This study aimed to investigate the ability of analyte protectants to enhance GC-MS signals and compensate matrix effects for a range of micropollutants in pure standard, effluent, and influent wastewater samples during analysis and detection.
Journal of Chromatography A 1676 (2022) 463280 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma Can analyte protectants compensate wastewater matrix induced enhancement effects in gas chromatography – mass spectrometry analysis? Mathias B Jørgensen a,b, Jan H Christensen b,∗ a BIOFOS A/S, Refshalevej 250, København 1432, Denmark Analytical Chemistry Group, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, Frederiksberg C, 1871, Denmark b a r t i c l e i n f o Article history: Received 21 March 2022 Revised 20 June 2022 Accepted 24 June 2022 Available online 25 June 2022 Keywords: Analyte protectants Wastewater Matrix enhancement effects GC-MS Micropollutants a b s t r a c t This study aimed to investigate the ability of analyte protectants to enhance GC-MS signals and compensate matrix effects for a range of micropollutants in pure standard, effluent, and influent wastewater samples during analysis and detection Wastewater samples were prepared for analysis using multilayer solid phase extraction for the purpose of extracting sample components with a broad range of physicalchemical properties The sample extracts were either spiked or not spiked with target compounds and four analyte protectants: 3-ethoxy-1,2-propanediol, D-sorbitol, gluconolactone, and shikimic acid In this way, it was possible to evaluate the matrix effects of wastewater samples and compare the use of analyte protectants with the conventional correction method of allocating a best matching internal standard to each target compound A relation was observed between level of wastewater treatment and matrix effects, with the largest effects observed for influent samples and the smallest effects for effluent samples Compensation of matrix effects with analyte protectants gave comparable results with the conventional correction method of allocating a best matching internal standard to each of the 13 investigated micropollutants The best overall compensation was observed using analyte protectants and the internal standard correction method in combination © 2022 The Author(s) Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Introduction In the European Union up to 70,0 0 chemicals are in use every day, including “down the drain” products such as pharmaceuticals, personal care products, biocides, and flame retardants They are all micropollutants detected in wastewater at trace concentrations Wastewater result from uses of freshwater in households, industry, hospitals, agriculture, and from rainwater ending up in drains Wastewater is treated at wastewater treatment plants (WWTPs) in industrialized countries to remove organic content, nitrogen, and phosphorus, and treated effluents enters the aquatic environment Conventional WWTPs are not designed to remove micropollutants from wastewater, and potential toxicological effects on aquatic life and human health from discharges of persistent micropollutants are manifold [1–3] Evaluation of exposure is usually based on a target list of micropollutants that are known to be hazardous and persistent in the ∗ Corresponding author E-mail address: jch@plen.ku.dk (J.H Christensen) aquatic environment [4] In target analysis, properties of these micropollutants are used to optimize sample preparation and correct for instrument drift and matrix effects (MEs) [5] A matrix refers to all compounds in the sample different from the target compounds of interest In gas chromatography – mass spectrometry analysis (GC-MS) of volatile and semi volatile micropollutants, MEs emerge most often as response enhancement effects: The presence of matrix components allows for a larger number of susceptible target molecules to reach the detector The matrix protects thermally labile targets at high temperatures from degradation and compete for active sites in the GC-MS system Active sites arise as exposed silanol groups and metal ions in the liner and capillary column, and from activities of metal ions in the MS Condensation of nonvolatile material from injection of multiple sample matrixes and use of harsh temperature programs during analysis can activate