Validation of an automated screening method for persistent organic contaminants in fats and oils by GC × GC-ToFMS

Food Chemistry 211 (2016) 645–653

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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Analytical Methods

Validation of an automated screening method for persistent organic contaminants in fats and oils by GC
GC-ToFMS Patricia López ⇑, Marc Tienstra, Arjen Lommen, Hans G.J. Mol RIKILT – Wageningen UR, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands

a r t i c l e

i n f o

Article history: Received 30 October 2015 Received in revised form 31 January 2016 Accepted 6 May 2016 Available online 7 May 2016 Keywords: Qualitative analysis Screening Validation GC
GC-ToFMS Fat

a b s t r a c t An screening method, comprised of straightforward sample treatment based on silica clean-up, GC
GCToFMS detection and automated data processing with the non-proprietary free downloadable software MetAlignID, has been successfully validated with respect to false negatives for the sum PCB 28, 52, 101, 138, 153 and 180), for the sum of BDE 28, 47, 99, 100, 153, 154 and 183, for the four markers of PAHs and for a number of emerging brominated flame retardants. A screening detection limit (SDL) equal to or lower than the maximum regulatory level was always achieved. MetAlignID considerably decreased the time needed for data treatment from 20 to 5 min/file. Automated identification of the signature mass spectral patterns was applied to identify chlorinatedand brominated-containing substances with more than two halogen atoms, and PAH derivates. Although the success rate was variable and needs to be further improved, the tool was considered to be of added value. Ó 2016 Published by Elsevier Ltd.

1. Introduction The rearing of healthy livestock and safe products of animal origin is highly dependent on the provision of high quality and safe feeds. Animal feed and feed ingredients are challenging matrices for determination of undesirable substances such as organic contaminants due to the enormous variability in matrix nature. Feed ingredients may range from relatively simple matrices like wheat to compound feeds and all kinds of by-products from agro and food industry (EC, 2011a). The analytical methods that are currently applied at different levels within the feed chain typically involve targeted approaches for a certain class of contaminants. These approaches fail in case of some unforeseen problems, like the melamine crisis (Yang, Huang, Zhang, Thomas, & Pei, 2009) and several incidents of contamination with dioxins/ polychlorinated biphenyls (PCBs) (Alcoser, Velthuis, Hoogenboom, & van der Fels-Klerx, 2011; Bernard et al., 2002). PCBs at high levels and polycyclic aromatic hydrocarbons (PAHs) have been reported in feed ingredients of animal origin and with high fiber content, respectively (YebraPimentel, Fernandez-Gonzalez, Carballo, & Simal-Gandara, 2012). Moreover, oils, fats and various by-products, of which some are not allowed to be used as feed fat ingredients, have been identified as potential source of feed contamination with persistent bioaccu⇑ Corresponding author. E-mail address: [email protected] (P. López). http://dx.doi.org/10.1016/j.foodchem.2016.05.041 0308-8146/Ó 2016 Published by Elsevier Ltd.

mulative substances (EC, 2012a), which calls for intensified control on the presence of these contaminants. Ideally, such control would not only include regulated contaminants, but also well-known and new potential contaminants, such as polybrominated diphenyl ethers (PBDEs) (EFSA, 2011) and emerging flame retardants (EFSA, 2012). Their presence should be detected at an early stage, both in time and in the feed chain, to contribute to the prevention of new feed/food crises. This requires analytical methods that can simultaneously detect contaminants with different toxicological end points at low levels. One of the strategies to efficiently achieve this requirement is the use of chemical screening methods, which will allow flagging suspicious samples that, after appropriate confirmation of the presence of the contaminant, will be removed from the feed chain. Multidimensional gas chromatography combined with time-offlight mass spectrometry (GC
GC-ToFMS) has a great potential for efficient comprehensive full scan screening of multiple classes of contaminants (Dasgupta et al., 2010; Hashimoto et al., 2011; Pena-Abaurrea, Covaci, & Ramos, 2011), and untargeted fingerprinting (Almstetter, Oefner, & Dettmer, 2012; Reichenbach, Tian, Cordero, & Tao, 2012), especially when combined with fast and straightforward sample preparation approaches. Extraction based on an one-step sample extraction combined with wateracetonitrile partitioning (QuEChERS) is a method that has been reported for simultaneous extraction of PCBs, PAHs and pesticides (Dasgupta et al., 2010; Mol, van der Kamp, van der Weg, van der Lee, Punt, & de Rijk, 2011). Sapozhnikova et al. has recently

