully used to extract efficiently and more analyst-independently the complex chromatographic patterns from the two-dimensional gas chromatograms of crude oils for geochemical purposes.17,18,19 Principal component analysis (PCA) has been the preferable multivariate analytical solution to assess geochemical features of crude oils from GC × GC – FID and GC × GC – MS. This approach has already been successfully implemented in petroleum geochemistry to distinguish similar intra-reservoir crude oils,17 and Brazilian crude oils from different organic matter depositional environments18 and thermal maturity levels.7


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In the present work, we introduce the combination of GC × GC - selected ion monitoring (GC × GC – MS/SIM) of petroleum biomarkers with multi-steps chemometrics to target efficient inter-reservoir discrimination of crude oils from different origins and/or degrees of chemical similarity. A new strategy is presented to highlight the chemical fingerprint that distinguishes different crude oils, and therefore their respective geochemical properties can be associated. In preference to the conventional approach based on individual peak picking and/or peak integration of target petroleum biomarkers, the data handling is performed by pixels-based analysis on concatenated sections of the 2D selected ions chromatograms (SIC). This approach can be considered an extension to GC × GC – MS of the CHEMSIC (CHEMometric analysis of Selected Ion Chromatograms) method, 20 which was originally developed for oil spill investigations using GC-MS. 21,22 Similarly, PCA is performed on the GC × GC – MS/SIM dataset to reveal the overall biomarkers fingerprints in the crude oils that distinguish their inter-reservoirs’ predominant marine or lacustrine origin of the depositional environments, oil’s thermal maturity and in situ biodegradation processes. Furthermore, a second chemometric approach combining variable selection into Partial Least Squares – Discriminant Analysis (PLS-DA) and double cross-validation (2CV) is presented for a non-biased search of less evident biomarker fingerprints that discriminated lacustrine crude oils. Therefore, the concept of using GC × GC – MS/SIM and chemometrics was also extended to obtain geochemical signatures at specific locations that contained similar crude oils. One-way ANOVA F-ratio was used to rank statistically the selected biomarkers obtained using 2CV and PLS-DA, using the combinatorial null distribution F-ratio analysis to assess the individual robustness of each biomarkers for the discrimination. Null distribution F-ratio analysis is a permute-based approach that estimates the statistical significance of each variable for the target 5 ACS Paragon Plus Environment

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discrimination using the experimental data and class-label combinations that nullify the true discrimination of the group of samples.23 This approach provides more realistic results compared to the conventional analysis based strictly on tabulated F-values, which assumes a mandatory chi-square distribution to compare within-class and between-class variances.24,25

2. Experimental 2.1. Samples Twenty-three crude oil samples from predominantly lacustrine (n = 12) or marine (n = 11) organic matter depositional environments and 8 different reservoirs spread in 4 different Brazilian basins were obtained from the inventories of our Organic Geochemical Research Laboratories (Institute of Chemistry, State University of Campinas, Brazil), Table 1. The location of the petroleum fields the samples were collected had to be omitted due to the confidential nature of this information.

2.2. Sample preparation The maltenes fraction (i.e. saturate and aromatic hydrocarbons) was isolated for each crude oil similarly to Gürgey:26 100.0 (± 0.5) mg of the crude oil was suspended in 7 mL of n-pentane and centrifuged at 300 rpm for 5 min, isolating the supernatant afterwards. This extraction procedure was repeated approximately 5 times, until the supernatant became fully transparent for each sample. The solutions containing the dissolved maltenes were assembled in a common flask, and next the solvent was evaporated under a gentle N2(g) 6 ACS Paragon Plus Environment

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flow. The dried maltenes fraction was re-dissolved using n-hexane to 20 mg mL−1, and next they were transferred to amber glass vials before the chromatographic analysis.