surfaces in the liner and column even further The instrument condition can therefore change over time while it is in use [6] Compounds prone to matrix response enhancement effects include polar acids or bases containing oxygen, nitrogen, phosphorus, or sulfur in their molecular structure The polar groups interact with ac- https://doi.org/10.1016/j.chroma.2022.463280 0021-9673/© 2022 The Author(s) Published by Elsevier B.V This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) M.B Jørgensen and J.H Christensen Journal of Chromatography A 1676 (2022) 463280 tive sites via Van der Waals forces, hydrogen bonding, ionic bonding, and even covalent bonding, and molecules can degrade during interaction MEs will therefore depend on; physical-chemical properties of each target compound, type, amount, and number of sample injections, and the condition of the GC-MS instrument in use [6–8] To limit or even avoid quantification biases when comparing samples with different matrix composition such as influent and effluent wastewater, it is important to be able to correct MEs as part of the analytical setup Several methods to correct or reduce MEs in target analysis exists such as dilution or extensive sample preparation to remove matrix components [9], the use of isotopically labeled standards (ISTD) or matrix-matched calibration to correct for MEs [10], standard addition experiments [11], and derivatization to decrease polarity and thereby the reactivity of the target compounds [12] However, these methods have all drawbacks such as losses during sample cleanup or derivatization, high costs and labor-intensive, difficulty in finding a blank sample matrix, and inconsistent and insufficient reactivity of derivatization agents [9– 13] Target analysis of a restricted number of compounds also lead to another type of bias as hazardous and persistent micropollutants may be overlooked if they are not among the selected targets [5] An alternative strategy to target screening is non-target screening (NTS) analysis The strategy allows for identification of potentially thousands of compounds in a sample Correction in NTS can involve the strategy of allocating a best matching ISTD to each detected unknown peak [14] As an alternative, the use of analyte protectants (APs) is a simple, practical, and cheap way to correct for MEs The basic idea of APs is to add a high concentration of one or a few specific compounds to all samples, calibration samples, and quality control samples before analysis The APs mimics a matrix and protects the micropollutants of interest during analysis Matrix effect differences and quantification biases between different types of samples is thereby reduced [6,15] Additionally, the matrix enhancement effect results in peaks with less tailing and lower detection limits for labile and reactive micropollutants Via implementation of APs, it is therefore possible to take advantage from the benefits of MEs Sugar derivates containing multiple hydroxy groups was found to be some of the most promising compounds to mimic and compensate the matrix effect for the purpose of optimizing analysis of pesticides present in food samples [15] Comparable recoveries were observed for matrix matched calibration and implementation of APs with the use of the three APs 3-ethoxy-1,2-propanediol, D-sorbitol, and L-gulonic acid γ -lactone [16] Improved ruggedness was also observed in a long-term stability test during another study using the same APs [17] Shikimic acid was found to protect base labile compounds and has been implemented together with the other three mentioned APs [18] All abovementioned studies deal with analyses of pesticides in food Only two studies have prior investigated the use of APs in relation to analyses of water [6] Barrek et al tested and implemented isopropanol as AP for the target analysis of 36 priority substances in surface water The study found isopropanol to be an efficient analyte protectant for these target compounds [19] Purdesova et al tested the use of the APs 3-ethoxy-1,2-propanediol, D-sorbitol, and L-gulonic acid γ -lactone for the quantification of pesticides in surface waters and found that the three APs could not eliminate MEs for the target pesticides and matrix investigated [20] In a pilot study, we have observed prominent MEs for micropollutants with a range