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published a multi-class, multi-residue method that included emerging brominated flame retardants (nBFRs) using this method (Sapozhnikova & Lehotay, 2013). However, this approach fails for matrices with substantial fat content. In that case more tedious, more time-consuming and less environmental-friendly sample treatments, such as gel permeation chromatography (GPC), have to be used to clean out the fat (triglycerides) and to reduce the number of matrix interferences (Hoh et al., 2012; van der Lee, Van der Weg, Traag, & Mol, 2008). A simplified and faster alternative based on ethyl acetate extraction and silica-column clean-up has been described by Kalachova, Pulkrabova, Drabova, Cajka, Kocourek, and Hajslova (2011), which was successfully applied for the simultaneous determination of PCBs, PBDEs and PAHs using GC
GC-ToF-MS in fish muscle containing up to 10% fat (Kalachova, Pulkrabova, Cajka, Drabova, & Hajslova, 2012). Due to the complexity and the amount of data obtained after GC
GC-ToFMS analysis, data handling is a major challenge, which has to be adequately addressed in order to obtain an efficient screening method. The data analysis procedures usually involve different mathematical tools to deal with noise and instrumental fluctuations, to deconvolute overlapping peaks, and to identify these deconvoluted peaks based on mass spectral matching. The LECO GC
GC-ToFMS software ChromaTOFÒ has fully integrated all these data processes (Hilton, Jones, & Sjodin, 2010). Despite the continuous improvements of the software, the processing speed and the unease to update some relevant parameters for automated detection and semi-quantification prevent this type of technology from being used for routine screening applications. In addition, other serious failures in analyte detection (i.e. no peak detection in case of detector saturation) have been reported. A number of pitfalls are also encountered in the software-hardware interaction, as described elsewhere (Lommen et al., 2012). In the past alternative freely available software packages, such as metAlign (Lommen, 2009; Lommen & Kools, 2012), have been successfully applied in the field of metabolomics (Lommen, van der Weg, van Engelen, Bor, Hoogenboom, & Nielen, 2007; RuizAracama, Lommen, Huber, Van De Vijver, & Hoogenboom, 2012). Using metAlign algorithms as a basis for GC
GC-ToFMS data handling, a software package called MetAlignID was recently developed to overcome the encountered practical limitations in the field of residues and contaminants analysis (Lommen et al., 2012). European guidelines for the validation of screening methods based on chromatography and full-scan MS have been established for pesticides (SANCO, 2013), veterinary drugs (Stolker, 2012), and mycotoxins (EC, 2014). However, with some exceptions (Mol, Reynolds, Fussell, & Stajnbaher, 2012; Mol, Zomer, & de Koning, 2012; Mol et al., 2011), validation of screening methods following these protocols has not been extensively reported. The aim of the present work was i) to adapt the fast sample clean-up method described by Kalachova et al. (2011) for animal fats, vegetable oils and by-products thereof, b) to investigate the applicability of GC
GC-TOF-MS for selective detection of various persistent contaminant classes in these matrices, c) to validate the fully automated screening method for detection of non-dioxin-like PCBs, PBDEs, PAHs and nBFRs, and d) to investigate the additional non-target detection of chlorine-/bromine-containing substances and PAH-derivates through their mass spectral signatures.

2. Experimental 2.1. Chemicals and reagents Dichloromethane (DCM), n-hexane (nHex) and iso-octane were purchased from Biosolve (Valkenswaard, the Netherlands). Silica gel (0.040-0.063 mm), which was supplied by Merck (Darmstadt,

Germany), was activated by heating at 200 °C overnight and deactivated with either 2% (w/w) or 5% (w/w) of deionized water by shaking for 3 h and storing it in a desiccator for at least 16 h before use. Silica cartridges 5 g/25 mL and 10 g/25 mL were purchased from Alltech/Applied Science BV (Breda, The Netherlands). Certified individual standards of 2,3,4,5,6-pentabromoethylben zene (PBEB), tetrabromo-p-xylene (TBX), 2,3,4,5,6pentabromotoluene (PBT) and 1,2-bis(2,4,6-tribromophenoxy)eth ane (BTBPE) were supplied by Greyhound Chromatography (Merseyside, UK). Hexabromobenzene (HBB) and a mixture of PBDE congeners (#17, 28, 47, 49, 66, 71, 75, 77, 85, 99, 100, 119, 138, 153, 154, 183, 190) were purchased from Chiron AS (Trondheim, Norway). Tebrabromo-o-chlorotoluene (TBoCT) was purchased from Wellington Laboratories (Guelph, Ontario, Canada). PAH and PCB certified mixtures were supplied by from Dr. Ehrenstorfer GmbH (Augsburg, Germany). The PAH mixture contained the 15 + 1 EU priority PAHs: 5-methylchrysene (5MC), benz[a]anthracene (BaA), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[j]fluoranthene (BjF), benzo[c]fluorene (BcL), benzo[a]pyrene (BaP), benzo[ghi]perylene (BgP), chrysene (CHR), dibenzo[a,e]pyrene (DeP), dibenzo[a,h]pyrene (DhP), dibenzo[a,i]pyrene (DiP), dibenzo[a,l]pyrene (DlP) and indeno[1,2,3-cd]pyrene (IcP). The PCB mixture contained the congeners #28, 52, 101, 118, 138, 153, 180. PCB198, which was supplied by Dr. Ehrenstorfer, was used as internal standard. Mixture stock solutions, working solutions and calibration solutions were prepared in iso-octane and stored at dark at 4 °C. 2.2. Samples The following fifteen different fatty matrices that might be used as feed ingredients were selected: M01 rapeseed oil, M02 palm fatty acid distillate (PFAD), M03 lamb tallow fat, M04 coconut oil, M05 fish oil, M06 soybean oil, M07 palm oil, M08 sunflower oil, M09 PFAD bottom tank oil, M10 basic vegetable blend oil, M11 acid oil, M12 hydrogenated palm oil, M13 residue PriplusÒ (residue of PFAD), M14 oil extracted from leaching earth, and M15 frying oil. Some of these matrices, like M09 and M15, are not included in the catalogue of feed materials listed in Commission Regulation 575/2011 (EC, 2011a), but might be applied in cases of frauds and/or adulteration. 2.3. Selection of analytes The following contaminants were selected for this study: nondioxin like PCBs (ndl-PCBs), PBDEs, nBFRs and PAHs. These contaminant classes consist of many individual congeners, however, for the screening they are detected through the most relevant marker substances. The European Food Safety Authority (EFSA) (EFSA, 2011) established eight PBDEs congeners (BDE 28, 47, 99, 100, 153, 154, 183 and 209) as markers for dietary PBDE exposure, while the sum of six PCBs congeners (PCB 28, 52, 101, 138, 153 and 180) represents around 50% of total PCBs in food (EFSA, 2005). BDE 209 is not very sensitive to electron impact ionization mode; hence it was discarded for the screening analysis. Maximum levels for the content of the sum of ndl-PCBs have been recently established by the Commission Regulation 277/2012, which range from 10 lg/kg in feed materials of plant origin to 175 lg/kg in fish oil (EC, 2012b). On the other hand, no specific legislation that limits the levels of PBDEs either in feed or food is available yet.nBFRs are defined as chemicals that are applied as flame retardants and that have been identified as anthropogenic chemicals in any environmental compartment, in wildlife, in food or in humans. Recently EFSA has published a scientific opinion, where the lack of experimental data on physical-chemical characteristics and stability/reactivity for all

