Forensic Science International 234 (2014) 86–94
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Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Method development for forensic identification of biodiesel based on chemical fingerprints and corresponding diagnostic ratios Zeyu Yang a,b,*, Bruce P. Hollebone b,**, Zhendi Wang b, Chun Yang b, Carl Brown b, Mike Landriault b a
Key Laboratory of Catalysis and Materials Science of the State Ethnic Affairs Commission & Ministry of Education, Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, College of Chemistry and Materials Science, South-Central University for Nationalities, Wuhan 430074, PR China Emergencies Science and Technology Section, EOALRSD, Science and Technology Branch, Environment Canada, Canada
b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 July 2013 Accepted 4 November 2013 Available online 14 November 2013
A forensic identification method based on the chemical fingerprinting of the first generation of biodiesel (fatty acid alkyl esters as effective components), and several corresponding diagnostic ratios was developed and validated. The distribution of major fatty acid methyl esters (FAMEs) and polar compounds (free fatty acids, glycerol, monoacylglycerides, and free sterols) in several representative above biodiesel products commercially available in Canada were positively quantified and compared, a number of cross-plots of diagnostic ratios of target FAMEs and sterols were developed for biofuel correlation and differentiation. It was found that the cross-plots of FAME ratios, for example, the sum of the di-unsaturated relative to saturated homologues of FAMEs (D/S) versus the sum of the monosaturated to saturated FAMEs (M/S), and the sum of di-unsaturated to mono-saturated FAMEs (D/M) versus the sum of the mono-saturated to saturated FAMEs (M/S), could cluster samples clearly into their individual feedstock. The cross-plots of diagnostic ratios of individual major sterols (cholesterol, brassicasterol, campesterol, b-stiosterol and stigmasterol) to the total sterols were also developed and proved to be effective in identifying biodiesel sources due to their self-normalizing effect on sterol data. The case study of a mystery biodiesel spill using this method showed that the two real samples can be tightly clustered into biodiesel from animal fat (Ban) group. However, the significant discrepancy of free fatty acids, glycerol, monoacylglycerides and sterol concentrations between the two real samples indicated their different producing batches. ß 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Biodiesel Chemical fingerprinting Forensic identification Diagnostic ratios
1. Introduction Biodiesel, an ‘‘alternative’’ diesel-grade fuel derived from triglycerides, vegetable oils and animal fats, has attracted significant attention in the past decade [1] with growing interest in sustainable sources of vehicular and heating fuels. Currently, the first generation of biodiesel, like fatty acid methyl esters (FAMEs), has been commercially used in many countries. It is well known that for petroleum oil spills, stable and source-distinctive compounds, such as alkylated polycyclic aromatic hydrocarbons (PAHs), terpanes, and steranes have been used for identification
* Corresponding author at: South-Central University for Nationalities, 708 Nationality Road, Hongshan District, Wuhan 430074, China. Tel.: +86 27 67842752; fax: +86 27 67842752. ** Corresponding author at: Emergencies Science and Technology Section (ESTS), Environment Canada, 335 River Road, Ottawa, ON K1V 1H3, Canada. Tel.: +1 613 991 4568; fax: +1 613 991 9485. E-mail addresses: [email protected] (Z. Yang), [email protected] (B.P. Hollebone). 0379-0738/$ – see front matter ß 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.forsciint.2013.11.001
and environmental assessment [2–4]. Development in oil fingerprinting procedures includes analytical techniques and data analysis. For example, data analysis generally encompasses visual comparison of chromatograms [5–7], bar charts of alkanes and PAH concentrations [7,8], tabulation, double plots of diagnostic ratios [7], and multivariate statistical techniques [4,9]. Therefore, a similar ‘‘chemical fingerprinting’’ methodology and the selection of relatively stable ‘‘molecular markers’’ for biodiesel is extremely important for characterizing spilled ones, monitoring the affected ecosystem, determining the fate of biodiesel in the environment, and subsequently, helping in assessing the environmental damage. It is noted that all biodiesels occurred in the present study represent FAME based biodiesels unless otherwise stated. In biodiesels, although the main components are the fatty acid alkyl esters [10–13], considerable chemical variation for fatty acid esters has been observed because of the use of a wide variety of feedstock source oils [1], this would be useful for source identification. The fatty acid ester compounds, however, are consumed by bacteria in a short period after a biodiesel or the blends of biodiesel with diesel spill, hence a contaminated site
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
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could eventually become indistinguishable from a fossil diesel spill [14]. This environmental instability could hinder longer-term spill source characterization and forensic investigations for biodiesels. In addition to the majority triglycerides, however, sterols are also compounds found in vegetable oils and animal fats. Concentrations of sterols in oil are highly dependent on the original organism and are frequently used as characteristic markers of biological oils [15]. As relatively stable, persistent compounds, sterols are thus attractive candidates for use as forensic markers. For example, cholesterol can remain unchanged even after six months despite anoxic condition, temperature and salinity variations [16]. Because of their high solubility in nonpolar solvents, it is reasonable to suppose that sterols would be found in high concentrations in biodiesels and that their distribution would reflect the source oil. Plank and Lorbeer [17] have reported the detection of free phytosterols in mixtures of fatty acid methyl esters (FAMEs) produced from vegetable oils by on-line LC–GC, and suggested that sterols would be useful tracers for pure biodiesels. Recently, sterol profiling was found to be a better approach for matching spilled biodiesel samples to a source than FAME profiling [18]. Except for FAMEs and free sterols, free fatty acids (FFAs), glycerol and monoacylglycerides (MGs), as the by-products of biodiesel, can also act as the complementary parameters for source tracking for biodiesels, these targets can be analyzed together with free sterols [19]. Currently, although many analytical methods have been developed for the quality control of biodiesel and its blends production [20,21], few reports have yet been found that are specifically about the forensic identification of biodiesel based on the full chemical fingerprinting of biodiesel and diagnostic indicators from FAMEs and free sterols. The purpose of the present study is to develop an analytical method for clustering biodiesels into their feedstock sources, and supply an integrated methodology for forensic identification of spilled biodiesel based on the full chemical fingerprinting of spilled biodiesel. The corresponding diagnostic ratios of FAMEs and sterols acting as ‘‘source-specific markers’’ will be defined, compared and evaluated by inspection analysis among different sourced biodiesels. These developed diagnostic ratios as well as complementary information will be applied for forensic identifying the origin of one mystery spilled biodiesel sample (obtained from St. Lawrence River in December, 2009).
information from manufacturers, these samples included six samples of biodiesel derived from soybean oils (Bsoy), 5 samples of biodiesel from canola oils (Bcan), 1 sample of biodiesel from a mixture of animal fat and waste frying oil (Bmix) and 2 samples of biodiesel from pure animal fat (Ban-1 and Ban-2). The developed source identification method was verified by applying it to seek for the source of one mystery biodiesel spill. The spill was occurred in December, 2009 along St. Lawrence River. One spilled sample and one suspected source was sampled at the same time. The spilled sample was partial solid with brown color and sandy sediment particles at controlled room temperature (about 22 8C). The suspected sample was transparent and colorless at room temperature. The total solvent extractable material (TSEM) contents in the spilled sample and the suspected source were determined to be 73.8% and 100% (mass/mass), respectively. The origin identification was performed by analyzing their full chemical fingerprinting and the developed diagnostic parameters in the present study.