of different physical-chemical properties in solid phase extraction (SPE) extracts of influent, mechanical treated, and effluent wastewater with an enrichment factor of 50 The main aim of this paper was to investigate in a systematic way the ability of APs to enhance GC-MS signals and compensate ME differences for a range of micropollutants in pure standard, ef- fluent, and influent wastewater samples during analysis and detection Additionally, a comparison of correction strategies is made with the method of allocating a best matching ISTD to each target compound To our knowledge this is the first study of ME correction using APs in the GC-MS analysis of wastewater [6] Methods 2.1 Chemicals and reagents All liquids were of analytical grade: Acetonitrile (≥ 99.9%, Honeywell), ethyl acetate (≥ 99.7%, Honeywell), water (Merck), methanol (≥ 99.9%, Honeywell), 4% ammonia in methanol (TCI chemicals), and formic acid (≥ 97.5%, Merck) A standard mix of 13 compounds (STD) and a separate internal standard mix of five deuterated compounds (ISTD) were both prepared in methanol (Table 1) The concentration was 10 mg L−1 in STD and 20 mg L−1 in ISTD mixture for each compound, respectively A mixture of APs was prepared in acetonitrile and water (v:v 6:4) with 200 g L−1 3-ethoxy-1,2-propanediol, g L−1 D-sorbitol, 10 g L−1 gluconolactone, and g L−1 shikimic acid according to the EURL method [21] All stock solutions were stored at −18 °C 2.2 Sampling and sample preparation Grab samples of influent and effluent wastewater was collected on November 12 2020 from Avedøre WWTP, Kanalhomen 28, 2650 Hvidovre, Denmark The two samples were vacuum filtered; first with a 1.6 μm and then with a 0.7 μm glass microfiber filter, to remove particles before sample preparation and analysis The filtered samples were stored at C and were extracted by multilayer SPE using an automated SPE-03 system (PromoChrom Technologies Ltd.) The SPE method was developed by Tisler et al for the purpose of analyzing micropollutants with a broad range of physical-chemical properties via NTS A detailed description of the method is in the supplementary information (SI) to the paper [22] L of sample was first loaded and then eluted with ml ethyl acetate/methanol/4% ammonia in methanol (v:v:v 46:46:8), subsequently with ml ethyl acetate/methanol/formic acid (v:v:v 49:49:1.7), and ml methanol Samples were then evaporated with nitrogen to a volume of 300 μL (± 50 μL) and reconstituted to a final volume of ml with methanol The overall enrichment factor was 500 A part of the extracts were handed over to this project, and then further diluted (v:v 9:1) with methanol to reach a relative enrichment factor of 50 Influent and effluent samples were post-spiked after SPE with ISTD (v:v 1:10) Methanol without SPE enrichment was used as pure standard solution and was also spiked with ISTD (v:v 1:10) Each type of sample (influent, effluent, and pure standard) was then split into two parts One part was spiked with AP mixture (v:v 3:110) according to the EURL method on APs, corresponding to 5.45 μg 3-ethoxy-1,2-propanediol, 0.136 μg D-sorbitol, 0.273 μg gluconolactone, and 0.136 μg shikimic acid in μL of injected sample with APs [21] The second part was spiked with same amount of methanol (v:v 3:110) to reach the same level of dilution for all samples Each of the six sample types (influent, effluent, and pure standard, with and without APs) were split into two parts One part was spiked with STD (v:v 1:7.5) and the other part was spiked with methanol (v:v 1:7.5) to reach the same level of dilution In this way, the three sample types (influent, effluent, and pure standard samples) were prepared both with APs and STD, only with APs, only with STD and with neither of the two, having a total of six sample types spiked with STD and six sample types spiked with methanol as a control (Fig B1) All 12 sample types were prepared in triplicate, ending up with a total of 36 samples prepared for analysis M.B Jørgensen and J.H Christensen Journal of Chromatography A 1676 (2022) 463280 Table The 13 STDs and five ISTDs, prepared in two separate solutions, with specified CAS number (CAS No.), type, chemical formula (Formula), molecular mass∗ (Mmi ), retention time (RT), m/z ion used for quantification (Quant.ion), m/z ion used for qualification (Qual.ion), LogP, and