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nBFRs as well as the lack of specific analytical methods for their identification were highlighted. This Opinion identified BTBPE and HBB as compounds that could raise a concern for bioaccumulation (EFSA, 2012). PAHs constitute a large class of organic compounds that are composed of two or more fused aromatic rings. EFSA came to the conclusion in 2008 that a group of 4 PAHs, which is composed of BaA, BaP, BbF and CHR, was the suitable marker to monitor the occurrence of PAHs in food (EFSA, 2008). Hence the European Commission set maximum levels for the content of BaP and the sum of these 4 PAHs in different types of foodstuff in the Commission Regulation No 835/2011 (EC, 2011b). As there is no regulation about the maximum levels of PAHs in feed ingredients, the legislation on food was taken as guidance. 2.4. Scale-up experiments In a first series of experiments, both manually-prepared and commercial cartridges of silica were evaluated. Two batches of bulk silica with different degrees of deactivation (2% and 5% w/w), and therefore different retention capabilities, were tested. Rapeseed oil, fish oil, avian fat and palm oil were spiked with PCBs, PBDEs and PAHs at around 50–100 lg/kg and loaded on the silica cartridge, which had been previously conditioned as described in the Experimental section. The eluate was collected as 10 fractions of 5 mL. The fractions, to which 0.5 mL of iso-octane had been added as keeper, were evaporated to 0.5 mL under a N2 stream and then analyzed by GCMS in SIM mode. From the results the elution profile of the target analytes was determined. In addition, retention time shifts, due to co-extracted matrix, were also monitored. Based on these data, the elution fraction was chosen. In a separate experiment, the amount of fat remaining in optimized fraction (with respect to recovery and removal of triglycerides) was gravimetrically determined. 2.5. Sample preparation The final clean-up procedure applied was as follows: 1 g of sample, diluted in 4 mL of n-Hex and spiked with 0.1 mL of PCB 198 (concentration around 200 ng/mL) as internal standard, was loaded on a column containing 10 g silica-gel deactivated at 2% (w/w) with water. The silica-gel column had been previously conditioned with 60 mL of a mixture n-Hex:DCM (3:1, v/v) and 30 mL of n-Hex. The sample was eluted with 35 mL of the mixture n-Hex:DCM (3:1, v/v). The extract was evaporated till 0.5 mL in a TurboVapÒ (Biotage, Uppsala, Sweden) and then further evaporated till 0.2 mL with solvent exchange into iso-octane under a gentle nitrogen stream. The test tube was rinsed with iso-octane to obtain a final volume of 0.5 mL, which were transferred into a GC-autosampler vial. 2.6. GC–MS analysis For evaluation of the scaling-up of the clean-up method an Agilent (Agilent Technologies Netherlands BV, Amstelveen, Then Netherlands) gas chromatograph 6890A mass selective detector MSD 5973N system was used. 10 lL of the extract were injected on a Rtx-5MS 15 m
0.25 mm I.D., 0.1 lm (Restek, Breda, The Netherlands). Helium was used as carrier gas at constant flow of 1.0 mL/min. The PTV temperature program was as follows: 60 °C for 0.5 min, then raised with 600 °C/min to 280 °C and kept for 10 min, then raised with 10 °C/min to 340 °C and kept for 25 min, and finally dropped with 70 °C/min to 70 °C. The vent time was 0.10 s, the vent pressure 50 kPa and the vent flow 100 mL/min. The injection speed was 5 lL/s and the splitless time was 120 s. The oven tem-