2.3. GC × GC – TQMS The maltenes were analyzed using a lab-made GC × GC – TQMS instrument containing a GC-2010+ chromatograph with a split/splitless injector and a GCMS-TQ8030 triple quadrupole mass-analyzer (Shimadzu Corp., Kyoto, Japan). The GC first dimension was a capillary column (30 m × 0.25 mm i.d. × 0.25 µm) containing 5% phenyl methyl polysiloxane as stationary phase (RTX-5ms), connected to a similar column (0.85 m x 0.25 mm i.d. x 0.25 µm) as the loop. The second dimension was a capillary column (1.5 m x 0.15 mm i.d. x 0.15 µm) coated with the more polar 50% diphenyl - 50% dimethyl polysiloxane (Rxi®-17Sil MS). The GC conditions were an injection volume of 1 µL, split mode 1:70 at T = 300 ºC, purge flow 2 mL min-1 and oven ramp set to 70 ºC until 325 ºC at 3 ºC min-1. Hydrogen (≥ 99.999 %) was used as carrier gas at initial flow of 1.09 mL min-1. The lab-made cryogenic modulator operated with N2(g) cold jets frozen in liquid nitrogen (p = 5 psi) and N2(g) hot jets at T = 380 ºC and p = 105 psi. The jet pulses were controlled by solenoid valves (ASCO, Florham Park, NJ – USA) connected to an Arduino® board that set the elapsed time of each pulse throughout the analysis. The modulation period (MP) was set to 5.0 s, with the following gradient programmed for the cold and hot pulses elapsed time throughout the chromatographic analysis: initial elapsed times of 2250 ms (cold jet) and 250 ms (hot jet) until the analysis time t = 30 min. Then, the times were varied by -2 ms (cold jet) and +2 ms (hot jet) per MP, until reaching 1506 ms (cold jet) and 994 ms (hot jet) at t = 61 min, remaining unaltered until the end of the run (t = 85 min). This approach 7 ACS Paragon Plus Environment

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using time-variable jets along the chromatographic analysis was necessary to properly modulate the entire range of hydrocarbons during the analysis of the crude oils, and it has already been described elsewhere.27 The mass spectrometer conditions were transfer line temperature at 300 ºC, ion source temperature at 230 ºC, detector voltage at 1.5 kV, quadrupoles temperature at 150ºC and electron ionization energy at 70 eV. The MS/SIM data were acquired for the targeted m/z ions; 177 (C-10 demethylated terpanes), 191 (tri-, tetra- and pentacyclic terpanes), 217 (ααα-steranes), 231 (triaromatic steroids and methyl steranes) and 259 (diasteranes and tetracyclic polyprenoids), at 25 Hz acquisition rate. The identification of the most relevant biomarkers was performed in the samples containing the highest (relative) abundances of the target compounds, using metastable reaction monitoring (MRM) GC × GC – MS/MS;12 Ar(g) collision gas at 12 eV energy, for the m/z transitions: 370 → 191, 384 → 191, 398 → 191, 412 → 191, 426 → 191 (C27 to C31 αβhopanes and gammacerane), 372 → 217, 386 → 217, 400 → 217 (C27 to C29 ααα-steranes).

3. Data Treatment 3.1. Data acquisition and preprocessing The data acquisition was done using the GCMS solution software, version 4.20 (Shimadzu Corp., Kyoto, Japan). The GC × GC – MS/SIM data from each m/z channel (i.e. 177, 191, 217, 231 and 259) were truncated at t = 72 min to exclude the next predominant noisy baseline signals. The chromatograms were converted to .txt files and next imported into Matlab® R2016a software (Matworks, Natick – MA, USA) as unfolded row-wise data matrices X(i)(23, 120000), in which i is the m/z channel, 23 is the number of samples and 120,000 is the total number of variables associated with the retention times in the first (1tR) 8 ACS Paragon Plus Environment

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and second (2tR) dimensions. The high number of variables in each X(i) was significantly reduced after excluding the regions of each SIM chromatogram containing only baseline signals. Then, the remaining portions of the X(i) matrices containing only the selectively detected peaks were row-wisely concatenated into a single augmented matrix D(23, 164250), the variables representing only the selected signals from the unfolded two-dimensional SIC. This approach is similar to the first data handling steps in the CHEMSIC method, except that GC × GC – MS provides enhanced peak resolution. The preprocessing steps were initially baseline correction and data smoothing, using the Savitzky-Golay algorithm (window = 11 points, polynomial grade = 3), to reduce noise without broadening the peaks. Next, D was normalized to the constant Euclidian norm to remove concentration difference artifacts in the signals due to the sample preparation and injection. An in-house script was written in Matlab to perform the piecewise peak alignment in D, using the icoshift v.1.2.3 algorithm,28 to minimize peak shifts among the samples that could disturb the data analysis.

