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  • 1 Chiba University, Center for Preventive Medical Sciences, Inage-ku Yayoi-cho 1-33, Chiba City 263-0022, Japan
  • 2 JEOL Ltd., 1156 Nakagami-cho, Akishima, Tokyo 196-0022, Japan
  • 3 Chiba University, Chiba City 260-8670, Japan
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In this study, we developed a highly sensitive, robust method for determining 12 congeners of two to ten chlorinated polychlorinated biphenyls (PCBs) in serum samples using gas chromatography (GC)–mass spectrometry (MS) operating in selected ion monitoring mode (SIM: m/z 35) with negative ion chemical ionization (NICI), and the results were compared with those from GC coupled with high-resolution MS (HRMS) with electron impact (EI). The recovery rates of the surrogate PCB congeners were 97.2%–112% (coefficient of variation: 5.3%–8.5%), and the method detection limits for PCBs in each matrix obtained by GC–NICI–quadrupole mass spectrometry (qMS) were 1.9–20 pg g−1 wet wt. The analytical values of the target compounds in the samples analyzed by GC–NICI–qMS and GC–EI–HRMS were comparable (Passing–Bablok regression: R = 0.888–0.967), and the analytical values obtained via GC–NICI–qMS were almost comparable with those of the certified serum samples from National Institute of Standards and Technology (NIST: SRM1957), indicating that GC–NICI–qMS is suitable for the analysis of tetra- to hepta-chlorinated PCBs in serum samples.

Abstract

In this study, we developed a highly sensitive, robust method for determining 12 congeners of two to ten chlorinated polychlorinated biphenyls (PCBs) in serum samples using gas chromatography (GC)–mass spectrometry (MS) operating in selected ion monitoring mode (SIM: m/z 35) with negative ion chemical ionization (NICI), and the results were compared with those from GC coupled with high-resolution MS (HRMS) with electron impact (EI). The recovery rates of the surrogate PCB congeners were 97.2%–112% (coefficient of variation: 5.3%–8.5%), and the method detection limits for PCBs in each matrix obtained by GC–NICI–quadrupole mass spectrometry (qMS) were 1.9–20 pg g−1 wet wt. The analytical values of the target compounds in the samples analyzed by GC–NICI–qMS and GC–EI–HRMS were comparable (Passing–Bablok regression: R = 0.888–0.967), and the analytical values obtained via GC–NICI–qMS were almost comparable with those of the certified serum samples from National Institute of Standards and Technology (NIST: SRM1957), indicating that GC–NICI–qMS is suitable for the analysis of tetra- to hepta-chlorinated PCBs in serum samples.

1. Introduction

Polychlorinated biphenyls (PCBs) affect the human endocrine system. Despite being banned, PCBs persist widely in wildlife and among humans because of their lipophilic properties, low water solubility, and bioaccumulation in fatty tissues [1]. Many previous environmental PCB studies utilized gas chromatography (GC)–electron impact (EI) high-resolution mass spectrometry (HRMS) [2], GC–EI–quadrupole mass spectrometry (qMS) [3], GC–electron capture detector (ECD) [4], and GC–negative ion chemical ionization (NICI)–qMS [5]. GC–EI–qMS is easy to handle and has high selectivity; however, its sensitivity is slightly lower than that of other methods. Although GC–EI–HRMS can provide high sensitivity and selectivity, the instrumentation is expensive for conducting routine or high-throughput analyses and its operation requires specialized technical skills. GC–ECD is a sensitive technique; however, selectivity is lower than other methods. In contrast, GC–NICI–qMS is a sensitive, selective technique particularly suitable for the analysis of halogenated compounds. So far, it has been used for the analysis of numerous contaminants, including PCBs, in various environmental matrices [613]. Usually, PCB ions are monitored using GC–NICI–qMS analysis set as M+ and M+2 ions. However, chlorine ion (Cl: m/z 35) was also monitored for rapid PCB analysis in insulating oil [14]. This method was highly suitable for routine monitoring of PCBs in insulating oil; however, the rapid pretreatment method of biological samples using GC–NICI–qMS is not well developed and validation was not determined. In this study, we developed a highly sensitive and rapid analytical method for determining PCBs in serum samples using GC–NICI–qMS.