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perature program was as follows: 70 °C kept for 2 min, then raised at 20 °C/min up to 150 °C, raised at 3 °C/min up to 250 °C and finally raised at 7 °C/min to 300 °C and kept for 5 min. The transfer line temperature was set at 300 °C. The MS operated in single ion monitoring (SIM) mode with the quadrupole and the ion source temperature set at 150 °C and 230 °C, respectively. 2.7. GC
GC-ToFMS analysis For GC
GC–ToFMS analysis of the extracts, a Pegasus-4D system (LECO, St. Joseph, MI, USA) equipped with a CIS-4 PTV injector (Gerstel, Mülheim a/d Ruhr, Germany) was used. A two-stage modulator facilitated comprehensive gas chromatography. Liquid nitrogen used for cold pulses was automatically filled into a Dewar using a liquid leveller. In the first dimension a Restek column RxiÒ17Sil MS 30 m
0.25 mm I.D., 0.25 lm was used. The second dimension column was also a Restek column RxiÒ-5Sil-MS 1 m
0.18 mm, 0.18 lm, mounted in a separate oven installed within the main GC oven. Helium was used as carrier gas at constant flow of 1.3 mL/min. The PTV temperature program was as follows: 60 °C for 0.5 min, then raised with 10 °C/s to 280 °C and kept for 25 min and finally raised with 0.3 °C/s at 340 °C and kept for 12 min. The injection speed was 2.20 lL/s. The splitless time was 180 s. The temperature program of the first column was as follows: 70 °C kept for 2 min, then raised at 20 °C/min up to 230 °C, raised at 2 °C/min up to 290 °C and finally raised at 3 °C/min to 340 °C. The temperature of the second oven was programmed with an offset of 10 °C and the modulator temperature offset was 40 °C relative to the first GC oven temperature. The second-dimension separation time (modulation time) was 3 s divided into a hot pulse time of 0.80 s and a cold pulse time between the stages of 0.70 s. The transfer line from the secondary oven into the mass spectrometer was maintained at 280 °C. The ion source was operated at 250 °C. The electron energy was 70 eV. The detector voltage was set at 1700 V. The data acquisition rate was 200scans/s, covering a mass range of 50–750 m/z. 2.8. Design of the validation procedure The guideline for screening methods of mycotoxins described in European Regulation 519/2014 was taken as reference (EC, 2014). The design of the validation protocol, therefore, was in line with these principles: assessment of the number false negatives in spiked samples at 95% confidence. In addition, the number of false positives was assessed for blank samples. Although no strict criterion applied for this, a high false positive rate makes a screening method less effective and economical. Therefore, the fifteen feed ingredients were analyzed in duplicate as such (level 0) and at three different spiking levels (level 1, level 2 and level 3). Level 0 corresponded to the ‘‘blank” samples; level 2 corresponded to the maximum legislative level, if available; level 1 and level 3 were respectively half and twice level 2 (see Table 1). As far as PCBs were concerned, the lowest legislative limit of 10 lg/kg in feed materials (EC, 2012b) of plant, mineral or animal origin for the sum of PCB28, 52, 101, 138, 153 and 180 (PCB6) was assigned as level 2. As for PAHs, level 2 was counted as the lowest limit of 1 lg/kg for BaP in some foodstuffs, while level 2 for the sum of PAH4 corresponded to 8 lg/kg (EC, 2011b). Given the fact that the PAHs were supplied as a common solution containing the 15 + 1 EU priority PAHs at the same concentration, the lowest legislative limit of 10 lg/kg was not feasible to prepare. As there is no legislation that restricts the content of either PBDE or nBFR in feed or food material, the concentration at level 2 was designed based on their detectability by GC
GC-ToFMS: 35 lg/kg for the sum of PBDEs and 15 lg/kg for each nBFRs.

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Table 1 Final concentrations (lg/kg) of each contaminant as well as the sum of markers for dietary exposure in the different feed-fat matrices at the different levels during the validation experiment.

Level Level Level Level a b c d

0 1 2 3

PCBi

a

– 0.83 1.67 3.33

– 5 10 20

PCB6

BaP

b

PBDEi

c

– 1 2 4

– 4 8 16

– 2.5 5 70

– 17.5 35 70

PAH4

PBDE7

Table 2 Summary of the silica scale-up experiments. Water content (%)

Silica 0.040-0.063 mm, 230–400 mesh ASTM

Silica AlltechÒ cartridge 1% (<5%)

d

nBFRi

– 7.5 15 30

PCB6 = R(PCB28 + PCB52 + PCB101 + PCB138 + PCB153 + PCB180). PAH4 = R(BaA + BbF + BaP + CHR). PBDE7 = R(BDE28 + BDE47 + BDE99 + BDE100 + BDE153 + BDE154 + BDE183). HBB twice as high in concentration.

Since the method was to be used as a screening method, the emphasis was on detectability. Therefore no requirements with regard to linearity and recovery were set.