3.2. Data Modelling 3.2.1. Principal Component Analysis The preprocessed D was mean-centered and the PCA was performed using the Pls_Toolbox 8.11 software (Eigenvector Research Inc., Wenatchee—WA, USA) for Matlab, extracting only the most relevant PCs from D while interpreting the data. The loadings from each PC were refolded to the original two-dimensional structure (1tR × 2tR) of the chromatograms for identification of the biomarkers significant to that PC.

3.2.2. Discriminating the lacustrine crude oils 9 ACS Paragon Plus Environment

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3.2.2.1. Variable Selection and Partial Least Squares – Discriminant Analysis The submatrix L(12, 164250) was built from D containing only the crude oils from predominant lacustrine depositional environments (Table 1). The variable selection performed during PLS-DA extracted the most relevant biomarkers while distinguishing the crude oils extracted in L1 from the crude oils extracted elsewhere (i.e. L2, L3, L4 and L5). For instance, the chemometric approach performed a variable selection using the VIP (Variable Importance on Projections) scores during PLS-DA with double cross-validation (2CV).29 For 2CV, L was randomly split into 2 new submatrices LCAL(9, 164250) and LTEST(3, 164250),

keeping unaltered the proportion of the samples from both classes in these

submatrices. The PLS-DA was performed only in the LCAL, using leave-one-out crossvalidation for model optimization. The correct number of latent variables (LVs) for the models was obtained in the lowest root mean squares error of cross-validation (RMSECV). After model optimization, only the VIP scores upon a threshold were selected for modelling LCAL. This threshold was defined (iteratively) below which the RMSECV did not increase for the corresponding PLS-DA model, which was computed for a new model every time a new threshold was established. The final PLS-DA model containing only the variables selected previously was used to predict the class-labels in LTEST, attesting the quality of the model when inspecting the misclassification predictions. This overall 2CV procedure was performed iteratively for all non-repetitive combinations of the samples in L while building LCAL and LTEST, resulting that only the chromatographic signals selected in at least 55 % of the total number of iterations were considered efficient and robust for the classification. The remaining variables in LCAL were removed, because the contrary resulted in an overall increase of the misclassifications while predicting LTEST after another cycle of iterations.


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This approach combining variable selection into double cross-validated PLS-DA models attested a non-biased identification of relevant biomarkers that successfully discriminated the similar lacustrine crude oils. A similar chemometric approach for variable selection in metabolomics has already been published elsewhere.30 PCA in the autoscaled data containing only the selected variables (peaks) highlighted the relative abundance of the corresponding biomarkers associated with the inter-reservoir geochemical features of the crude oils. These procedures were also performed using in-house Matlab scripts and the Pls_Toolbox.

3.2.2.2. One-way ANOVA F-ratio and combinatorial null distribution analysis One-way ANOVA F-ratio ranked the biomarkers selected previously that successfully discriminated the lacustrine crude. The null distribution F-ratio analysis computed the statistical significance of each biomarker directly form the experimental data and all the non-repetitive class-label combinations that nullified the true discrimination of the samples. An in-house script was also written in Matlab to perform the one-way ANOVA F-ratio and the null distribution analysis.