2. Materials and methods
2.3. Identification and quantification of target analytes
2.1. Chemicals and materials
Identification of analytes were based on the following criteria: (1) a positive match of both GC retention time and mass spectral data of silylation derivatives of target analytes with those of both authenticated standards and representative of biodiesel samples and (2) a positive match of mass spectra of target analytes with that of the NIST (the U.S. National Institute of Standards and Technology) 2008 Mass Spectral Library [23]. To minimize the interference of other sample components, target analytes were quantified by the single ion with highest abundance, or characterized fragment ions. The identification and quantification of FAMEs were based on the 37 FAME standards and the FAMEs surrogate 13-methyl, myristate (13-methyl, C14:0 ME). The quantitative and confirmation ions, similar to Ref. [22], are listed in Table S1. Fatty acids, glycerol, a-glyceryl oleate (1-C18:1) and sterols in the silylanized high-polarity isolates were also quantified by GC/ MS in SIM mode, the quantification ions and the corresponding information are available in Table S2. The internal standards used were 5-a-cholestane (5 mg/mL, monitored at m/z = 217) for FFAs and sterols, and tricaprin (9 mg/mL, monitored at m/z = 383) for
The solvents used, including hexane, dichloromethane (DCM), acetone, and methanol, were obtained from Caledon (Georgetown, ON, Canada) with HPLC grade and used without further purification. Silica gel (100–200 meshes) was obtained from Sigma– Aldrich (Milwaukee, WI, USA). All FAME standards and heptadecanoic acid methyl ester (C17:0 ME, internal standard for FAMEs), were purchased from Sigma– Aldrich (Bellefonte, PA, USA). The detailed chemical information and abbreviations for FAME standards are listed in Table S1 (Supplementary Materials). Standard mixture including FFAs, glycerol, monoacylglycerides, and sterols (Table S2), were purchased from Sigma-Aldrich Canada Ltd. (Oakville, ON, Canada). The silylation reagent, bis (trimethylsilyl) trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS), was obtained from Supelco (Bellefonte, PA, USA). Fourteen biodiesel samples with known sources were obtained from commercial producers. Samples were stored in a dark cold room (4 8C) to reduce the rate of degradation. Based on the
2.2. Analytical protocols Analytical protocols, including sample preparation, fractionation of polar fraction (including FFAs, glycerol, MGs and free sterols) from biodiesel, derivatization of polar compounds, GC/MS analysis, identification and quantification of target analytes, were abstracted from Refs. [19,22]. Briefly, biodiesel samples diluted and spiked with C17:0 ME as internal standard, were analyzed directly by GC/MS in a Selective Ion Monitoring (SIM) mode for individual FAME analysis. The monitored ions are the most abundant ions for every target analytes. For analysis of polar compounds, biodiesel samples were fractionated by eluting a 3-g silica gel column with 30 mL of DCM (designated as F1 fraction) and 15 mL of methanol (designated as F2 fraction), sequentially, into FAME (F1) and polar compound (F2) fractions. F2 fraction was dried, silylanized by BSTFA with 1% TMCS and spiked with 5a-cholestane and tricaprin as internal standard for GC/MS analysis of FFAs, glycerol, MGs and free sterols. A DB-225 MS GC column (30 m 0.25 mm i.d., film thickness: 0.25 mm) obtained from Agilent and an SAC 5 GC column (30 m 0.25 mm i.d., film thickness: 0.25 mm) supplied by Supelco (Bellefonte, PA, USA) were employed to separate FAME and polar compounds, respectively. An internal calibration method was used to determine all target analytes in the present study.
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3. Results and discussion 3.1. Distinguishing biodiesels by feedstocks based on FAME distribution The GC separation information of FAMEs is consistent with previous reports [24–28]. The total ion chromatogram (TIC) for FAME standards and several real samples with known sources are shown in Figs. S1 and S2 in the Supplementary Materials. For the composition profiles of FAMEs, the dominant components of all the investigated biodiesels in this study are FAMEs (>90%, mass/mass) and the distribution profiles of FAMEs in Bmix and Ban samples are more complex due to the contribution from animal fats. FAMEs with 16 and 18 carbons are the main constituents for all biodiesels, methyl linoleate (cis-9, 12, C18:2 ME) is the most dominant components in Bsoy samples, however, methyl oleate (cis-9, C18:1) ME is the most abundant FAME in Bcan and Ban samples. Bmix and Ban samples are characterized as high saturated FAMEs, for example, methyl palmitate (C16:0 ME) and methyl stearate (C18:0 ME). Similar compositional profiles were observed for the other five additional biodiesel samples. In all, biodiesels manufactured from the same type of feedstock were found to have similar patterns and relative levels of FAME congeners. A Student’s t-test for our former reported samples showed that more than 90% of p-values were higher than 0.05 for the majority (>1% mass/mass) FAMEs made from the same feedstock, this indicates the high similarity among them [29]. It is noted that the data analysis of FAMEs and sterols of Bmix sample suggested that this sample was sourced from the mixture of animal fat and canola oil based. Therefore, this sample was designated as Bmix-can in later sections. Another 5 biodiesel samples were also analyzed in the present study, similar results were achieved for all the freshly added Bcan and Ban samples. However, except for the dominance of cis-9, 12, C18:2 ME in the two freshly added Bsoy samples, it was found that the concentration of the saturated FAMEs (C16:0 ME and C18:0 ME) in these two samples were higher than the old Bsoy samples. It seems that these two Bsoy samples designated by manufacture were not sourced by pure soybean oil, but the mixture of animal fat and soybean oil. This assumption was confirmed by the analysis of the distribution of sterols. For sterols, except that phytosterols were detected in them, cholesterol was dominant in these two Bsoy samples. The combination analysis of the above results indicated these two samples were originated from the mixture of animal fat and soybean oil. They were designated as Bmix-soy in later sections. 3.2. Diagnostic ratios of different types of FAMEs Many diagnostic ratios currently used in oil spill studies and environmental forensics were originated from the petroleum geochemistry literature [2,8,30,31]. An important benefit of comparing diagnostic ratios of spilled oil and suspected source oils is that concentration effects are minimized. In addition, the use of ratios tends to induce a self-normalizing effect on the data variations due to the fluctuation of instrument operating conditions day-to-day, operator, as well as the minimization of matrix
effects. Therefore, comparison of diagnostic ratios reflects more direct difference of the target analytes distribution among samples. Selection of diagnostic ratios employed in petroleum oil spill studies is mainly based on source-specific variables, and the most appropriate diagnostic ratios are those that are not changed in spill samples and source oils. For biodiesel, FAMEs are the main components, where saturated FAMEs are the most recalcitrant degradation species among them. However, the ratios between saturated congeners (e.g., the ratios between C16:0 ME and C18:0 ME) are close to each other among different sourced biodiesel, which demonstrates that these ratios are not the appropriate parameters to distinguish biodiesels from different origins. The diagnostic ratios based on the saturation degree of FAMEs were primarily investigated in the following section. In detail, the ratios of the sum of the di-unsaturated and saturated homologues of FAMEs, the ratios of the sum of the monounsaturated and saturated homologues of FAMEs and the ratios of the sum of di-unsaturated and mono-saturated homologues of FAMEs (i.e., SDIFAME/SSFAME, SMFAEM/SSFAME and SDIFAME/ SMFAME, abbreviated as D/S, M/S and D/M) were calculated. It is noted that saturated FAMEs indicates that all FAMEs without any double bonds in the test samples; mono-unsaturated and diunsaturated FAMEs indicate all FAMEs with one and two double bonds, respectively, identified in the test samples. The cross-plots of the double ratios based on D/S and D/M versus M/S are shown in Fig. 1. It illustrates a clear separation among different sourced biodiesels. Biodiesels with same feedstock from different batches produce tight clusters on the plot. In detail, the M/S ratios for all Bmix and Ban samples were close to those of Bsoy samples, but their 5.0 Bsoy
4.0 Bcan
3.0
D/S
glycerol and MG congeners. The relative response factors (RRFs) determined from the standards were used directly for quantifying the corresponding target analytes in samples, while the RRFs averaged from two adjacent fatty acids were used for quantifying the intermediate fatty acids. The RRF determined from 1-C18:1 was used to quantify all monoacylgycerides in the two real samples as no other monoacylglyceride standards were available. Similarly, the RRFs from the chromatographically adjacent sterols were used for quantifying sterols without authenticated standards.
2.0
Bmix
Bmix
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M/S Fig. 1. Cross-plots of the double ratios of SDIFAME/SSFAME (D/S) and SDIFAME/ SMFAME (D/M) versus SMFAME/SSFAME (M/S) for biodiesels with known sources.