2%

5%

Mass silica (g) Bed length (cm) Bed width (cm) Loading fata (mg)

10 7 2 1000

5 8 1.5 500

10 7 2 1000

5 4 2 500

10 7 2 1000

Final elution volume (mL) Hex:DCM (3:1, v/v) Elution PAHs (mL) Elution PCBs (mL) Elution PBDEs (mL)

35

22.5

45

22.5

65

30–45 25–30 35–40

22.5 15–17.5 20

40–45 30 30–35

22.5 17.5 17.5–20

65–70 45–50 45–50

Recoveryb (%) Fat removal (mg)

80–120% 84–110% 60–120% 78–110% 85–115% 998 469 933 500 1000

a Experiments performed with vegetable oil (rapeseed, coconut, palm, soybean), fish oil and animal fat. b Based on the final elution volume.

2.9. Data processing For automated spectrum-based detection of contaminants MetAlignID (http://www.wageningenur.nl/nl/show/MetAlign. htm) was used (Lommen, 2009). Basically, the LECO raw data files (.peg) were converted into .netCDF (network Common Data Form) format using the fast conversion tool in the metAlignID software package, NetCDF files were then pre-processed to obtain peakpicked and thereby reduced about 100-fold (Lommen, 2009). Next, the resulting .redms files (reduced ms files) were searched against a user-composed library. The detected compounds, including indicative values of the intensity of the peak based on either a selected ion or on the TIC (total ion chromatogram), were reported in Excel format (Lommen et al., 2012). These Excel tables were used to build the different quality control graphs as well as to calculate the cut-off values for the different groups of analytes. For detection by spectral features, a software module embedded in the LECO ChromaTOF software, was used. 3. Results and discussion 3.1. Sample clean-up Due to the intrinsic characteristics of GC-systems, the direct introduction of fat/oil sample is not feasible since this would entail deposit of high-boiling products in the system resulting in peak broadening and peak distortion (Grob & Bossard, 1984). GPC is the technique commonly used to prevent contamination of the GC system by the fat matrix (Hoh, Lehotay, Mastovska, & Huwe, 2008; Hoh et al., 2012; van der Lee et al., 2008). However, it is very time-consuming and not environmental-friendly due to the huge amount of organic solvents needed. Therefore it was decided to follow an alternative approach based on silica columns as described by Kalachova et al. (2011). This procedure had to be scaled up and adjusted in order to handle the much larger amount of fat (1 g vs 0.1 g) of the targeted samples. A summary of the results of the scaling-up experiments is shown in Table 2. In general, PCBs eluted the first, while PAHs were the most retained compounds. Within the same chemical group, the elution order depended on the molecular weight. Commercial silica cartridges showed a slightly greater capacity to retain fat, which led to broader elution profiles of the target analytes. The deactivation level of commercial cartridges is a parameter that is often not provided by the supplier. Based on these results, the activation of commercial silica cartridges was expected to be lower than 5%, since the lower the silica gel deactivation level, the higher the elution volume needed for PCBs and PAHs (Cahnmann, 1957).

Manually-prepared silica columns were preferred since less volume was needed for the quantitative elution of the analytes. Regardless of the type of fat ingredient, their elution profiles were narrower and they showed much less procedural interferences (Supplementary Information SS1A). Although silica deactivated at 5% showed good performance at removing the matrix compounds from rapeseed or soybean oil, it did not work for other type of matrices such as palm oil. These matrix interferences, which mainly eluted at 20–25 mL, caused shifting in retention times and skewing of the peak shapes (Supplementary information SS1B). By decreasing the degree of deactivation from 5 to 2%, triglycerides were more retained and eluted after 35 mL. A stronger interaction of PAHs with 2%-deactivated silica was as expected, thus showing broader elution profiles. Around 10–20% of some PAHs, such dibenzopyrenes, IcP, BgP and DhA, were eluted in the same fraction (35–40 mL) as some triglycerides. However, as these PAHs are not considered markers by EFSA (2008), it was preferable to obtain cleaner extracts and accept the 10–20% loss of the PAH, which is considered less relevant for the purpose of the present analysis. The clean-up method with 2% of deactivated silica and 35 mL of elution volume was proven to be robust, no deterioration in chromatographic performance in the GC–MS chromatograms was observed after the injection of 20 extracts. Deviations in retention times were lower than ±0.2 min for all target analytes. The clean-up method was demonstrated to be suitable for matrices such as vegetable oils, fish oil and animal fats, but not for PFAD (Supplementary Information SS1C). 3.2. GC
GC-TOF-MS analysis and data processing According to Kalachova et al. (2012), the difficulties in the determination of PCBs, PBDEs and PAHs by GC
GC-ToFMS in a single run arose from the separation of the three PAHs groups: i) BaA, CPP and CHR, ii) BbF, BkF and BjF and iii) DhA, IcP and BgP. The proposed column system, BPX5 (30 m
0.25 mm i.d.
0.25 lm film thicknesses)
BPX50 (1 m
0.1 mm i.d.
0.1 lm film thickness), achieved to resolve these three critical pairs, but without baseline separation. This issue was not especially critical for either BaA/CPP/CHR or DhA/IcP/BgP since they could be discriminated based on their different spectrum in the automated data treatment. The three isomeric benzofluoranthenes on the other hand, have identical mass spectra, which could lead to false hits, biased results and eventually to wrong decisions regarding the compliance of the samples. In this study, a reverse-type