4. Results and Discussion 4.1. Inter-Reservoir Discrimination of crude oils using Principal Component Analysis The PCA model performed in D extracting 2 PCs (explained variance = 62.39%) revealed PC1 mainly explaining the predominant lacustrine or marine origin of the organic matter depositional environment of the crude oils, Fig. 1-a), independently their location 11 ACS Paragon Plus Environment

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within the basins. The PC1 loadings corresponding to the SIC m/z 177 and m/z 191 demonstrate the overall higher relative abundance of the signals corresponding to C20 to C34 tri- and tetracyclic terpane series, C27 to C31 17α,21β(H)-hopane series (αβ-hopanes) and (di)methyl-phenanthrenes (dm-Ph and m-Ph), within the corresponding chromatographic fingerprints for the lacustrine crude oils; conversely, 18α(H),21β(H)-30norneohopane (C29Ts), 17α(H)-30-nor-29-homohopane (NOR30H), 17α(H),21β(H)-25,30bisnorhopane (25,30-BNH), C29 17α(H),21β(H)-25-norhopane (25-NH) and gammacerane were found in higher relative abundance in the marine crude oils (Fig. 1-b). Diasteranes, C27 to C29 steranes and the triaromatic steroids series obtained from the SIC m/z 217 (αααsteranes), m/z 231 (triaromatic steroids and methyl steranes) and m/z 259 (diasteranes) were also found in higher (relative) intensities within the chromatograms of the marine crude oils (Fig. 1-c). Therefore, the clustering between lacustrine and marine crude oils in PC1 is intrinsically related to the relative differences between steranes and αβ-hopanes in the crude oils, the main source of variance expressed in the SIC. The steranes/αβ-hopanes ratio, i.e. the main source in PC1, has been a common geochemical parameter to attribute the predominant lacustrine or marine depositional environment of crude oils.15,14,11 Moreover, strongly 1D coeluting C19 - C20 tricyclic terpanes and (di)methyl phenanthrenes, tricyclic and tetracyclic terpanes, tricyclic terpanes and αβ-hopanes, as well as triaromatic steroids and methyl steranes (SIC m/z 191 and 231, respectively) demanded GC × GC for proper chromatographic resolution. PC1 also distinguishes the marine oils in M1 and the lacustrine oils in Basin 3 (L2-1 and L3-1) from their respective counterparts. The lower PC1 scores for the M1 oils reveal higher steranes/αβ-hopanes ratios and more gammacerane (see Fig. 1a) within their chromatographic fingerprint compared to M2 and M3, which typifies a hypersaline marine deposition environment for the crude oils from M1.10,31 Although L2-1 12 ACS Paragon Plus Environment

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and L3-1 (Basin 3) are predominantly lacustrine, the steranes/αβ-hopanes patterns and the (relative) higher abundance of 25,30-BNH and 25-NH (i.e. negative chromatographic loadings in PC1) in these oils (Basin 3) distinguish them from the remaining lacustrine oils. These geochemical features suggest a more marine organic matter input into the depositional environments of the de-cluttered lacustrine oils. For instance, the higher presence of the norhopanes within the crude oils fingerprint has been attributed to the occurrence of in situ biodegradation due to demethylation of hopanes. 15,32,33,34 The depletion of n-alkanes and isoprenoids (data not shown) reveal biodegradation severely affecting only L2-1; The higher presence of the norhopanes within the chromatographic fingerprint of the predominantly (non-biodegraded) marine oils has already been reported in marine evaporitic Brazilian crude oils, incl. highly anoxic oils, by Mello and coworkers. 14,15

The inter-reservoir clustering of the crude oils is enhanced after projecting the samples on PC2, specially while distinguishing M3-1 from M2 oils and L4-1 from the [L1 + L5-1] cluster (see Fig. 1-a). The PC2 loadings in SIC m/z 177 and m/z 191 (Fig. 2-a) show the relative higher abundance within the chromatograms of the signals from tri- and tetracyclic terpanes series, 25,28,30-trisnorhopane (25,28,30-TNH), 17α(H)-22,29,30trisnorhopane (Tm), 17α(H),21β(H)-30-norhopane (H29) and 17α(H),21β(H)-hopane (H30), in the following order for the marine crude oils; M2 < M3 < M1. The contrary occurs for the signals from dm-Ph, m-Ph, 25,30-BNH and 25-NH. The higher abundance of Tm comparing to the 18α(H)-22,29,30-trisnorneohopane (Ts) is a classical geochemical diagnostic for lower thermal maturity crude oils.8 Furthermore, in situ biodegradation can also be investigated for the above-mentioned marine oils based on the relative abundance of