volatility∗ ∗ Compound CAS No Type Formula Mmi ∗ RT (min) Quant.ion (m/z) Qual.ion (m/z) LogP Volatility∗∗ DEET Ibuprofen Caffeine-d9 Terbutryn Triclosan Venlafaxine Bisphenol A-d16 Amitriptyline-d3 Amitriptyline Carbamazepine-d8 Carbamazepine Tebuconazole Sertraline Citalopram Estradiol Ethinylestradiol Progesterone-d9 Simvastatin 134-62-3 15687-27-1 72238-85-8 886-50-0 3380-34-5 93413-69-5 96210-87-6 342611-00-1 549-18-8 1538624-35-9 298-46-4 107534-96-3 79617-96-2 59729-33-8 50-28-2 57-63-6 15775-74-3 79902-63-9 Insecticide Pharmaceutical NA Herbicide Antibiotic Antidepressant NA NA Antidepressant NA Anticonvulsant Fungicide Antidepressant Antidepressant Hormone Hormone NA Pharmaceutical C12H17NO C13H18O2 C8H10N4O2 C10H19N5S C12H7Cl3O2 C17H27NO2 C15H16O2 C20H24ClN C20H24ClN C15H12N2O C15H12N2O C16H22ClN3O C17H17Cl2N C20H21FN2O C18H24O2 C20H24O2 C31H30O2 C25H38O5 191.13 206.13 203.08 241.14 287.95 277.20 244.22 280,20 277.18 244.09 236.09 307.15 305.07 324.16 272.18 296.18 314.22 418.27 9.51 9.81 11.40 12.14 13.20 13.23 13.54 13.92 13.99 14.70 14.70 14.86 14.98 15.16 16.27 16.59 17.19 18.01 119.05 161.05 203.15 226.05 290.05 58.15 224.15 61.15 58.05 200.15 193.05 124.95 274.05 58.05 272.15 213.05 129.15 159.15 190.15 163.05 115.15 185.05 288.00 134.05 223.15 62.15 59.00 171.15 192.05 250.05 276.05 238.05 160.05 296.25 323.35 157.05 2.18 3.97 −0.7 3.74 4.76 3.20 3.32 4.92 4.92 2.45 2.45 3.70 5.51 3.76 4.01 3.67 3.87 4.68 2.1E-8 1.5E-7 1.1E-11 1.15E-8 2.1E-8 2.04E-11 4.0E-11 6.85E-8 6.85E-8 1.08E-7 1.08E-7 1.45E-10 6.45E-5 2.69E-11 3.64E-11 7.94E-12 6.49E-8 2.8E-10 ∗ ∗∗ Monoisotopic mass Henrys constant (atm-m3 /mol at 25 °C) 2.3 Instrumentation and analysis physical-chemical properties in terms of interactions and thermal stability during the GC-MS analysis The Euclidean distance (ED) between each STD and ISTD in the 12-dimensional space was calculated according to Eq (2), where xpi and xqi are the ME values of STD compound p and ISTD compound q from ME measurement number i The ISTD with shortest ED to a STD was selected as a best matching ISTD to correct for MEs, as described elsewhere in literature [10] No separate training and validation sets were used to first calculate EDs and then calculate the ME with ISTD correction Instead, the same set of samples were used for both actions, potentially leading to overfitting and a bias in the result An Agilent 6890 gas chromatograph interfaced to an Agilent 5973 MS was operated in EI mode (70 eV) Helium was used as carrier gas with a flow rate of 1.1 ml min−1 and with injection run in splitless mode (50 ml min−1 at min) Injection volume was μL and injection temperature 280 C Transfer line and ion source temperatures were 300 C and 230 C, respectively Massto-charge ratios (m/z) were measured in selected ion monitoring (SIM) mode with one quantifier and one qualifier ion for each compound (Table 1) The instrument was equipped with a 30 m 5% phenyl 95% dimethylpolysiloxane (ZB-5) column with an inner diameter of 0.25 mm and a film thickness of 0.25 μm The following temperature program was used: Starting temperature 60 °C held for min, then ramped at a rate of 15 °C min−1 to 300 °C and held at The total run time was 19 The prepared samples were analyzed in a systematic sequence; first four pure standards, then four effluents, and then four influent wastewater samples with and without STDs and APs The sequence setup was repeated in three identical batches to include the prepared triplicates (12 × = 36 samples) according to Table C1 of the SI A methanol system blank was also analyzed before and after the sequence, and the average of the two system blanks was subtracted from all samples before further data treatment 12 ED = (2) Results and discussion The normalized peak area for each of the five ISTDs as a function of injection order is shown in Fig Amitriptyline-d3 was the most stabile ISTD, with relative peak areas between 0.9 and 1.2 This stability is related to steric hindrance of tertiary amine, the only reactive group present in the molecule The nitrogen lone pair in tertiary amine is less available for chemical interaction with active sites in the GC-MS compared to primary and secondary groups Considering the remaining four ISTDs, relative signals along the sequence obtain values ≥ 1, indicating clear enhancement effects, after injection