2. Materials and Methods

2.1 Chemicals and Reagents

Samples used in this study were collected from subsamples originating from a measurement campaign performed in our laboratory, which was accredited in compliance with ISO/IEC 17025:2005 standards (Accreditation of Certification Body: Japan Accreditation Board). Herein, 12 congeners of PCBs (tetra- to hepta-: PCB66, 74, 99, 105, 118, 138, 153, 156, 170, 178, 180, and 187), isomers, and surrogates (PCB23, 30, 55, and 207) were used for identification and quantification. These samples were obtained from Cambridge Isotope Laboratories Inc. (Andover, MA, USA), and the syringe spike (PBB 154) was purchased from AccuStandard, Inc. (New Haven, CT, USA). Sulfuric acid (H2SO4), n-hexane, ethanol, decane, and silica gel (Wako-gel S1) were purchased from Wako Pure Chemical Industries (Tokyo, Japan). The standard reference material (SRM 1957) was purchased from the National Institute of Standards and Technology (NIST), USA [15]. Fetal bovine serum (BWT-1650-00C) was purchased from Funakoshi (Tokyo, Japan).

2.2 Sample Collection

Informed consent was obtained from all donors. This study was approved by the Ethics Committee of Chiba University, Japan. Human serum samples (n = 26) were collected from Chiba Prefecture, Japan [16]. Whole blood samples were collected by a certified physician, and serum samples were obtained via centrifugation after heparin treatment and stored at −80 °C for the analysis of PCBs.

2.3 Sample Preparation

For the analyses using GC–NICI–qMS and GC–EI–HRMS, the serum samples (0.5 g) were denatured with 1 mL of 1 M potassium hydroxide–MeOH. The target compounds were extracted twice with 500 μL of n-hexane, and CB23, 30, 55, and 207 (40 pg of each) were spiked as surrogate internal standards. After adding the surrogates, the extracts were combined and washed with ultrapure water.

The solvent-evaporated residue was dissolved in 3 mL of n-hexane, passed through a glass column packed with 500 mg of 44% H2SO4 silica gel, and concentrated to near dryness. Then, 20 pg of PBB 154 dissolved in 200 μL of decane was added as a syringe spike for the GC–MS analysis.

2.4 Conditions for GC–NICI–qMS

PCB analysis was run on a JMS-Q1050GC (JEOL Ltd., Tokyo, Japan) quadrupole mass spectrometer equipped with an Agilent 7890B gas chromatograph and a 7693 autosampler (Agilent Technologies Inc., Tokyo, Japan). GC separation was achieved using an HP5-MSUI fused-silica capillary column (30 m × 0.25 mm ID × 0.25 μm film Agilent Technologies Inc., Tokyo, Japan). The injector was held at 280 °C and operated in the pulsed splitless mode. The column oven temperature program for the analysis of PCB congeners was maintained at 130 °C for 1 min, heated to 180 °C at a rate of 20 °C min−1, heated to 260 °C at a rate of 2 °C min−1, heated to 300 °C at a rate of 5 °C min−1, and maintained at 300 °C for 4 min. Helium (purity: >99.99995) at a column flow rate of 1.3 mL min−1 (constant flow rate) was used as the GC carrier gas, while methane (purity: >99.999) was used as the reagent gas for the NICI source. The ionizing energy and ion source temperature were set to 150 eV and 280 °C, respectively. The identification and quantification of the 12 PCB congeners were achieved by monitoring chlorine ions (Cl: m/z 35) in selected ion monitoring (SIM) analysis with the NICI–MS detector. In this analysis, retention times of the analytes were used as identification.

2.5 Conditions for GC–EI–HRMS

PCB analysis was run on a JMS-700D (JEOL Ltd., Tokyo, Japan) high-resolution spectrometer equipped with an Agilent 6890B gas chromatograph and a 7683 autosampler (Agilent Technologies Inc., Tokyo, Japan). GC separation was achieved using a DB-5MS fused-silica capillary column (30 m × 0.25 mm ID × 0.25 μm film Agilent Technologies Inc., Tokyo, Japan). The injector was held at 270 °C and operated in the pulsed splitless mode. The column oven temperature program for the analysis of PCB congeners was maintained at 120 °C for 1 min, heated to 180 °C at a rate of 20 °C min−1, heated to 280 °C at a rate of 2 °C min−1, and maintained at 280 °C for 3 min. Helium at column flow rate of 1.0 mL min−1 (constant flow rate) was used as the GC carrier gas. The ionizing energy and ion source temperature were set to 38 eV and 250 °C, respectively. The identification and quantification of the 12 PCB congeners were achieved by monitoring M+ and M+2 ions in SIM analysis with the HRMS detector.