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the norhopanes and secohopanes. 11,34 For instance, the (relative) higher abundance of H29 in relation to C29Ts, and the contrary for 25,30-BNH and 25-NH, suggest a lower in situ biodegradation level of the crude oils according to the aforementioned order. The PC2 loadings for the SIC m/z 217, m/z 231 and m/z 259 (Fig. 2-b) reveal higher C27 to C30 steranes and methyl-steranes and lower C27 diasteranes. This geochemical features suggest a more reducing condition for the organic matter inputs in the depositional environment commonly occurring in hypersaline marine environments.35 These patterns also correspond to the inter-reservoir discrimination of the lacustrine crude oils in the PC1 x PC2 scores plot that infer lower salinity for the lacustrine crude oils extracted from L3-1 (Basin 3) and L4-1 (Basin 2).

4.2. Variable Selection and Classification of the Lacustrine Crude Oils Although the PCA of the GC × GC – MS/SIM crude oils data succeeded the interreservoir discrimination of the marine crude oils, this is still unclear when inspecting only the lacustrine crude oils, even after fitting models with additional PCs. However, interreservoir discrimination of lacustrine oils could be performed after selecting only the biomarkers correlated to this specific information. The 2CV handles PLS-DA models prone to overfitting for data containing high variables/samples ratio,29 typically occurring in (multidimensional) chromatography; therefore, the robust and most discriminant chromatographic signals (biomarkers) are revealed more efficiently. The double crossvalidated PLS-DA after variable selection resulted in averaged models fitted with 2 (±1) LVs, inner CV misclassifications (model optimization) and prediction misclassifications of 24.70 (±0.07) % and 5.70 (±0.03) %, respectively. The CV errors are due to the reduced 14 ACS Paragon Plus Environment

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number of samples in class 2 (i.e. L2-1, L3-1, L4-1 and L5-1) comparing to class 1 (i.e. 8 crude oils from L1), which can leverage the CV misclassifications in the reduced group. However, the prediction errors are coherent to an efficient variable selection and appropriate non-biased classification, because the predictions are performed totally independent from the modelling step within the 2CV iterations.29 This approach only identified the target biomarkers for the discrimination. The biomarker patterns in the oils were revealed after PCA in L containing only the selected signals. The model containing 2 PCs (explained variance = 81.21%) successfully distinguishes the crude oils from L1 and the remaining crude oils, Fig. 3-a). The more discriminating PC1 loadings (Fig. 3b) infers the crude oils in L1 contain relatively higher contribution of the C21 and C23 tricyclic terpanes, 25,28,30-TNH, Tm, H29 and H30 within their respective biomarkers fingerprints; likewise, the same trend occurs for NOR30H and gammacerane in the crude oils from the other basins. Therefore, the crude oils in L1 are prone to be less mature (↑Tm), they had more terrigenous organic matter input into the depositional environment (↑tricyclic terpanes, ↑H29 and ↑H30), and they were formed under lower salinity conditions (↓gammacerane), comparing to the lacustrine crude oils from elsewhere. Although all crude oils are predominantly lacustrine, the results suggest a lower influence of marine organic matter inputs into the depositional environments that generated the oils in L1. The one-way ANOVA F-ratios computed for the selected biomarker are ranked in Table 2, in which the most discriminant biomarkers contain the highest F-value. The Fratio thresholds obtained from the combinatorial null distribution analysis minimizes the false discovery rate (FDR) according to different confidence levels, Fig. 4. The lower null probability can be associated with the lower chances for type-I error (false positive), i.e.