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
D/S and D/M ratios were less than those of Bcan and Bsoy samples, this character distinguished all animal based biodiesels from pure vegetable oil based ones. For Bsoy and Bcan samples, although the D/ S ratios of Bcan and Bsoy samples were found to be close to each other (2.5–3.9), Bcan samples were characterized as the high M/S (7.4–11.3) and low D/M (0.18–0.35) ratios, but Bsoy samples were characterized as the high D/M (1.8–2.3), but low M/S (1.4–1.7) ratios compared with those from Bcan. Therefore, it is easy to differentiate Bsoy and Bcan from each other and from Ban and Bmix samples by the cross-plots in Fig. 1. The next purpose is to differentiate Bmix from Ban samples. Among Bmix and Ban samples, the D/S ratios from high to low were ranked as: Bmix-soy (sourced from the mixture of soybean oil and animal fat, 1.45) > Bmix-can (sourced from the mixture of canola oil and animal fat, 0.7) > Ban (0.2), similar trend was observed for D/M ratios, and Bmix-can (2.4) > Bmix-soy (Ban) (1.2–1.4) for M/S ratios. It is obvious that the existence of soybean oil source contributes to relatively higher D/S and D/M ratios compared with those from Bmix-can and Ban samples. The high concentration of saturated FAMEs from animal fat contributes to the relative low ratios of M/S and D/S in pure animal fat sourced biodiesel. These results demonstrate the clear differentiation of two types of Bmix samples from Ban. In conclusion, biodiesels from different sources can be clustered into their own group directly by this method. Except for the purpose of using these ratios to identify biodiesel from different sources, these ratios also show the promising potential for characterizing the degradation degree for spilled biodiesel. This is because the principal drawback of biodiesel against petroleum-based fluids is its vulnerability to oxidation, the susceptibility to oxidation of a biodiesel increases with the number of double bonds found in its composition and their configuration [32]. FAMEs with two and one double bonds will be degraded faster than the saturated homologues. The ratios of D/S, M/S, and D/M will decrease as
time goes by after spill occurs. The degradation degree of spilled fluids can be revealed by comparing these ratios between spilled sample and suspected source. 3.3. Polar compounds in biodiesels 3.3.1. Free sterols in biodiesels As discussed in the previous sections, FAMEs, especially unsaturated ones with methylene-interrupted double bonds, are highly biodegradable. Once biodiesel spills into the environment, most of FAMEs will degrade in several days to 3–4 weeks [33,34], this will cause the alteration of above diagnostic ratios, e.g., D/S, or D/M, resulting in inaccurate forensic identification results. Accordingly, characterization of the more stable free sterols in biodiesels may be a very useful option for source allocation of a spilled biofuel. Phytosterols have been used as the markers of plant oils to assess the contamination of some vegetable oils with cheaper ones [15]. In the present study, free sterols, including phytosterols and zoosterols, have been identified and quantified in the representative biodiesel samples as a complementary parameter for the origin identification of biodiesel. Except for sterol standards listed in Table S2 (Supplementary Materials), the identified and quantified sterols also included campestanol (CAO) and D5avenasterol (AV). The detailed information of GC separation and GC chromatograms of the TMS ether of standards and representative samples are shown in Fig. S3 in Supplementary Materials. The distribution profiles of free sterols in target biodiesels are similar to those that we have reported [19]. In brief, the concentration of cholesterol (CS) in biodiesels with animal fat source is far higher than those with pure vegetable oil source, only trace amounts of CS were detected in Bcan and Bsoy samples, and very low concentration of phytosterols were detected in pure animal fat sourced samples. The phytosterols b-sitosterol (SI) and
.6
.15
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BR/Total sterols
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CS/Total sterols Fig. 2. Cross-plots of diagnostic ratios of CA, BR, ST and SI relative to total sterols versus CS relative to total sterols for biodiesel samples with known sources. CS: Cholesterol; CA: Campesterol; BR: Brassicasterol; ST: Stigmasterol and SI: b-stiosterol.
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
3.3.2. FFAs, glycerol and monoacylglycerides in biodiesels Similar results have been achieved for the identified and quantified FFAs, glycerol and monoacylglycerides reported in our previous work [19] for all studied biodiesels. In brief, the measured total concentrations of FFAs were found to be in good agreement with the acid values measured by the ASTM standard titration method. The main FFAs were identified as oleic acid (cis-9, C18:1), stearic acid (C18:0), linoleic acid (cis-9, 12, C18:2), and palmitic acid (C16:0). The distribution of glycerol and MGs, as well as FFAs showed source specificity. These results suggest that FFAs, glycerol and MGs have great potential as target compounds for forensic identification of biodiesels and for the future research on the degradation of biodiesels. Unfortunately, different products may present different levels of glycerol and MGs, as they are main byproducts of biodiesel. The detrimental components of free fatty acids, either carried over from the feedstock or produced as the degradation intermediates of fatty acid esters during the storage, transportation and usage of biodiesel, are also varied with feedstock properties and storage, transportation conditions. In conclusion, their distribution profiles would partly exhibit source specification but contents vary randomly, which suggests that they are more suitable for distinguishing samples from same source but with different producing batches. Therefore, only diagnostic ratios of free sterols would be discussed in the following section for identifying biodiesel with different sources. 3.4. Diagnostic ratios of sterols for forensic identification of specific biodiesels Because sterols showed feedstock specificity, various diagnostic ratios based on the major source specific sterols (CS, BR, CA, SI and ST) have been calculated and evaluated to directly clarify their source specificity. For example, the ratios between CS and BR, CS/ (1 + BR), or CS/total phytosterols (ratios between major sterols and the total phytosterols are listed in Table S3 in the Supplementary Materials). The cross-plots of these ratios did not show their obvious clustering effects. However, the cross-plots of the ratios of individual major sterols to total sterols (expressed as CS, BR, CA, ST or SI)/total sterols) versus the ratios of CS/total sterols can cluster biodiesels into different groups based on their feedstock sources (Fig. 2). It can be seen that CS was dominant with an average ratio of higher than 0.94 in pure animal sourced biodiesels, followed by Bmix with a ratio ranging from 0.37 to 0.55. However, this ratio was less than 0.01 in any vegetable oil sourced biodiesels. All other phytosterol ratios for animal based biodiesels were far less than vegetable oil based ones. Accordingly, the CS ratio can tell the difference among biodiesels from pure animal fat, mixture of animal fat and vegetable oil and pure vegetable oil source. BR ratio (from 0.03 to 0.11) was only significant in Bmix-can and Bcan samples due to the contribution from canola oil source. Similarly, soybean oil based samples (Bsoy and Bmix-soy) showed specific high ST ratios due to the contribution from soybean oil source. It is noted that BR ratio for Bmix-can and ST ratios for Bmix-soy were less than those in the corresponding pure vegetable oil based samples. Although Bsoy and Bcan samples cannot be discriminated from each other depending on CA and SI ratios, both of them are significant in
all samples from pure vegetable oil because CA and SI are two major phytosterols in these two feedstocks. In conclusion, BR and ST ratios combined with CS ratios can be used for source identification of biodiesels with various sources. These sourcespecified free sterols suggest the possibility of using them as complimentary tracers for forensic identification of biodiesel and its blends. For example, medium to high CS ratio indicates the contribution of animal fat, low CS ratio indicates the vegetable oil contribution. Significant ST ratios suggest the contribution of soybean oil, and significant BR ratios mean the canola oil source. In summary, diagnostic ratios of M/S, D/S, D/M, CS/total sterols and four phytosterols (BR, CA, ST and SI) to total sterols can be used for forensic identification of spilled biodiesel. However, some diagnostic ratios are heavily affected by measurement errors, thus a proper variable selection is important to keep the uncertainties to minimum and yield reliable results. Diagnostic power (DP), defined as the relative standard derivation of a diagnostic ratio in oils with different sources (RSDv) divided by relative sampling standard derivation (RSDs) by Christensen et al. [9,35] was used to perform variable-outlier detection for proper selection of diagnostic ratios in the present study. The obtained RSDv, RSDs and DP values are listed in Table S4 in the Supplementary Materials. It was found all DP values are higher than 1, this suggests that these selected FAMEs and sterols ratios are not affected by large experimental errors or weathering, so all of them can be selected as estimation parameters for identifying spilled biodiesel. It is noted that only limited types of biodiesel feedstocks have been present in the present study because these types have been
FAME concentration (% mass/massTSEM)
campesterol (CA) are the most abundant sterols found in all Bsoy and Bcan samples. No significant discrepancy was found for SI and CA in all these biodiesel samples. Significant concentrations of brassicasterol (BR) were only found in Bcan or Bmix-can samples. Similarly, stigmasterol (ST) is especially significant in any soybean oil or the mixture of soybean oil and animal fat based samples. These differences in the distribution of sterols showed a potential for clarifying the sources of biodiesels due to the relative stability of sterols compared to FAMEs.
50 Suspected source Spilled sample Ban
Major FAMEs 40
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0 C14:0
C16:0 C16:1n7c C18:0 C18:1n9cC18:1n8cC18:2n6cC18:3n3c
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Identified sterols Fig. 3. Comparison of the TSEM normalized major FAMEs and sterols among the suspected source, spilled, Ban, Bmix-can, and Bmix-soy samples.