2.6 Method Validation

Multilevel calibration curves (20–500 fg μL−1: R2 > 0.997) in the linear response interval of the detector were created for the quantification of PCBs. The recovery rates of the surrogate PCB congeners (40 pg each with five replicates) were in the range 97.2%–112% with a 5.3%–8.5% coefficient of variation (CV). Identification of the target analytes was based on the comparison of the relative retention times with the internal standards used for quantification and ion chromatograms. Procedural blanks were simultaneously analyzed with every batch of nine samples to check for interference or contamination from solvent and glassware. For each analysis, the mean procedural blank value was used for subtraction. The method detection limits (MDLs) were defined as three times the standard deviation (SD) of a low concentration sample (20 pg of fetal bovine serum) (n = 5). Finally, performance of the analytical method was verified by repeated analysis (n = 7) of certified reference material (SRM 1957).

2.7 Statistical Analysis

Passing–Bablok regression [17] and Pearson's correlation coefficients were used to investigate the agreement between the original GC–EI–HRMS analysis and the new GC–NICI–qMS methods using the MCR package [18] in R, freely-available statistical software [19]. Moreover, the 95% confidence intervals (CIs) of the regression slope were obtained by applying the bootstrap method (999 bootstraps). The parameters used in this study for GC–EI–HRMS are similar to those previously described [20].

3. Results and Discussion

3.1 Method Detection Limit for GC–NICI–qMS

Temperature of the ion source of MS was optimized. The peak area of PCB at 280 °C or above (relatively stable in that temperature range) was about 1.5 and 1.2 times higher than the peak area at 210 °C and 250 °C, respectively, indicating that the sensitivity is greater at the higher ion source temperatures. However, to keep the equilibrium a stability of ion source and column condition, the temperature of the ion source was set at 280 °C.

The MDLs for individual PCBs in this method were 1.9–20 pg g−1 wet wt (Table 1). These results agree with those reported in the other analytical methods for serum samples using GC–EI–HRMS (MDL: CB153, 2 pg g−1 wet wt) [21], GC–ECD (limit of detection [LOD]: 30–340 pg g−1 wet wt) [5], and GC–NICI–qMS (LOD: 5–20 pg g−1 wet wt [9] and 10–80 pg g−1 wet wt [5], limit of quantification [LOQ]: 10–50 pg g−1 wet wt [6]), indicating that the MDLs for the proposed analytical methods were slightly higher than those for the GC–EI–HRMS method; however, except for CB99, most of the MDLs using this method were comparable with GC–EI–HRMS and were slightly lower the than previous analytical method using GC–NICI–qMS and GC–ECD. Moreover, the throughput and simplicity of analysis was superior to GC–EI–HRMS, indicating that this method is suitable for PCB screening analysis in serum.

Table 1.

Method detection limits (MDLs) of analytes and comparison of the certified values and the experimental concentrations of the target PCBs obtained in the analysis of the SRM 1957 (n = 7 of independent treatments)

GC–NICI–MS (pg g−1 wet wt)Certified values (pg g−1 wet wt)MDL (pg g−1 wet wt)
CB66NA2.7
CB747.2 ± 2.613.8 ± 0.11.9
CB9911.6 ± 0.620
CB105NA3.6
CB11814 ± 2.118.9 ± 1.27.9
CB13837 ± 1.736.9 ± 9.03.2
CB15351 ± 2.858.2 ± 0.92.6
CB1567.6 ± 2.88.2 ± 0.63.9
CB17015 ± 1.616.2 ± 2.03.7
CB178NA4.4
CB18045 ± 3.4462.7
CB18710 ± 1.415.5 ± 0.52.2