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one biomarker is reported to have discrimination power in the samples at a certain confidence level when this is not correct.23 The higher F-values obtained for C20 to C24 tricyclic terpanes, 25,28,30-TNH, Tm and Gam emphasize their higher diagnostic power (p-value ≤ 0.01) when discriminating the lacustrine crude oils from L1, while the statistical significance decreases (p-value ≤ 0.05) for the remaining biomarkers according to the order: dmPh < H29 < NOR30H. The m-Ph and H30 were considered false positives at the 95% confidence level (p-values > 0.05), despite an apparent contribution for the classification is obtained with PLS-DA, i.e. they showed linear correlation for the discrimination. The FDR > 5% for m-Ph and H30 can be attributed to their respective higher within-group variances, which cannot be handled by the PLS-DA when maximizing the linear correlations in the dataset for the classification.36 The within-groups sources of variation have to be also considered when interpreting the role of the relevant biomarkers that explain the geochemical differences between similar lacustrine oils.

5. Conclusions The GC × GC – MS/SIM combines the enhanced chromatographic performance with the selective detection of the target petroleum biomarkers for geochemical interpretation about crude oils. The capability of chemometrics to extract the different sources of variation from the biomarkers fingerprints in the crude oils was crucial to handle more efficiently the geochemical features that discriminate the different types of crude oils. The multivariate patters were successfully correlated with the predominant organic matter


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depositional environments, and levels of thermal maturity and biodegradation. The main geochemical features responsible for the inter-reservoir discriminations was extracted from the data using PCA. Furthermore, similar crude oils could be successfully distinguished when combining variable selection and PLS-DA. This approach highlighted the interreservoir geochemical differences of similar crude oils from the biomarkers fingerprints. One-way ANOVA F-ratio enhanced the interpretation about these fingerprints after ranking the selected biomarkers according to their respective discrimination power, in which the combinatorial null distribution F-ratio analysis assessed the individual statistical significance of the biomarkers. This work demonstrated that different chemometric approaches can be successfully integrated into high-throughput multidimensional techniques to efficiently handle the complex data obtained from the selective analysis of petroleum biomarkers in crude oils for geochemical purposes.

Acknowledgment The authors acknowledge the São Paulo Research Foundation (FAPESP, grant number 2015/08201-0) for providing the research funding.

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Table 1. Crude oils from predominantly lacustrine or marine depositional environments, according to their respective reservoirs and basins. Sample L1-1 L1-2 L1-3 L1-4 L1-5 L1-6 L1-7 L1-8 L2-1 L3-1 L4-1 L5-1 M1-1 M1-2 M2-1 M2-2 M2-3 M2-4 M2-5 M2-6 M2-7 M2-8 M3-1

Reservoir

Basin

Depositional environment

L1 L1 L1 L1 L1 L1 L1 L1 L2 L3 L4 L5 M1 M1 M2 M2 M2 M2 M2 M2 M2 M2 M3

Basin 1 Basin 1 Basin 1 Basin 1 Basin 1 Basin 1 Basin 1 Basin 1 Basin 3 Basin 3 Basin 2 Basin 4 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2 Basin 2

Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Lacustrine Marine Marine Marine Marine Marine Marine Marine Marine Marine Marine Marine


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Table 2. One-way ANOVA F-ratio computed for the peaks (biomarkers) selected using PLS-DA, while distinguishing lacustrine crude oils in L1 from the remaining counterparts (i.e. L2, L3, L4 and L5). Biomarker 25,28,30-TNH TriT21 TriT23 Gam TriT20 TriT24 Tm dmPh NOR30H H29 H30 mPh