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
widely found in the market of the North America. However, many different feedstocks have been found as the source of biodiesels, such as palm oil, rapeseed, and coconut oil. Actually, canola oil is a modified rapeseed oil by removing erucic acid due to its adverse effect on human health, therefore, rapeseed oil based biodiesel can be clustered into canola oil group based on the above cross-plots. For biodiesels from palm oil or coconut oil, both of them are characterized as high levels of saturated fatty acids (up to 70–90%), this results in both of them falling into the Ban group by crossplotting of D/S versus M/S or D/M versus M/S. However, the low concentration of cholesterol in both feedstocks makes them separate from Ban group. Accordingly, the above two series of cross-plots combined together can distinguish biodiesels from different feedstocks, regardless of the limited types of biodiesel feedstocks presented in this study. 3.5. Method application for forensic identification of a mystery biodiesel spill
it can be qualitatively concluded that the two real samples are dominant with C16:0 ME, followed by cis-9, C18:1 ME, then C18:0 ME, methyl myristate (C14:0 ME) and cis-9, 12, C18:2 ME. Their FAME distribution profiles are significantly different from Bcan. Similar distribution profiles of glycerol, MG congeners, FFAs and sterols were observed for both of them regardless of the difference of the absolute abundance (Fig. S5). In detail, phytosterols (SI, CA and BR) are dominant in Bcan, however, only CS is significant in the two real samples. All above qualitative results primarily indicated the two real samples may be originated from animal fat. Fig. 3 depicts the comparison of the compositional profiles of major FAMEs and free sterols among these two real samples, Bmixsoy, Bmix-can and Ban samples. It is noted that FAMEs with mass percentage of less than 1% in all above samples are not available here. All data discussed here are expressed on the total solvent extractable material (TSEM) basis (in mg/g TSEM or in mg/g TSEM) rather than on the sample weight or extracted volume basis. TSEM provides an equal basis for forensic comparison of the relative chemical composition of FAMEs, and other target polar compounds between spill and suspected source samples. It is only by this way that comparison of the quantified results between samples is meaningful. In brief, a very similar FAME distribution profile pattern for the spilled and suspected samples was observed although the FAME concentration in the spilled samples was slightly lower than the suspected one. Paired t-test showed that all p-values were higher than 0.05 for the majority (>1% mass/mass) FAMEs, indicating a very high degree of similarity in feedstock for both samples. The
10000 Suspected source Spilled sample
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3.5.1. FAMEs and sterols distribution in two real samples One spilled sample and one suspected source were sampled from St. Lawrence River in December, 2009 for the forensic identification of the spilled sample and the evaluation of the present developed method. The physical properties and the primary GC/MS full scan of the two real samples indicate that they are pure biodiesel. The GC/MS chromatograms of FAMEs for the two real samples and one Bcan are shown in Fig. S4. From Fig. S4
91
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0 :1 :2 C20:0 C22:0 C24:0 C14:0 C16:0 C18:0 , C18 , C18 cis-9cis-9, 12
FFA congeners
Bcan
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4
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M/S Fig. 4. Comparison of the TSEM normalized glycerol, monoacylglycerides (MGs) and free fatty acids (FFAs) between the suspected source and spilled samples. The detailed abbreviations for MGs are listed in Table S5. For example, 2-C16:0 represents b-glyceryl palmitate; 1-C18:2 represents a-glyceryl linoleate.
Fig. 5. Clustering the real samples using cross-plots of the double ratios of SDIFAME/SSFAME, and SDIFAME/SMFAME versus SMFAME/SSFAME for all studied biodiesel samples.
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distribution of FAME compounds are complex with many geometries and saturation (straight, iso, anteiso-methyl-branched chains) homologues, suggesting the contribution from animal fat feedstock. They can be primarily clustered into Bmix-can or Ban only based on the FAME distribution profile. The total FAMEs concentration in the spilled and suspected source samples was determined to be 58.9% and 91.2% (mass/mass), respectively, the total FAME content in the spilled sample is 84% after normalized to TSEM. The possible reasons for the discrepancy of the normalized FAMEs concentration in the two real samples can be partially ascribed to the following factors: (1) the suspected source was not directly responsible for the spilled sample, (2) weathering process, for examples, dispersion, photo-degradation or biodegradation, resulting in the relative low concentration of FAMEs in the spilled sample. However, the spilled sample was collected once the spill occurred, some poly-, and di-unsaturated FAMEs were detected in the spilled sample although they were more vulnerable to be oxidized (Fig. 3). All these information suggest that the weathering process is not the controlling factor for the discrepancy of the FAME concentrations between these two samples. This also demonstrates that the spilled sample may not be subjected to the direct discharge of the suspected source. For sterols, CS is the most abundant free sterol in both samples, with an average concentration of 272 and 3598 mg/g TSEM in the suspected source and spilled sample, respectively (Fig. 3). CA, ST, and BR were detected with a very low concentration in the two real samples. The abundant CS in these two samples primarily indicates that both of them were made from animal fat, at least mainly from animal fat. Reverse to the FAME concentration, the sterol concentrations in the spilled sample are consistently higher than those in the suspected source. For example, CS in the spilled sample was more than 10 times of the suspected sample (Fig. S6).
The comparison of sterols from these two real samples with Ban and two types of Bmix samples indicates that the measured CS in the suspected biodiesel was quite low, followed by Bmix-can and Bmix-soy, then the spilled sample and Ban (Fig. 3). CA, BR and ST in Bmix samples are generally higher than those in Ban, the suspected and spilled samples. As the spilled sample was found to be the mixture of biodiesel and sandy sediment, the reason for the abundant CS in the spilled sample can be ascribed to the following possibilities: (1), the suspected sample was not directly responsible for the spilled samples, i.e., they were sourced from same feedstock, but different producing batches; (2), other high input of CS into the sampling area except for biodiesel. It is well known that CS is ubiquitous in the environmental matrix, especially in sediment, due to the contribution of cholesterol synthesized by freshwater organisms such as algae, phytoplankton and macrophytes [36,37] or from anthropogenic sources such as agricultural runoff and sewage disposal [38]. Therefore, the further identification is necessary to exclude any of the above two possibilities. 3.5.2. FFAs, glycerol and monoacylglycerides distribution profiles in two real samples FFAs, glycerol and MGs, as the complementary parameters, can be used to identify biodiesels with different feedstocks or with same feedstock but different producing batches. So the above target analytes in the two real samples were identified and quantified (Fig. 4). For MG congeners, the abbreviated names are used in Fig. 4 (please see the detailed abbreviations listed in Table S5.). It is obvious that a-glyceryl palmitate (1-C16:0) and a-glyceryl stearate (1-C18:0) are dominant in both samples, followed by a-glyceryl oleate (1-C18:1), then glycerol, despite the measured concentrations in the spilled sample are significantly higher than those in the suspected sample. For FFAs, C16:0 is the most abundant component, followed by cis-9, 12, C18:2 and C18:0, then myristic acid (C14:0)
0.6
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CS/Total sterols Fig. 6. Clustering the real samples using cross-plots of diagnostic ratios of CA, BR, ST and SI relative to total sterols versus CS relative to total sterols for all studied samples. CS: Cholesterol; CA: Campesterol; BR: Brassicasterol; ST: Stigmasterol and SI: b-stiosterol.
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and cis-9, C18:1. Similarly, the measured FFA concentrations in the spilled sample are significantly high than those in the suspected one. Based on the above mentioned results from the polar fraction, it can be concluded that all the determined polar targets showed significant discrepancy between the two real samples. As the discussion in the previous paragraph, environmental matrix could contribute CS to the spilled sample except for biodiesel, but not for FFAs, glycerol and MG congeners. They should be similar to each other because biodiesel was the only dominant contributor to them if both samples were from same source. Therefore, the significantly high CS in the spilled sample was not contributed from the environmental matrix but from biodiesel itself, the suspected sample was not directly responsible for the spilled one. 3.5.3. Source identification of two real samples Just as the description in Section 3.2, the diagnostic ratios of FAMEs based on saturation degree are good indicators for the forensic identification of biodiesels from different sources. The cross-plots of diagnostic ratios of D/S versus M/S and D/M versus D/S of the two real samples are exhibited (Fig. 5) to investigate the possible source and the relationship between the two real samples. It can be seen that all ratios are located in the area of Ban samples, all Bmix samples can be clearly differentiated from both of them. Therefore, both samples were originated from animal fat. Similarly, the cross-plots of the diagnostic ratios of CS, BR, ST and SI relative to the total sterols versus CS/total sterols for the two real samples are present in Fig. 6 for oil matching of the two real samples, as we have verified the measured CS in the spilled sample was from the spilled biodiesel itself. It can be seen that the ratios of CS (close to 1) and all phytosterol clustered both of them into Ban group and separated them from the other sourced biodiesels clearly. Based on the above full chemical fingerprinting and the corresponding cross-plotting of diagnostic ratios for the two real samples, it can be concluded as follows: (1), the two real samples were sourced from animal fat; (2) the suspected source was not directly responsible for the spilled biodiesel, they may be produced from different batches. 4. Conclusions A biodiesel forensic identification method based on the full chemical fingerprinting of biodiesel and diagnostic ratios of fatty acid methyl esters (FAMEs) and source specific free sterols was developed and validated. The cross-plots of FAME ratios based on the saturated degree and the relative sterol contents versus cholesterol can cluster biodiesels into different feedstocks. The distribution profiles of FFAs, glycerol and MG congeners can act as the complementary parameters for the source identification of spilled biodiesels. The spilled case study suggested that the two real samples were clustered into animal fat source but with different producing batches. Acknowledgements Funding for this work was provided by Natural Resources Canada through the Program for Energy Research and Development (PERD), the National Natural Science Foundation of China (No.41373133), and the Scientific Fund from State Ethnic Affairs Commission of China (12ZNZ003). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.forsciint.2013. 11.001.