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column combination, based on RxiÒ-17Sil MS (30 m
0.25 mm I.D., 0.25 lm film thicknesses)
RxiÒ-5Sil-MS (1 m
0.18 mm, 0.18 lm film thicknesses), was tested. This system not only accomplished baseline separation of all critical PAHs groups, but was also suited for the incorporation of the emerging brominated flame retardants (see Fig. 1). The separation, identification and accurate quantification of the three benzofluoranthenes isomers was not only one of the main challenges in the analysis of PAHs, but also for automated data treatment. An accurate quantification could be conducted with the developed method by manual integration. However, this study intended to develop a completely automated screening methodology with the application of MetAlignID. As the three isomers have identical spectra, they could only be distinguished by their retention time. During the data treatment it was observed that due to subtle differences in the quantification algorithms of MetAlignID for the TIC (total ion chromatogram) and the quantification mass(es), it was required to update the retention time for each isomer to the actual value from the sequence and to set narrower retention windows in order to avoid false hits. See supplementary information SS2 for more detailed explanation on MetAlignID algorithms for TIC and quantification mass(es). The quality of the spectra is of paramount significance for the identification of hits by MetAlignID. MetAlignID constructs partial experimental spectra that later matches to the library spectra, only if some thresholds are fulfilled. The thresholds had be defined by the user through the functionalities ‘‘Search_GCGCMS_data.exe” and ‘‘Search_GCMS_data.exe” and customized to the specific application in order to reduce the number of false positives and false

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negatives. For more detailed information, see supplementary information SS2. Due to the diverse nature of the feed ingredients as well as the non-exhaustive clean up procedure, detector saturation was observed for some matrices. MetAlignID, unlike ChromaTOFÒ software that is not able to digitize the top of the signal of the overloaded peaks, can address this issue. However, for certain types of matrices MetAlignID also failed due to very extreme saturation problems. This occurred for the matrices M02 (palm fatty acid distillate, PFAD), M13 (residue of PFAD called PriPlusÒ) and M14 (oil extracted from bleaching earths). PFAD is a by-product of physical refining of crude oil products with free fatty acids as main constituent (approx. 82%) followed by triglycerides (14%), squalene (0.8%), vitamin E (0.5%) sterols (0.4%) and other substances. The clean-up procedure failed to sufficiently remove the free fatty acids, which was attributed to the relative higher number of hydroxyl functionalities compared to triglycerides, thereby overloading the silica column. As a consequence, the final extract of M02 still contained a high amount of polar interferences (fatty acids) compared to the extract of palm oil. In case of M14 detector saturation was due to the high amount of fatty-acid methyl esters in this type of matrix (99%). The fatty-acid methyl esters were not retained by the silica column and eluted in the same fraction as the target analytes. As for M13 (PriplusÒ), no information was available about the composition and saturation issues could not readily be attributed to certain matrix compounds. It was concluded that the method was not suited for these matrices and therefore they were rejected from the validation set. MetAlignID considerably reduced the time needed for data treatment. While data processing with ChromaTOFÒ took 17 min/file, MetAligID (on a standard

Fig. 1. 2D-chromatogram showing the separation of the target analytes (de Jong et al., 2016).

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P. López et al. / Food Chemistry 211 (2016) 645–653

quadcore PC) only needed around 5 min, which in overall figures meant a saving of 41 h for the 205 files used for the validation of the screening method. The output from MetAlignID was a .csv file with the information of all the individual hits, which was copied into in-house develTa.ble 3 Percentage of false positives and false negatives at different spiking levels. Screening detection limit (SDL) vs maximum regulatory limit (MRL).

RPCB6 BaP RPAH4 RPBDE7 RBFR6

Control sample %False positivesa

Level 1 %False negativesb

Level 2 %False negativesc

SDL (lg/kg)

MRL (lg/kg)

4.5% 4.5% 4.5% 0% 0%

0% 4.5% 0% 4.5% 0%

0% 0% 4.5% 13.6% 0%

5.0 1.0 4.0 17.5 g 52.5

d

10 2 10 f n.a. f n.a. e e

a (Number of blank samples above cut-off level (level 1)/total number of samples (22)
100). b (Number of samples at level 1 below cut-off (level 1)/total number of samples (22)
100). c (Number of samples at level 2 below cut-off (level 2)/total number of samples (22)
100). d Commission Regulation 272/2012. e Commission Regulation 835/2011. f No Regulation available. g SDL for each nBFR is the concentration corresponding to level 1.

oped Excel macros. The macros were used to calculate by bracketing the concentration of each analyte in every matrix at the different spiking levels, to plot control charts for concentration of analytes and to calculate the cut-off values. The concentration graphs for the other targeted groups/analytes are available in the Supplementary Information SS3.

3.3. Validation of the screening method: cut-off values A multi-residue screening method should be able to reliably detect the analytes at a concentration at which the samples would be categorized as non-compliant. Validation in this case was based on detectability and required the establishment of a cut-off level (EC, 2010, 2014). The cut-off level or threshold is defined as the response or signal that indicates that the sample contains an analyte above certain concentration and therefore is classified as suspect. This concentration should be at least the maximum allowed limit or even lower. If the cut-off level is exceeded, then a quantitative confirmatory test must be carried out. The capability of a screening method to detect samples potentially exceeding the established legal limits is a fundamental performance characteristic. The method is considered valid when the analyte(s) can be detected in the samples with 95% confidence. The lowest level for which this has been demonstrated has been defined as the screening detection limit (SDL) (EC, 2010).