3.2 Comparison of Serum PCB Levels Using GC–NICI–qMS and GC–EI–HRMS

In this study, Passing–Bablok regression analysis was used for the comparison of values from developed and previous methods [17]. This analysis is a statistical procedure that estimates the agreement and possible systematic bias between methods. It is robust, non-parametric, and non-sensitive to the distribution of errors and outliers in a data set [22]. In this study, CB138, 153, and 180 were detected in >90% of human serum samples (n = 26) using GC–NICI–qMS and GC–EI–HRMS. Analytical values from each method were compared using Passing–Bablok regression analysis. The Passing–Bablok regression showed outstanding agreement between the GC–NICI–qMS and GC–HRMS methods for CB138, 153, and 180 (R = 0.888, 0.967, and 0.906, respectively, and coefficients = 0.91 [95% CI, 0.730–1.07], 0.73 [95% CI, 0.630–0.833], and 0.90 [95% CI, 0.742–1.08], respectively; Figure 1). The values calculated by GC–NICI–qMS were slightly lower than those calculated by GC–EI–HRMS; especially, the value of CB153 in GC–NICI–qMS was significantly lower than that in GC–EI–HRMS. However, Pearson's R-values were within the permissible range, indicating that the analytical values of PCB congeners calculated by GC–NICI–qMS were comparable with those calculated by GC–EI–HRMS.

Figure 1.
Figure 1.

Correlation analysis of the PCB values (CB138, 153, and 180) in human serum samples (n = 26) using GC–NICI–MS and GC–EI–HRMS. The gray zone represents the 95% CI for regression, determined via bootstrapping (999 bootstraps)

Citation: Acta Chromatographica Acta Chromatographica 29, 4; 10.1556/1326.2017.00029

3.3 Analysis of the Certified Reference Material SRM 1957

Table 1 shows the comparison of the analytical values calculated by GC–NICI–qMS and the certified values for SRM 1957. Except for CB74, 99, and 187, the experimental value for PCB congeners corresponded with the certified values, indicating that, when only chlorine ion (Cl: m/z 35) was monitored, the selectively of PCBs was maintained. As shown in Figure 2, the background at m/z 35 using this analytical method shows a good noise ratio with very low background. These results indicate that using GC–NICI–qMS is a suitable method for rapid screening analysis in large-scale cohort studies.

Figure 2.
Figure 2.

The extracted ion chromatogram for m/z = 35 of certified reference material serum SRM 1957

Citation: Acta Chromatographica Acta Chromatographica 29, 4; 10.1556/1326.2017.00029

3.4 Application to Serum Sample

The developed method was applied to analyze the PCB content in unspiked serum samples (n = 26). The median concentration of PCB was 0.35 ng g−1 wet wt, with CB153 (median: 0.12 ng g−1 wet wt), CB138 (median: 0.088 ng g−1 wet wt), and CB180 (median: 0.067 ng g−1 wet wt) contributing the most to the total PCB concentration. In this study, PCB profiles in serum samples were comparable with previous Japanese research [23]. However, residue levels of PCBs were lower than that of previous Japanese research (median CB153: 0.40 ng g−1 wet wt) [23]. The mean age of donors from this and previous studies was 32.5 [16] and 51 years [23], respectively. It is well known that the PCB levels in the human body increase with age [24], indicating that the age of donors might affect the concentration of PCBs. However, sample size was insufficient, and further investigation is required to assess the exposure of the Japanese population to PCBs.

Acknowledgment

These studies were supported by grants for Scientific Research (B): Grants-in-Aid for Scientific Research [KAKENHI (24310021)] and the Environment Research and Technology Development Fund (5-1305 and 5-1652) from the Ministry of the Environment of Japan.

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If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1.

    Safe, S. H. Crit. Rev. Toxicol. 1994 , 24 , 87 .

  • 2.

    Barr, J. R.; Maggio, V. L.; Barr, D. B.; Turner, W. E.; Sjodin, A.; Sandau, C. D.; Pirkle, J. L.; Needham, L. L.; Patterson, D. G. J. Chromatogr. B 2003 , 794 , 137 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Covaci, A.; Schepens, P. Chemosphere 2001 , 43 , 439 .

  • 4.