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Contents lists available at ScienceDirect
Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Method development for forensic identification of biodiesel based on chemical fingerprints and corresponding diagnostic ratios Zeyu Yang a,b,*, Bruce P. Hollebone b,**, Zhendi Wang b, Chun Yang b, Carl Brown b, Mike Landriault b a
Key Laboratory of Catalysis and Materials Science of the State Ethnic Affairs Commission & Ministry of Education, Key Laboratory of Analytical Chemistry of the State Ethnic Affairs Commission, College of Chemistry and Materials Science, South-Central University for Nationalities, Wuhan 430074, PR China Emergencies Science and Technology Section, EOALRSD, Science and Technology Branch, Environment Canada, Canada
b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 July 2013 Accepted 4 November 2013 Available online 14 November 2013
A forensic identification method based on the chemical fingerprinting of the first generation of biodiesel (fatty acid alkyl esters as effective components), and several corresponding diagnostic ratios was developed and validated. The distribution of major fatty acid methyl esters (FAMEs) and polar compounds (free fatty acids, glycerol, monoacylglycerides, and free sterols) in several representative above biodiesel products commercially available in Canada were positively quantified and compared, a number of cross-plots of diagnostic ratios of target FAMEs and sterols were developed for biofuel correlation and differentiation. It was found that the cross-plots of FAME ratios, for example, the sum of the di-unsaturated relative to saturated homologues of FAMEs (D/S) versus the sum of the monosaturated to saturated FAMEs (M/S), and the sum of di-unsaturated to mono-saturated FAMEs (D/M) versus the sum of the mono-saturated to saturated FAMEs (M/S), could cluster samples clearly into their individual feedstock. The cross-plots of diagnostic ratios of individual major sterols (cholesterol, brassicasterol, campesterol, b-stiosterol and stigmasterol) to the total sterols were also developed and proved to be effective in identifying biodiesel sources due to their self-normalizing effect on sterol data. The case study of a mystery biodiesel spill using this method showed that the two real samples can be tightly clustered into biodiesel from animal fat (Ban) group. However, the significant discrepancy of free fatty acids, glycerol, monoacylglycerides and sterol concentrations between the two real samples indicated their different producing batches. ß 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Biodiesel Chemical fingerprinting Forensic identification Diagnostic ratios
1. Introduction Biodiesel, an ‘‘alternative’’ diesel-grade fuel derived from triglycerides, vegetable oils and animal fats, has attracted significant attention in the past decade [1] with growing interest in sustainable sources of vehicular and heating fuels. Currently, the first generation of biodiesel, like fatty acid methyl esters (FAMEs), has been commercially used in many countries. It is well known that for petroleum oil spills, stable and source-distinctive compounds, such as alkylated polycyclic aromatic hydrocarbons (PAHs), terpanes, and steranes have been used for identification
* Corresponding author at: South-Central University for Nationalities, 708 Nationality Road, Hongshan District, Wuhan 430074, China. Tel.: +86 27 67842752; fax: +86 27 67842752. ** Corresponding author at: Emergencies Science and Technology Section (ESTS), Environment Canada, 335 River Road, Ottawa, ON K1V 1H3, Canada. Tel.: +1 613 991 4568; fax: +1 613 991 9485. E-mail addresses: [email protected] (Z. Yang), [email protected] (B.P. Hollebone). 0379-0738/$ – see front matter ß 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.forsciint.2013.11.001
and environmental assessment [2–4]. Development in oil fingerprinting procedures includes analytical techniques and data analysis. For example, data analysis generally encompasses visual comparison of chromatograms [5–7], bar charts of alkanes and PAH concentrations [7,8], tabulation, double plots of diagnostic ratios [7], and multivariate statistical techniques [4,9]. Therefore, a similar ‘‘chemical fingerprinting’’ methodology and the selection of relatively stable ‘‘molecular markers’’ for biodiesel is extremely important for characterizing spilled ones, monitoring the affected ecosystem, determining the fate of biodiesel in the environment, and subsequently, helping in assessing the environmental damage. It is noted that all biodiesels occurred in the present study represent FAME based biodiesels unless otherwise stated. In biodiesels, although the main components are the fatty acid alkyl esters [10–13], considerable chemical variation for fatty acid esters has been observed because of the use of a wide variety of feedstock source oils [1], this would be useful for source identification. The fatty acid ester compounds, however, are consumed by bacteria in a short period after a biodiesel or the blends of biodiesel with diesel spill, hence a contaminated site
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could eventually become indistinguishable from a fossil diesel spill [14]. This environmental instability could hinder longer-term spill source characterization and forensic investigations for biodiesels. In addition to the majority triglycerides, however, sterols are also compounds found in vegetable oils and animal fats. Concentrations of sterols in oil are highly dependent on the original organism and are frequently used as characteristic markers of biological oils [15]. As relatively stable, persistent compounds, sterols are thus attractive candidates for use as forensic markers. For example, cholesterol can remain unchanged even after six months despite anoxic condition, temperature and salinity variations [16]. Because of their high solubility in nonpolar solvents, it is reasonable to suppose that sterols would be found in high concentrations in biodiesels and that their distribution would reflect the source oil. Plank and Lorbeer [17] have reported the detection of free phytosterols in mixtures of fatty acid methyl esters (FAMEs) produced from vegetable oils by on-line LC–GC, and suggested that sterols would be useful tracers for pure biodiesels. Recently, sterol profiling was found to be a better approach for matching spilled biodiesel samples to a source than FAME profiling [18]. Except for FAMEs and free sterols, free fatty acids (FFAs), glycerol and monoacylglycerides (MGs), as the by-products of biodiesel, can also act as the complementary parameters for source tracking for biodiesels, these targets can be analyzed together with free sterols [19]. Currently, although many analytical methods have been developed for the quality control of biodiesel and its blends production [20,21], few reports have yet been found that are specifically about the forensic identification of biodiesel based on the full chemical fingerprinting of biodiesel and diagnostic indicators from FAMEs and free sterols. The purpose of the present study is to develop an analytical method for clustering biodiesels into their feedstock sources, and supply an integrated methodology for forensic identification of spilled biodiesel based on the full chemical fingerprinting of spilled biodiesel. The corresponding diagnostic ratios of FAMEs and sterols acting as ‘‘source-specific markers’’ will be defined, compared and evaluated by inspection analysis among different sourced biodiesels. These developed diagnostic ratios as well as complementary information will be applied for forensic identifying the origin of one mystery spilled biodiesel sample (obtained from St. Lawrence River in December, 2009).
information from manufacturers, these samples included six samples of biodiesel derived from soybean oils (Bsoy), 5 samples of biodiesel from canola oils (Bcan), 1 sample of biodiesel from a mixture of animal fat and waste frying oil (Bmix) and 2 samples of biodiesel from pure animal fat (Ban-1 and Ban-2). The developed source identification method was verified by applying it to seek for the source of one mystery biodiesel spill. The spill was occurred in December, 2009 along St. Lawrence River. One spilled sample and one suspected source was sampled at the same time. The spilled sample was partial solid with brown color and sandy sediment particles at controlled room temperature (about 22 8C). The suspected sample was transparent and colorless at room temperature. The total solvent extractable material (TSEM) contents in the spilled sample and the suspected source were determined to be 73.8% and 100% (mass/mass), respectively. The origin identification was performed by analyzing their full chemical fingerprinting and the developed diagnostic parameters in the present study.