Fig. 2. Cut-off graphs for the different target groups of contaminants.

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The cut-off level, which is defined as the response or signal that indicates that the sample contains an analyte above certain concentration, was estimated as:

Cut-off value ¼ mean þ t-value ðb ¼ 0:05Þ
total standard deviation Cut-off values at one side were calculated for the blank (control) samples and the spiking levels 1 and 2. As mentioned before, matrices M02, M13 and M14 were discarded for data evaluation. Matrix PFAD vegetable tank oil (M09) was also rejected for calculation purposes. Although, the analytes of interest could be correctly identified by MetAlignID, the outcome was not valid for quantification purposes. In addition, the concentration values for BaP and other PAHs were lower than the spiked value, which might be due to the fact that these compounds were more sensitive to matrix effects (data not shown). The percentage of false positives and false negatives at each spiking level is shown in Table 3. In addition, the cut-off values for each class of contaminants can be seen in Fig. 2. As observed, the lowest spiking level (level 1) could be always statistically distinguished from the control matrices. The number of false negatives was always equal to or lower than 5% and therefore the SDL was equal to level 1. At level 2 (legislative limits for PCBs and PAHs), no false negatives in the spiked samples and no false positives in the blanks were obtained. This finding ensured with high confidence that the non-compliant samples would be always flagged for confirmatory analysis. A limitation of the method was to statistically differentiate between spiking level 1 and 2 in certain cases. As for M09, certain type of analyses could be identified, but no differences in concentrations were observed Palm oil (M07), hydrogenated palm oil (M12) and basic vegetable blend oil (M10) exhibited substantial matrix interferences due to for example their high content of squalene, which was not removed during the clean-up procedure. Squalene eluted as a broad peak in the same region as the marker PAHs, PCBs and BDE47. This effect was clearly observed for PAHs, which had a percentage of false positives at level 2 higher than at level 1. The matrix effect observed for M10 and M7 resulted in a decrease of the sensitivity; thus showing similar response at level 2 as the other matrices at level 1. The consequence was that samples contaminated at 50% of the maximum limit could be classified as suspect non-compliant and triggered a confirmatory analysis. In the samples set, this occurred in approximately 40% of the samples contaminated at level 1. Since in real samples the levels are expected to be mostly below 50% of the maximum limit, the actual number of confirmatory analyses needed will remain acceptable, and with that the screening method effective and economical (Lattanzio, von Holst, & Visconti, 2013). 3.4. Semi non-targeted detection based on spectral features The scripting module in the LECO ChromaTOFÒ software is based on user-developed scripts written in MicrosoftÒ VBScripts language and uses some classification rules, as those suggested by Welthagen, Schnelle-Kreis, and Zimmermann (2003). A combination of the scripting/classification module was successfully applied to identify chlorine, bromine and PAHs in household dust (Hilton et al., 2010). Compounds containing chlorine or bromine were identified by matches to empirically derived ranges of isotope ratios or by a match to a theoretically predicted isotope cluster with computed match score. With both techniques, the probable molecular ion is identified in the spectrum. Compounds containing from one to six atoms of chlorine and bromine could be identified and classified. The identification of PAHs followed three strategies: i) selection of those spectra that had a strong abundance of the

molecular ion and the ion M-1, and a relative abundance less than 50% for the rest of the ions; ii) selection of those spectra that had strong molecular ion and might also have strong abundances for losses of 1 (H), 15 (CH3), and/or 29 (C2H5); iii) definition of a region in the 2D chromatogram where PAHs might be expected. These scripts, which can be found in the manuscript by Hilton (Hilton et al., 2010), were applied to detect the spiked chlorinated/brominated compounds and PAH-derivates in the raw data files generated during the validation. Table 4 shows the effectiveness of the scripting data processing to classify and filter the peaks as brominated/chlorinated-containing compounds. The detection was highly affected by the amount of matrix in the extract that survived the clean-up procedure. While the identification as Cl/Br compounds in oil was as high as 85%, it decreased for some of the more complex matrices, such as PFAD. Nevertheless, further experiments demonstrated that these scripts provided a considerably number of false positives hits of compounds containing one halogen atom, thus needing further improvement. Unlike Cl/Br-containing compounds, the accurate identification of PAHs was not so straightforward. Despite the restriction of the area of search more hits than the spiked compounds were filtered (more information in Supplementary Information SS4). Furthermore, the scripting failed to select alkylated PAHs, like 5methylchrysene, and to differentiate among the critical pairs BaA-CHR-CPP and the three isomers of benzofluoranthenes, which were most of the times flagged as one unique compound. Although the success rate in terms of accuracy was variable and needed to be further improved, the tool was considered to be of added value to detect unexpected or unknown Cl/Br containing contaminants, as long as they had three or more halogen atoms.