    Vidal, J. L. M.; Frias, M. M.; Frenich, A. G.; Olea-Serrano, F.; Olea, N. Anal. Bioanal. Chem. 2002 372 , 766 .

  • 5.

    Bolanos, P. P.; Frenich, A. G.; Vidal, J. L. J. Chromatogr. A 2007 , 1167 , 9 .

  • 6.

    Kontsas, H.; Pekari, K. J. Chromatogr. B 2003 , 791 , 117 .

  • 7.

    Turci, R.; Bruno, F.; Minoia, C. Rapid Commun. Mass. Sp. 2003 , 17 , 1881 .

  • 8.

    Saito, K.; Sjodin, A.; Sandau, C. D.; Davis, M. D.; Nakazawa, H.; Matsuki, Y.; Patterson, D. G. Chemosphere 2004 , 57 , 373 .

  • 9.

    Hovander, L.; Linderholm, L.; Athanasiadou, M.; Athanassiadis, I.; Bignert, A.; Fangstrom, B.; Kocan, A.; Petrik, J.; Trnovec, T.; Bergman, A. Environ. Sci. Technol. 2006 , 40 , 3696 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Grimalt, J. O.; Howsam, M.; Carrizo, D.; Otero, R.; de Marchi, M. R. R.; Vizcaino, E. Anal. Bioanal. Chem. 2010 , 396 , 2265 .

  • 11.

    Helaleh, M. I. H.; Al-Rashdan, A.; Ibtisam, A. Talanta 2012 , 94 , 44 .

  • 12.

    Pena-Abaurrea, M.; Ramos, J. J.; Gonzalez, M. J.; Ramos, L. J. Chromatogr. A 2013 , 1273 , 18 .

  • 13.

    Somoano-Blanco, L.; Rodriguez-Gonzalez, P.; Profrock, D.; Prange, A.; Alonso, J. I. G. Anal. Methods 2015 , 7 , 9068 .

  • 14.

    Machii, Y.; Kumazaki, O.; Mizuno, K.; Nagano, M.; Hayasaka, Y.; Hase, R.; Kondo, H.; Deguchi, T. J. Environ. Chem. 2003 , 13 , 959 .

  • 15.

    Keller, J. M.; Calafat, A. M.; Kato, K.; Ellefson, M. E.; Reagen, W. K.; Strynar, M.; O'Connell, S.; Butt, C. M.; Mabury, S. A.; Small, J.; Muir, D. C. G.; Leigh, S. D.; Schantz, M. M. Anal. Bioanal. Chem. 2010 , 397 , 439 .

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Sakurai, K.; Miyaso, H.; Eguchi, A.; Matsuno, Y.; Yamamoto, M.; Todaka, E.; Fukuoka, H.; Hata, A.; Mori, C. BMJ Open 2016 , 6 , 1 .

  • 17.

    Passing, H.; Bablok, W. J. Clin. Chem. Clin. Bio. 1983 , 21 , 11 , 709 .

  • 18.

    Ekaterina Manuilova, A. S. Fabian Model 2014 .

  • 19.

    Team, R. C. R.: A Language and Environment for Statistical Computing , Available at:http://www.R-project.org/.

  • 20.

    Jotaki, T.; Fukata, H.; Mori, C. Chemosphere 2011 , 82 , 107 .

  • 21.

    Sjodin, A.; Jones, R. S.; Lapeza, C. R.; Focant, J. F.; McGahee, E. E.; Patterson, D. G. Anal. Chem. 2004 , 76 , 1921 .

  • 22.

    Bilic-Zulle, L. Biochem. Medica 2011 , 21 , 49 .

  • 23.

    Nomiyama, K.; Yonehara, T.; Yonemura, S.; Yamamoto, M.; Koriyama, C.; Akiba, S.; Shinohara, R.; Koga, M. Environ. Sci. Technol. 2010 , 44 , 2890 .

  • 24.

    Mori, C.; Kakuta, K.; Matsuno, Y.; Todaka, E.; Watanabe, M.; Hanazato, M.; Kawashiro, Y.; Fukata, H. Environ. Sci. Pollut. R 2014 , 21 , 6434 .

    • Crossref
    • Search Google Scholar
    • Export Citation

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