2. Materials and methods
2.3. Identification and quantification of target analytes
2.1. Chemicals and materials
Identification of analytes were based on the following criteria: (1) a positive match of both GC retention time and mass spectral data of silylation derivatives of target analytes with those of both authenticated standards and representative of biodiesel samples and (2) a positive match of mass spectra of target analytes with that of the NIST (the U.S. National Institute of Standards and Technology) 2008 Mass Spectral Library [23]. To minimize the interference of other sample components, target analytes were quantified by the single ion with highest abundance, or characterized fragment ions. The identification and quantification of FAMEs were based on the 37 FAME standards and the FAMEs surrogate 13-methyl, myristate (13-methyl, C14:0 ME). The quantitative and confirmation ions, similar to Ref. [22], are listed in Table S1. Fatty acids, glycerol, a-glyceryl oleate (1-C18:1) and sterols in the silylanized high-polarity isolates were also quantified by GC/ MS in SIM mode, the quantification ions and the corresponding information are available in Table S2. The internal standards used were 5-a-cholestane (5 mg/mL, monitored at m/z = 217) for FFAs and sterols, and tricaprin (9 mg/mL, monitored at m/z = 383) for
The solvents used, including hexane, dichloromethane (DCM), acetone, and methanol, were obtained from Caledon (Georgetown, ON, Canada) with HPLC grade and used without further purification. Silica gel (100–200 meshes) was obtained from Sigma– Aldrich (Milwaukee, WI, USA). All FAME standards and heptadecanoic acid methyl ester (C17:0 ME, internal standard for FAMEs), were purchased from Sigma– Aldrich (Bellefonte, PA, USA). The detailed chemical information and abbreviations for FAME standards are listed in Table S1 (Supplementary Materials). Standard mixture including FFAs, glycerol, monoacylglycerides, and sterols (Table S2), were purchased from Sigma-Aldrich Canada Ltd. (Oakville, ON, Canada). The silylation reagent, bis (trimethylsilyl) trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS), was obtained from Supelco (Bellefonte, PA, USA). Fourteen biodiesel samples with known sources were obtained from commercial producers. Samples were stored in a dark cold room (4 8C) to reduce the rate of degradation. Based on the
2.2. Analytical protocols Analytical protocols, including sample preparation, fractionation of polar fraction (including FFAs, glycerol, MGs and free sterols) from biodiesel, derivatization of polar compounds, GC/MS analysis, identification and quantification of target analytes, were abstracted from Refs. [19,22]. Briefly, biodiesel samples diluted and spiked with C17:0 ME as internal standard, were analyzed directly by GC/MS in a Selective Ion Monitoring (SIM) mode for individual FAME analysis. The monitored ions are the most abundant ions for every target analytes. For analysis of polar compounds, biodiesel samples were fractionated by eluting a 3-g silica gel column with 30 mL of DCM (designated as F1 fraction) and 15 mL of methanol (designated as F2 fraction), sequentially, into FAME (F1) and polar compound (F2) fractions. F2 fraction was dried, silylanized by BSTFA with 1% TMCS and spiked with 5a-cholestane and tricaprin as internal standard for GC/MS analysis of FFAs, glycerol, MGs and free sterols. A DB-225 MS GC column (30 m 0.25 mm i.d., film thickness: 0.25 mm) obtained from Agilent and an SAC 5 GC column (30 m 0.25 mm i.d., film thickness: 0.25 mm) supplied by Supelco (Bellefonte, PA, USA) were employed to separate FAME and polar compounds, respectively. An internal calibration method was used to determine all target analytes in the present study.
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3. Results and discussion 3.1. Distinguishing biodiesels by feedstocks based on FAME distribution The GC separation information of FAMEs is consistent with previous reports [24–28]. The total ion chromatogram (TIC) for FAME standards and several real samples with known sources are shown in Figs. S1 and S2 in the Supplementary Materials. For the composition profiles of FAMEs, the dominant components of all the investigated biodiesels in this study are FAMEs (>90%, mass/mass) and the distribution profiles of FAMEs in Bmix and Ban samples are more complex due to the contribution from animal fats. FAMEs with 16 and 18 carbons are the main constituents for all biodiesels, methyl linoleate (cis-9, 12, C18:2 ME) is the most dominant components in Bsoy samples, however, methyl oleate (cis-9, C18:1) ME is the most abundant FAME in Bcan and Ban samples. Bmix and Ban samples are characterized as high saturated FAMEs, for example, methyl palmitate (C16:0 ME) and methyl stearate (C18:0 ME). Similar compositional profiles were observed for the other five additional biodiesel samples. In all, biodiesels manufactured from the same type of feedstock were found to have similar patterns and relative levels of FAME congeners. A Student’s t-test for our former reported samples showed that more than 90% of p-values were higher than 0.05 for the majority (>1% mass/mass) FAMEs made from the same feedstock, this indicates the high similarity among them [29]. It is noted that the data analysis of FAMEs and sterols of Bmix sample suggested that this sample was sourced from the mixture of animal fat and canola oil based. Therefore, this sample was designated as Bmix-can in later sections. Another 5 biodiesel samples were also analyzed in the present study, similar results were achieved for all the freshly added Bcan and Ban samples. However, except for the dominance of cis-9, 12, C18:2 ME in the two freshly added Bsoy samples, it was found that the concentration of the saturated FAMEs (C16:0 ME and C18:0 ME) in these two samples were higher than the old Bsoy samples. It seems that these two Bsoy samples designated by manufacture were not sourced by pure soybean oil, but the mixture of animal fat and soybean oil. This assumption was confirmed by the analysis of the distribution of sterols. For sterols, except that phytosterols were detected in them, cholesterol was dominant in these two Bsoy samples. The combination analysis of the above results indicated these two samples were originated from the mixture of animal fat and soybean oil. They were designated as Bmix-soy in later sections. 3.2. Diagnostic ratios of different types of FAMEs Many diagnostic ratios currently used in oil spill studies and environmental forensics were originated from the petroleum geochemistry literature [2,8,30,31]. An important benefit of comparing diagnostic ratios of spilled oil and suspected source oils is that concentration effects are minimized. In addition, the use of ratios tends to induce a self-normalizing effect on the data variations due to the fluctuation of instrument operating conditions day-to-day, operator, as well as the minimization of matrix
effects. Therefore, comparison of diagnostic ratios reflects more direct difference of the target analytes distribution among samples. Selection of diagnostic ratios employed in petroleum oil spill studies is mainly based on source-specific variables, and the most appropriate diagnostic ratios are those that are not changed in spill samples and source oils. For biodiesel, FAMEs are the main components, where saturated FAMEs are the most recalcitrant degradation species among them. However, the ratios between saturated congeners (e.g., the ratios between C16:0 ME and C18:0 ME) are close to each other among different sourced biodiesel, which demonstrates that these ratios are not the appropriate parameters to distinguish biodiesels from different origins. The diagnostic ratios based on the saturation degree of FAMEs were primarily investigated in the following section. In detail, the ratios of the sum of the di-unsaturated and saturated homologues of FAMEs, the ratios of the sum of the monounsaturated and saturated homologues of FAMEs and the ratios of the sum of di-unsaturated and mono-saturated homologues of FAMEs (i.e., SDIFAME/SSFAME, SMFAEM/SSFAME and SDIFAME/ SMFAME, abbreviated as D/S, M/S and D/M) were calculated. It is noted that saturated FAMEs indicates that all FAMEs without any double bonds in the test samples; mono-unsaturated and diunsaturated FAMEs indicate all FAMEs with one and two double bonds, respectively, identified in the test samples. The cross-plots of the double ratios based on D/S and D/M versus M/S are shown in Fig. 1. It illustrates a clear separation among different sourced biodiesels. Biodiesels with same feedstock from different batches produce tight clusters on the plot. In detail, the M/S ratios for all Bmix and Ban samples were close to those of Bsoy samples, but their 5.0 Bsoy
4.0 Bcan
3.0
D/S
glycerol and MG congeners. The relative response factors (RRFs) determined from the standards were used directly for quantifying the corresponding target analytes in samples, while the RRFs averaged from two adjacent fatty acids were used for quantifying the intermediate fatty acids. The RRF determined from 1-C18:1 was used to quantify all monoacylgycerides in the two real samples as no other monoacylglyceride standards were available. Similarly, the RRFs from the chromatographically adjacent sterols were used for quantifying sterols without authenticated standards.
2.0
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Bmix
1.0 Ban
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.5
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M/S Fig. 1. Cross-plots of the double ratios of SDIFAME/SSFAME (D/S) and SDIFAME/ SMFAME (D/M) versus SMFAME/SSFAME (M/S) for biodiesels with known sources.