of wastewater samples and samples with APs: Caffeine-d9 and progesterone-d9 with relative peak areas up to 3.6 and 3.5, respectively Bisphenol A-d16 and carbamazepine-d8 with relative peak areas up to 12.4 and 16.8 (Fig 1) Primary amine, amide, hydroxyl, imidazole, ketone, and carboxylic acid were present in the structure of one or several of the investigated STDs and ISTDs susceptible to enhancement effects These reactive groups were also highlighted as susceptible to matrix enhancement effects in prior publications [8,13] The chemical structure of all STDs, ISTDs and APs are presented in the SI (Table A1) Chromatograms are also presented in the SI (Figs D3 and D4) Repeating patterns along the sequence was observed for the four susceptible ISTDs (Fig 1) A positive change in signal was observed every time pure standards and effluents with APs (squares and triangles in Fig 1) were analyzed after pure standards and The ME (%) was calculated as the peak area of a STD compound in a wastewater sample (S) (influent or effluent, with or without APs) relative to the pure standard (B) (with or without APs) according to Eq (1) The peak area of the control (SControl and BControl ) was subtracted from the samples spiked with STD, to correct for the inherent content of STD compounds in the wastewater and to subtract any background noise (Eq (1)) S − SControl · 100 B − BControl i=1 2.4 Data treatment ME (% ) = xpi − xqi (1) Results for each compound can be placed in a 12-dimensional space, where each dimension is a ME measurement for each of the 12 samples (effluent, influent, effluent with APs, and influent with APs, all in triplicate) Compounds close to each other in the 12-dimensional space can be deemed as compounds with similar M.B Jørgensen and J.H Christensen Journal of Chromatography A 1676 (2022) 463280 Fig Stability plot of the five ISTDs as a function of injection order Data is presented as peak areas relative to peak area of the first run for pure standard without APs (circle), pure standard with APs (square), effluent without APs (diamond), effluent with APs (triangle), influent without APs (plus), and influent with APs (cross) See also Table C1 of the SI for the injection order of the 36 samples estradiol (173 ± 32%, p value = 0.03), using a one-way student ttest to calculate the p values DEET and ethinylestradiol revealed larger MEs at 229 ± 48% and 165 ± 2.9% in effluent samples and 394 ± 61% and 267 ± 48% in influent samples, respectively Caffeine-d9 was the best matching ISTD to DEET and progesteroned9 the best matching ISTD to ethinylestradiol Ibuprofen, triclosan, and simvastatin obtained the largest observed MEs of all STDs and large ED values to all five ISTDs It was therefore not possible to find a good match to any ISTD, and carbamazepine-d8 and bisphenol A-d16, the two most susceptible ISTDs, obtained the lowest ED values to these three STDs (Tables D1, D2, and D3) Additionally, amitriptyline and carbamazepine obtained the lowest calculated EDs to their respective exact matching ISTD (Tables D1 and D2) An increasing trend in MEs were observed with decreasing level of wastewater treatment (influent > mechanical treated wastewater (mechanical) > effluent > pure standard) (Fig and Table D6) This trend was not observed for carbamazepine in effluent compared to the pure standard sample MEs were calculated according to slope differences and enhancement effects were observed in influent wastewater for amitriptyline (140%), carbamazepine (126%), and estradiol (144%) No significant enhancement was observed for terbutryn (102%) (Fig D2, Table D6, and Eq (D1)) Variation in MEs were observed in the different experiments (Figs and 2, Table D3), and potential reasons to ME fluctuations were also highlighted in literature: A difference in system condition of the GC-MS instruments used in the two studies, and differences in matrix contribution from the different wastewater samples analyzed, were reasonable explanations to observed fluctuations [6] The effect of ME correction on 13 investigated STDs in effluent and influent wastewater is shown in Fig An enhancement effect was observed for all 13 STDs in influent compared to effluent samples, when no ME correction was applied (dark-green boxplots in Fig 3) Using the ISTD with closest retention time to each STD, correction of MEs