F-ratio 44.25 37.42 27.72 24.51 17.81 13.17 11.51 8.12 6.52 6.26 4.91 3.43


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Fig. 1. a) PC1 vs PC2 scores plot for the samples (Table 1) after PCA on D containing concatenated GC × GC – MS/SIM data for the selective m/z 177 (C-10 demethylated terpanes), 191 (tri-, tetra- and pentacyclic terpanes), 217 (ααα-steranes), 231 (triaromatic steroids and methyl steranes) and 259 (diasteranes and tetracyclic polyprenoids). b) PC1 chromatographic loadings for m/z 177 and 191: TriTi; Ci tricyclic terpane, mPh; methyl-phenanthrenes, dm-Ph; dimethyl-phenanthrenes, TetrT24; C24 tetracyclic terpane, Ts; 18α(H),21β(H)-22,29,30- trisnorhopane, TNH; 17α(H),21β(H)-25,28,30-trisnorhopane, Tm; 17α(H),21β(H)22,29,30-trisnorhopane, 25,30-BNH; 17α(H),21β(H)-25,30-bisnorhopane, H28; 17α(H),21β(H)-29,30bisnorhopane, 25NH; C29 17α(H),21β(H)-25-norhopane, H29; 17α(H),21β(H)-30-norhopane, C29Ts; 18α(H),21β(H)-30-norneohopane, H30; 17α(H),21β(H)-hopane, NOR30H; 17α(H)-30-nor-29-homohopane, H31; C31 homohopanes, Gam; gammacerane. c) PC1 chromatographic loadings for m/z 217, 231 and 259: Dia27; C27 diasteranes, Si; Ci ααα-steranes, mSi; Ci methyl steranes, TAS; triaromatic steroids. Tentative identifications performed according to the elution order in the MRM GC × GC – MS/MS chromatograms (Supporting Information), ref.12. 308x177mm (300 x 300 DPI)

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Fig. 2. a) PC2 chromatographic loadings for m/z 177 and 191: TriTi; Ci tricyclic terpane, mPh; methylphenanthrenes, dm-Ph; dimethyl-phenanthrenes, TetrT24; C24 tetracyclic terpane, Ts; 18α(H),21β(H)22,29,30- trisnorhopane, TNH; 17α(H),21β(H)-25,28,30-trisnorhopane, Tm; 17α(H),21β(H)-22,29,30trisnorhopane, 25,30-BNH; 17α(H),21β(H)-25,30-bisnorhopane, H28; 17α(H),21β(H)-29,30-bisnorhopane, 25NH; C29 17α(H),21β(H)-25-norhopane, H29; 17α(H),21β(H)-30-norhopane, C29Ts; 18α(H),21β(H)-30norneohopane, H30; 17α(H),21β(H)-hopane, NOR30H; 17α(H)-30-nor-29-homohopane, H31; C31 homohopanes, Gam; gammacerane. b) PC2 chromatographic loadings for m/z 217, 231 and 259: Dia27; C27 diasteranes, Si; Ci ααα-steranes, mSi; Ci methyl steranes, TAS; triaromatic steroids. Tentative identifications performed according to the elution order in the MRM GC × GC – MS/MS chromatograms (Supporting Information), ref. 12. 309x112mm (300 x 300 DPI)


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Fig. 3. a) PC1 x PC2 scores plot after variable selection into CV2-PLSDA, while distinguishing the crude oils from Basin 1 and the remaining lacustrine crude oils from Basin 2, Basin 3 and Basin 4 (Table 1). b) PC1 chromatographic loadings corresponding to the main biomarkers for the discrimination: TriTi; Ci tricyclic terpane, mPh; methyl-phenanthrenes, dm-Ph; dimethyl-phenanthrenes, TNH; 17α(H),21β(H)-25,28,30trisnorhopane, Tm; 17α(H),21β(H)-22,29,30-trisnorhopane, H29; 17α(H),21β(H)-30-norhopane, H30; 17α(H),21β(H)-hopane, NOR30H; 17α(H)-30-nor-29-homohopane, Gam; gammacerane. Tentative identifications performed according to the elution order in the MRM GC × GC – MS/MS chromatograms (Supporting Information), ref. 12. 310x115mm (300 x 300 DPI)


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Fig. 4. Combinatorial null distribution F-ratio analysis to attest the statistical significance of the one-way ANOVA F-ratio when discriminating the lacustrine crude oils in L1 from the remaining counterparts (i.e. L2, L3, L4 and L5). Green and red lines denote the F-ratio thresholds (α < 0.01) upon which the false discovery probability of the selected biomarkers (Table 2) ≤ 5% and ≤ 1%, respectively. 119x96mm (300 x 300 DPI)


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