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D/S and D/M ratios were less than those of Bcan and Bsoy samples, this character distinguished all animal based biodiesels from pure vegetable oil based ones. For Bsoy and Bcan samples, although the D/ S ratios of Bcan and Bsoy samples were found to be close to each other (2.5–3.9), Bcan samples were characterized as the high M/S (7.4–11.3) and low D/M (0.18–0.35) ratios, but Bsoy samples were characterized as the high D/M (1.8–2.3), but low M/S (1.4–1.7) ratios compared with those from Bcan. Therefore, it is easy to differentiate Bsoy and Bcan from each other and from Ban and Bmix samples by the cross-plots in Fig. 1. The next purpose is to differentiate Bmix from Ban samples. Among Bmix and Ban samples, the D/S ratios from high to low were ranked as: Bmix-soy (sourced from the mixture of soybean oil and animal fat, 1.45) > Bmix-can (sourced from the mixture of canola oil and animal fat, 0.7) > Ban (0.2), similar trend was observed for D/M ratios, and Bmix-can (2.4) > Bmix-soy (Ban) (1.2–1.4) for M/S ratios. It is obvious that the existence of soybean oil source contributes to relatively higher D/S and D/M ratios compared with those from Bmix-can and Ban samples. The high concentration of saturated FAMEs from animal fat contributes to the relative low ratios of M/S and D/S in pure animal fat sourced biodiesel. These results demonstrate the clear differentiation of two types of Bmix samples from Ban. In conclusion, biodiesels from different sources can be clustered into their own group directly by this method. Except for the purpose of using these ratios to identify biodiesel from different sources, these ratios also show the promising potential for characterizing the degradation degree for spilled biodiesel. This is because the principal drawback of biodiesel against petroleum-based fluids is its vulnerability to oxidation, the susceptibility to oxidation of a biodiesel increases with the number of double bonds found in its composition and their configuration [32]. FAMEs with two and one double bonds will be degraded faster than the saturated homologues. The ratios of D/S, M/S, and D/M will decrease as
time goes by after spill occurs. The degradation degree of spilled fluids can be revealed by comparing these ratios between spilled sample and suspected source. 3.3. Polar compounds in biodiesels 3.3.1. Free sterols in biodiesels As discussed in the previous sections, FAMEs, especially unsaturated ones with methylene-interrupted double bonds, are highly biodegradable. Once biodiesel spills into the environment, most of FAMEs will degrade in several days to 3–4 weeks [33,34], this will cause the alteration of above diagnostic ratios, e.g., D/S, or D/M, resulting in inaccurate forensic identification results. Accordingly, characterization of the more stable free sterols in biodiesels may be a very useful option for source allocation of a spilled biofuel. Phytosterols have been used as the markers of plant oils to assess the contamination of some vegetable oils with cheaper ones [15]. In the present study, free sterols, including phytosterols and zoosterols, have been identified and quantified in the representative biodiesel samples as a complementary parameter for the origin identification of biodiesel. Except for sterol standards listed in Table S2 (Supplementary Materials), the identified and quantified sterols also included campestanol (CAO) and D5avenasterol (AV). The detailed information of GC separation and GC chromatograms of the TMS ether of standards and representative samples are shown in Fig. S3 in Supplementary Materials. The distribution profiles of free sterols in target biodiesels are similar to those that we have reported [19]. In brief, the concentration of cholesterol (CS) in biodiesels with animal fat source is far higher than those with pure vegetable oil source, only trace amounts of CS were detected in Bcan and Bsoy samples, and very low concentration of phytosterols were detected in pure animal fat sourced samples. The phytosterols b-sitosterol (SI) and
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CS/Total sterols Fig. 2. Cross-plots of diagnostic ratios of CA, BR, ST and SI relative to total sterols versus CS relative to total sterols for biodiesel samples with known sources. CS: Cholesterol; CA: Campesterol; BR: Brassicasterol; ST: Stigmasterol and SI: b-stiosterol.
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3.3.2. FFAs, glycerol and monoacylglycerides in biodiesels Similar results have been achieved for the identified and quantified FFAs, glycerol and monoacylglycerides reported in our previous work [19] for all studied biodiesels. In brief, the measured total concentrations of FFAs were found to be in good agreement with the acid values measured by the ASTM standard titration method. The main FFAs were identified as oleic acid (cis-9, C18:1), stearic acid (C18:0), linoleic acid (cis-9, 12, C18:2), and palmitic acid (C16:0). The distribution of glycerol and MGs, as well as FFAs showed source specificity. These results suggest that FFAs, glycerol and MGs have great potential as target compounds for forensic identification of biodiesels and for the future research on the degradation of biodiesels. Unfortunately, different products may present different levels of glycerol and MGs, as they are main byproducts of biodiesel. The detrimental components of free fatty acids, either carried over from the feedstock or produced as the degradation intermediates of fatty acid esters during the storage, transportation and usage of biodiesel, are also varied with feedstock properties and storage, transportation conditions. In conclusion, their distribution profiles would partly exhibit source specification but contents vary randomly, which suggests that they are more suitable for distinguishing samples from same source but with different producing batches. Therefore, only diagnostic ratios of free sterols would be discussed in the following section for identifying biodiesel with different sources. 3.4. Diagnostic ratios of sterols for forensic identification of specific biodiesels Because sterols showed feedstock specificity, various diagnostic ratios based on the major source specific sterols (CS, BR, CA, SI and ST) have been calculated and evaluated to directly clarify their source specificity. For example, the ratios between CS and BR, CS/ (1 + BR), or CS/total phytosterols (ratios between major sterols and the total phytosterols are listed in Table S3 in the Supplementary Materials). The cross-plots of these ratios did not show their obvious clustering effects. However, the cross-plots of the ratios of individual major sterols to total sterols (expressed as CS, BR, CA, ST or SI)/total sterols) versus the ratios of CS/total sterols can cluster biodiesels into different groups based on their feedstock sources (Fig. 2). It can be seen that CS was dominant with an average ratio of higher than 0.94 in pure animal sourced biodiesels, followed by Bmix with a ratio ranging from 0.37 to 0.55. However, this ratio was less than 0.01 in any vegetable oil sourced biodiesels. All other phytosterol ratios for animal based biodiesels were far less than vegetable oil based ones. Accordingly, the CS ratio can tell the difference among biodiesels from pure animal fat, mixture of animal fat and vegetable oil and pure vegetable oil source. BR ratio (from 0.03 to 0.11) was only significant in Bmix-can and Bcan samples due to the contribution from canola oil source. Similarly, soybean oil based samples (Bsoy and Bmix-soy) showed specific high ST ratios due to the contribution from soybean oil source. It is noted that BR ratio for Bmix-can and ST ratios for Bmix-soy were less than those in the corresponding pure vegetable oil based samples. Although Bsoy and Bcan samples cannot be discriminated from each other depending on CA and SI ratios, both of them are significant in
all samples from pure vegetable oil because CA and SI are two major phytosterols in these two feedstocks. In conclusion, BR and ST ratios combined with CS ratios can be used for source identification of biodiesels with various sources. These sourcespecified free sterols suggest the possibility of using them as complimentary tracers for forensic identification of biodiesel and its blends. For example, medium to high CS ratio indicates the contribution of animal fat, low CS ratio indicates the vegetable oil contribution. Significant ST ratios suggest the contribution of soybean oil, and significant BR ratios mean the canola oil source. In summary, diagnostic ratios of M/S, D/S, D/M, CS/total sterols and four phytosterols (BR, CA, ST and SI) to total sterols can be used for forensic identification of spilled biodiesel. However, some diagnostic ratios are heavily affected by measurement errors, thus a proper variable selection is important to keep the uncertainties to minimum and yield reliable results. Diagnostic power (DP), defined as the relative standard derivation of a diagnostic ratio in oils with different sources (RSDv) divided by relative sampling standard derivation (RSDs) by Christensen et al. [9,35] was used to perform variable-outlier detection for proper selection of diagnostic ratios in the present study. The obtained RSDv, RSDs and DP values are listed in Table S4 in the Supplementary Materials. It was found all DP values are higher than 1, this suggests that these selected FAMEs and sterols ratios are not affected by large experimental errors or weathering, so all of them can be selected as estimation parameters for identifying spilled biodiesel. It is noted that only limited types of biodiesel feedstocks have been present in the present study because these types have been
FAME concentration (% mass/massTSEM)
campesterol (CA) are the most abundant sterols found in all Bsoy and Bcan samples. No significant discrepancy was found for SI and CA in all these biodiesel samples. Significant concentrations of brassicasterol (BR) were only found in Bcan or Bmix-can samples. Similarly, stigmasterol (ST) is especially significant in any soybean oil or the mixture of soybean oil and animal fat based samples. These differences in the distribution of sterols showed a potential for clarifying the sources of biodiesels due to the relative stability of sterols compared to FAMEs.
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Identified sterols Fig. 3. Comparison of the TSEM normalized major FAMEs and sterols among the suspected source, spilled, Ban, Bmix-can, and Bmix-soy samples.