resulted in underestimation (ME values 1.5 times the interquartile range, marked as black circles in Fig 3) When no exact matching ISTD is available, these observations illustrate the importance of using an ISTD with similar GC-MS properties in terms of MEs Application of APs gave similar overall improvements on the ME correction compared to the ED method, though with slight underestimations (ME 1.5 times the interquartile range beyond either end of the box Measurements > 500% are not presented (see instead Table D4 and Fig D1) studies to validate the presented approach on a much wider range of compounds, different APs, and several environmental matrixes Still, the promising initial results presented in this study suggest analyte protectants as a potential alternative approach to conventional correction methods in target analysis, but maybe also in situations such as nontarget screening, where the high number of potential compounds of interest make conventional correction strategies with ISTDs inappropriate CRediT authorship contribution statement Mathias B Jørgensen: Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Methodology, Conceptualization Jan H Christensen: Project administration, Supervision, Writing – review & editing, Conceptualization, Methodology Acknowledgments Conclusion This study is a contribution to the VANDALF project under grant agreement No 9067-0 032B and supported by the Innovation Fund Denmark We would like to thank MSCi for making laboratory facilities and a GC-MS instrument available for our disposition Furthermore, we would like to thank the reviewers for valuable suggestions, which improved the manuscript The four analyte protectants 3-ethoxy-1,2-propanediol, Dsorbitol, gluconolactone, and shikimic acid were able to enhance the signals of investigated micropollutants in pure standard and effluent wastewater samples Matrix enhancement effects were observed for six micropollutants in effluent and 11 out of 13 investigated micropollutants in influent wastewater Especially micropollutants containing one or several of the following reactive groups; hydroxyl, primary amine, amide, ketone, carboxylic acid, and imidazole were recognized as susceptible to enhancement effects in wastewater samples and in samples with APs MEs of the micropollutants were increasing with content of matrix components in the wastewater samples, and the largest MEs were observed for influent samples Analyte protectants were able to significantly enhance the signals of pure standard and effluent wastewater samples This was not the case for influent samples, also indicating a high matrix contribution from influent wastewater alone Correction of MEs with APs resulted in comparable overall results with the method of allocating a best matching ISTD to each target compound The best overall correction of MEs was observed using APs and ISTDs in combination Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.chroma.2022.463280 References [1] R.P Schwarzenbach, B.I Escher, K Fenner, T.B Hofstetter, C.A Johnson, U Von Gunten, B Wehrli, The challenge of micropollutants in aquatic systems, Science 313 (5790) (2006) 1072–1077, doi:10.1126/science.1127291 [2] J Margot, L Rossi, D.A Barry, C Holliger, A review of the fate of micropollutants in wastewater treatment plants, Wiley Interdiscip Rev Water (5) (2015) 457–487, doi:10.1002/wat2.1090 [3] J.Y Tang, S McCarty, E Glenn, P.A Neale, M.S.J Warne, B.I Escher, Mixture effects of organic micropollutants present in water: towards the development of effect-based water quality trigger values for baseline toxicity, Water Res 47 (10) (2013) 3300–3314, doi:10.1016/j.watres.2013.03.011 [4] The European Union, 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Nakajima, Compensation of matrix effects in gas chromatography? ? ?mass spectrometry analysis of pesticides using a combination of matrix matching and multiple isotopically labeled internal standards,... Negative instrument drifts can result from increasing numbers of active sites in the liner and column after several injections, and instrument maintenance is a point of consideration, analyzing sample... samples Matrix enhancement effects were observed for six micropollutants in effluent and 11 out of 13 investigated micropollutants in influent wastewater Especially micropollutants containing one