Table 4 Percentage of the spiked compounds containing Cl/Br or b) PAH-derivates that were identified by scripting data processing of fatty feed ingredients. Level 1

Level 2

Level 3

a) Containing Cl/Br Standards M15 Frying oil M06 Soybean oil M05 Fish oil M08 Sunflower oil M01 Rapeseed oil M04 Coconut oil M07 Palm oil M10 Basic vegetable oil M11 Acid oil M14 Bleaching earths M12 Hydrogenated Palm fat M03 Animal fat M09 PFAD vegetable oil tank M13 Residue Plus M02 PFAD

Matrix

81% 81% 85% 77% 79% 82% 85% 56% 60% 63% 19% 47% 53% 11% 3% 2%

78% 90% 90% 90% 89% 87% 85% 68% 65% 63% 55% 55% 53% 34% 19% 8%

86% 87% 84% 90% 95% 95% 92% 71% 71% 60% 60% 65% 53% 0% 26% 13%

b) PAH-derivate Standards M15 Frying oil M06 Soybean oil M05 Fish oil M08 Sunflower oil M01 Rapeseed oil M04 Coconut oil M07 Palm oil M10 Basic vegetable oil M11 Acid oil M14 Bleaching earths M12 Hydrogenated Palm fat M03 Animal fat M09 PFAD vegetable oil tank M13 Residue Plus M02 PFAD

59% 68% 82% 64% 73% 68% 64% 14% 73% 73% 27% 0% 50% 0% 0% 0%

65% 73% 82% 68% 73% 73% 64% 18% 64% 73% 23% 9% 68% 0% 0% 0%

75% 55% 73% 64% 73% 82% 73% 36% 59% 59% 36% 14% 68% 0% 0% 0%

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4. Conclusions Comprehensive gas chromatography coupled to mass spectrometry was demonstrated to be a useful tool for the early detection of non-conformity of multiple classes of persistent organic contaminants in fat sources used for feed, at a laboratory level. The method presented here comprised of a fast and straightforward sample treatment, GC
GC-ToFMS detection and automated data processing with semi non-targeted detection of chlorinated/ brominated-containing compounds. The method was successfully validated with respect to false negatives for the six PCB markers, the seven PBDEs markers, BaP and the PAH4 markers and for six emerging brominated flame retardants. A screening detection limit (SDL) equal to or lower than the maximum regulatory level was always achieved, which would suffice to test the safety of feed material as regards PCBs or PAHs contamination. The data processing on the basis of scripting in the LECO software package was presented as a promising filter that allowed a rapid identification of chlorinated/brominated compounds in oils of different nature. To the best of our knowledge, this has been the first time that such a straightforward sample treatment in terms of time- and solvent-consumption and easy handling has been applied with success to highly challenging matrices like pure fat and oil. The fact that data were acquired in full-scan mode added a retrospective feature if necessary. Moreover, MetAligID software considerably reduced the data file sizes, which favoured their storing and archiving. Acknowledgments The research leading to these results has been funded by the European Commission 7th Framework Programme, Quality and Safety of Feeds and Food for Europe (QSAFFE FP7-KBBE-2014-4, 265702). 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.foodchem.2016. 05.041. References Alcoser, V. H. L., Velthuis, A. G. J., Hoogenboom, L. A. P., & van der Fels-Klerx, H. J. (2011). Financial impact of a dioxin incident in the Dutch dairy chain. Journal of Food Protection, 74(6), 967–979. Almstetter, M. F., Oefner, P. J., & Dettmer, K. (2012). Comprehensive twodimensional gas chromatography in metabolomics. Analytical and Bioanalytical Chemistry, 402(6), 1993–2013. Bernard, A., Broeckaert, F., De Poorter, G., De Cock, A., Hermans, C., Saegerman, C., & Houins, G. (2002). The Belgian PCB/dioxin incident: Analysis of the food chain contamination and health risk evaluation. Environmental Research, 88(1), 1–18. Cahnmann, H. J. (1957). Partially deactivated silica gel columns in chromatography – Chromatographic behavior of benzo-alpha-pyrene. Analytical Chemistry, 29(9), 1307–1311. Dasgupta, S., Banerjee, K., Patil, S. H., Ghaste, M., Dhumal, K. N., & Adsule, P. G. (2010). Optimization of two-dimensional gas chromatography time-of-flight mass spectrometry for separation and estimation of the residues of 160 pesticides and 25 persistent organic pollutants in grape and wine. Journal of Chromatography A, 1217(24), 3881–3889. de Jong, J., Lopez, P., Mol, H., Baeten, V., Fernandez-Pierna, J. A., Vermeulen, V., ... Elliot, C. T. (2016). Analytical strategies for the early quality and safety assurance in the global feed chain. Approaches for nitrogen adulterants in soybean meal and mineral and transformer oils in vegetable oils. Trends in Analytical Chemistry, 76, 203–215. EC (2010). Guidelines for the validation of screening methods for residues of veterinary medicines. EC (2011a). Commission regulation (EU) No 575/2011 of 16 June 2011 on the catalogue of feed materials. In EC (Ed.). Official Journal of the European Union (Vol. 575/2011/EU, pp. 41).

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