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
widely found in the market of the North America. However, many different feedstocks have been found as the source of biodiesels, such as palm oil, rapeseed, and coconut oil. Actually, canola oil is a modified rapeseed oil by removing erucic acid due to its adverse effect on human health, therefore, rapeseed oil based biodiesel can be clustered into canola oil group based on the above cross-plots. For biodiesels from palm oil or coconut oil, both of them are characterized as high levels of saturated fatty acids (up to 70–90%), this results in both of them falling into the Ban group by crossplotting of D/S versus M/S or D/M versus M/S. However, the low concentration of cholesterol in both feedstocks makes them separate from Ban group. Accordingly, the above two series of cross-plots combined together can distinguish biodiesels from different feedstocks, regardless of the limited types of biodiesel feedstocks presented in this study. 3.5. Method application for forensic identification of a mystery biodiesel spill
it can be qualitatively concluded that the two real samples are dominant with C16:0 ME, followed by cis-9, C18:1 ME, then C18:0 ME, methyl myristate (C14:0 ME) and cis-9, 12, C18:2 ME. Their FAME distribution profiles are significantly different from Bcan. Similar distribution profiles of glycerol, MG congeners, FFAs and sterols were observed for both of them regardless of the difference of the absolute abundance (Fig. S5). In detail, phytosterols (SI, CA and BR) are dominant in Bcan, however, only CS is significant in the two real samples. All above qualitative results primarily indicated the two real samples may be originated from animal fat. Fig. 3 depicts the comparison of the compositional profiles of major FAMEs and free sterols among these two real samples, Bmixsoy, Bmix-can and Ban samples. It is noted that FAMEs with mass percentage of less than 1% in all above samples are not available here. All data discussed here are expressed on the total solvent extractable material (TSEM) basis (in mg/g TSEM or in mg/g TSEM) rather than on the sample weight or extracted volume basis. TSEM provides an equal basis for forensic comparison of the relative chemical composition of FAMEs, and other target polar compounds between spill and suspected source samples. It is only by this way that comparison of the quantified results between samples is meaningful. In brief, a very similar FAME distribution profile pattern for the spilled and suspected samples was observed although the FAME concentration in the spilled samples was slightly lower than the suspected one. Paired t-test showed that all p-values were higher than 0.05 for the majority (>1% mass/mass) FAMEs, indicating a very high degree of similarity in feedstock for both samples. The
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3.5.1. FAMEs and sterols distribution in two real samples One spilled sample and one suspected source were sampled from St. Lawrence River in December, 2009 for the forensic identification of the spilled sample and the evaluation of the present developed method. The physical properties and the primary GC/MS full scan of the two real samples indicate that they are pure biodiesel. The GC/MS chromatograms of FAMEs for the two real samples and one Bcan are shown in Fig. S4. From Fig. S4
91
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0 :1 :2 C20:0 C22:0 C24:0 C14:0 C16:0 C18:0 , C18 , C18 cis-9cis-9, 12
FFA congeners
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M/S Fig. 4. Comparison of the TSEM normalized glycerol, monoacylglycerides (MGs) and free fatty acids (FFAs) between the suspected source and spilled samples. The detailed abbreviations for MGs are listed in Table S5. For example, 2-C16:0 represents b-glyceryl palmitate; 1-C18:2 represents a-glyceryl linoleate.
Fig. 5. Clustering the real samples using cross-plots of the double ratios of SDIFAME/SSFAME, and SDIFAME/SMFAME versus SMFAME/SSFAME for all studied biodiesel samples.
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
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distribution of FAME compounds are complex with many geometries and saturation (straight, iso, anteiso-methyl-branched chains) homologues, suggesting the contribution from animal fat feedstock. They can be primarily clustered into Bmix-can or Ban only based on the FAME distribution profile. The total FAMEs concentration in the spilled and suspected source samples was determined to be 58.9% and 91.2% (mass/mass), respectively, the total FAME content in the spilled sample is 84% after normalized to TSEM. The possible reasons for the discrepancy of the normalized FAMEs concentration in the two real samples can be partially ascribed to the following factors: (1) the suspected source was not directly responsible for the spilled sample, (2) weathering process, for examples, dispersion, photo-degradation or biodegradation, resulting in the relative low concentration of FAMEs in the spilled sample. However, the spilled sample was collected once the spill occurred, some poly-, and di-unsaturated FAMEs were detected in the spilled sample although they were more vulnerable to be oxidized (Fig. 3). All these information suggest that the weathering process is not the controlling factor for the discrepancy of the FAME concentrations between these two samples. This also demonstrates that the spilled sample may not be subjected to the direct discharge of the suspected source. For sterols, CS is the most abundant free sterol in both samples, with an average concentration of 272 and 3598 mg/g TSEM in the suspected source and spilled sample, respectively (Fig. 3). CA, ST, and BR were detected with a very low concentration in the two real samples. The abundant CS in these two samples primarily indicates that both of them were made from animal fat, at least mainly from animal fat. Reverse to the FAME concentration, the sterol concentrations in the spilled sample are consistently higher than those in the suspected source. For example, CS in the spilled sample was more than 10 times of the suspected sample (Fig. S6).
The comparison of sterols from these two real samples with Ban and two types of Bmix samples indicates that the measured CS in the suspected biodiesel was quite low, followed by Bmix-can and Bmix-soy, then the spilled sample and Ban (Fig. 3). CA, BR and ST in Bmix samples are generally higher than those in Ban, the suspected and spilled samples. As the spilled sample was found to be the mixture of biodiesel and sandy sediment, the reason for the abundant CS in the spilled sample can be ascribed to the following possibilities: (1), the suspected sample was not directly responsible for the spilled samples, i.e., they were sourced from same feedstock, but different producing batches; (2), other high input of CS into the sampling area except for biodiesel. It is well known that CS is ubiquitous in the environmental matrix, especially in sediment, due to the contribution of cholesterol synthesized by freshwater organisms such as algae, phytoplankton and macrophytes [36,37] or from anthropogenic sources such as agricultural runoff and sewage disposal [38]. Therefore, the further identification is necessary to exclude any of the above two possibilities. 3.5.2. FFAs, glycerol and monoacylglycerides distribution profiles in two real samples FFAs, glycerol and MGs, as the complementary parameters, can be used to identify biodiesels with different feedstocks or with same feedstock but different producing batches. So the above target analytes in the two real samples were identified and quantified (Fig. 4). For MG congeners, the abbreviated names are used in Fig. 4 (please see the detailed abbreviations listed in Table S5.). It is obvious that a-glyceryl palmitate (1-C16:0) and a-glyceryl stearate (1-C18:0) are dominant in both samples, followed by a-glyceryl oleate (1-C18:1), then glycerol, despite the measured concentrations in the spilled sample are significantly higher than those in the suspected sample. For FFAs, C16:0 is the most abundant component, followed by cis-9, 12, C18:2 and C18:0, then myristic acid (C14:0)
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CS/Total sterols Fig. 6. Clustering the real samples using cross-plots of diagnostic ratios of CA, BR, ST and SI relative to total sterols versus CS relative to total sterols for all studied samples. CS: Cholesterol; CA: Campesterol; BR: Brassicasterol; ST: Stigmasterol and SI: b-stiosterol.
Z. Yang et al. / Forensic Science International 234 (2014) 86–94
and cis-9, C18:1. Similarly, the measured FFA concentrations in the spilled sample are significantly high than those in the suspected one. Based on the above mentioned results from the polar fraction, it can be concluded that all the determined polar targets showed significant discrepancy between the two real samples. As the discussion in the previous paragraph, environmental matrix could contribute CS to the spilled sample except for biodiesel, but not for FFAs, glycerol and MG congeners. They should be similar to each other because biodiesel was the only dominant contributor to them if both samples were from same source. Therefore, the significantly high CS in the spilled sample was not contributed from the environmental matrix but from biodiesel itself, the suspected sample was not directly responsible for the spilled one. 3.5.3. Source identification of two real samples Just as the description in Section 3.2, the diagnostic ratios of FAMEs based on saturation degree are good indicators for the forensic identification of biodiesels from different sources. The cross-plots of diagnostic ratios of D/S versus M/S and D/M versus D/S of the two real samples are exhibited (Fig. 5) to investigate the possible source and the relationship between the two real samples. It can be seen that all ratios are located in the area of Ban samples, all Bmix samples can be clearly differentiated from both of them. Therefore, both samples were originated from animal fat. Similarly, the cross-plots of the diagnostic ratios of CS, BR, ST and SI relative to the total sterols versus CS/total sterols for the two real samples are present in Fig. 6 for oil matching of the two real samples, as we have verified the measured CS in the spilled sample was from the spilled biodiesel itself. It can be seen that the ratios of CS (close to 1) and all phytosterol clustered both of them into Ban group and separated them from the other sourced biodiesels clearly. Based on the above full chemical fingerprinting and the corresponding cross-plotting of diagnostic ratios for the two real samples, it can be concluded as follows: (1), the two real samples were sourced from animal fat; (2) the suspected source was not directly responsible for the spilled biodiesel, they may be produced from different batches. 4. Conclusions A biodiesel forensic identification method based on the full chemical fingerprinting of biodiesel and diagnostic ratios of fatty acid methyl esters (FAMEs) and source specific free sterols was developed and validated. The cross-plots of FAME ratios based on the saturated degree and the relative sterol contents versus cholesterol can cluster biodiesels into different feedstocks. The distribution profiles of FFAs, glycerol and MG congeners can act as the complementary parameters for the source identification of spilled biodiesels. The spilled case study suggested that the two real samples were clustered into animal fat source but with different producing batches. Acknowledgements Funding for this work was provided by Natural Resources Canada through the Program for Energy Research and Development (PERD), the National Natural Science Foundation of China (No.41373133), and the Scientific Fund from State Ethnic Affairs Commission of China (12ZNZ003). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.forsciint.2013. 11.001.
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