Suspect screening of human serum with UHPLC-QTOF/MS

Suspect screening of human serum with

UHPLC-QTOF/MS

By: Louise P. Poolsri

Supervisor: Tuulia Hyötyläinen and Dawei Geng

Examiner: Anna Kärrman

26 MAY 2018

BACHELOR DEGREE THESIS 15 HP Örebro University

Abstract

The number of environmental pollutants has increased due to the increased production and the use of various chemicals in different applications. An estimation of today’s synthetic chemicals is approximately 50,000 to 100,000 and many of them may be harmful to humans and the environment. Persistent Organic Pollutants (POPs) are a specific type of environmental pollutants that are very stable and do not decompose easily. Example of those are pesticides, phthalates, phenols and per-/polyfluorinated alkyl substances (PFASs).

Several studies have reported that environmental pollutants may particularly affect pregnant women and the fetus. The aim of the present study was to characterize the environmental pollutant profile in serum extracts of pregnant women, by analyzing the blood and characterizing the chemicals using a suspect screening approach with chromatography combined with high-resolution mass spectrometry. The samples in this study were extracted with solid-phase extraction (SPE) prior to the instrumental analysis, to precipitate the proteins as well as to remove some phospholipids and other contaminants that could interfere with the instrumental analysis. Quality control samples was used to validate the instrumental performance.

Twelve compounds out of the 38 suspects were identified with this method, including both fatty acids and a few environmental pollutants. The detection rate of the fatty acids was higher than the environmental pollutants, which are; perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), monooctyl phthalate and 3,5-ditertbutyl salicylic acid. The low detection rate of the environmental pollutants, particularly of PFASs could be due to the low sensitivity of the method for the PFASs but also due to the decreased exposure to those compounds, since the samples were collected between 2013 to 2016, after the restrictions in the use of the two most common PFASs; PFOA and PFOS.

CONTENT

1. I

4

1.1. Environmental pollutants

4

1.2. Non-targeted screening and Suspect screening

5

1.3. Liquid Chromatography coupled with Quadrupole

d

Time-of-Flight Mass Spectrometry

5

1.4. Sample extraction

5

1.5. Human exposure during pregnancy

6

2. A

6

3. M

6

3.1. Sample preparation

6

3.2. Instrument analysis

7

3.3. Data analysis

8

3.4. Quality control

8

4. R

9

4.1. Quality control

4.2. Suspect screening and identification

10

14

5. D

21

6. C

22

7. A

22

8. R

23

1. Introduction

An estimation of today’s synthetic chemicals is approximately 50,000 to 100,000 and many of them can harm human and the environment (McGinn and Bright, 2000). Traditionally, environmental pollutants are analyzed using targeted methods. However, more recently non-target methods and suspect screening methods have been developed.

1.1. Environmental pollutants

Persistent Organic Pollutants (POPs) are a specific type of environmental pollutants that are very stable and do not degrade fast in the environment. The POPs can remain in the environment for decades after they have been released and they can be evaporated and travel through the air and water and accumulate in tissues of animals. POPs are produced both intentionally and unintentionally. Examples of these are pesticides, phthalates, phenols and perfluorinated alkylated substances (PFASs) (Spooner, 2012). There are several studies on how the environmental pollutants affect pregnant women and the fetus. Some studies suggest that exposure during the fetal development increases the child’s risk for asthma or overall risk of health problems (Liu et al., 2004).

Pesticides are used to attack, kill and remove the pest, but it has been reported that they have caused serious adverse health effects to agricultural workers (Spooner, 2012). Exposure to pesticides has been related to cancer, immune system disruption, nervous system damage, liver damage, memory loss, endocrine disruption, birth defects and other reproductive problems

(Bergman, 2018). Phthalates are used in everyday products in our everyday life, such as

makeup, plastic food packaging, body care products and kid’s toys, and they have been related to laboratory animals’ death and reproductive issues in human (Spooner, 2012).

Phenols or bisphenols have been associated with endocrine disruption and reproductive system problem (Spooner, 2012). They have also been linked to miscarriages and mental retardation in animals.

PFASs are synthetic fluorinated compounds that are used in industry and consumer products in everyday use (Yuan and Leanderson, 2017). PFASs can be found in food packaging, household products, workplaces, drinking water and living organisms. Two most common PFASs are perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), which are also present in humans. The concentrations of these compounds vary depending on age, diet, occupation and different lifestyles. PFASs do not degrade easily and they are usually present in liver and the bloodstream and they can also transfer to fetus and infants via placenta and breast milk (Berglund et al., 2017). PFASs can increase health risks by altering and affecting the liver, metabolism, immune system and the reproduction system. European Food Safety Authority (EFSA) has estimated that an exposure of 0.15 g PFOS/kg- and 1.5 g PFOA/kg body weight per day, do not pose any health risk (Glynn et al., 2015). However, the European exposure of the PFOA and PFOS are significantly lower than the limited value. For other PFASs, no known estimation has been made due to lack of knowledge about the substances health effect.

Parabens are esters of para-hydroxybenzoic acid and are also used in our everyday life, as preservatives in personal care, cosmetics, pharmaceutical and food products (Kirchhof and de

Gannes, 2013). The most common parabens are methylparaben, ethylparaben, propylparaben

and butylparaben. Studies have shown that parabens have weak estrogen properties and could cause cancer through endocrine disruption (Boberg et al., 2010). However, this occurs only with extremely high doses, which is far greater than what we normally use in the everyday life.

1.2. Non-targeted screening and suspect screening

Specific analytical methods are used for environmental pollutant screening analysis. Low limits of detection and quantification are often required to detect the environmental pollutants. Non-targeted screening is one of the analytical methods used for environmental pollutants

(Plassmann et al., 2016). The samples are analyzed with no prior knowledge of the pollutants

of interest. Non-targeted methods compose of broad sample analysis procedures, which are usually gas- or liquid chromatography-high-resolution mass spectrometry, combined with advanced data analyzing tools and identification by comparison with mass spectral libraries and using structure elucidation. The number of peaks detected in the datasets can be reduced to the number of relevant peaks prior to the compound identification.

Another screening analysis method is suspect screening method. Compared to non-targeted screening method, suspect screening method uses a suspect screening list of the compounds of interest in order to identify the compounds detected.

1.3. Liquid Chromatography coupled with Quadrupole Time-of-Flight

Mass Spectrometry

The goal in this project was to identify unknown chemicals in the human serum using suspect screening analysis and therefore, the instrument used must be sensitive, fast and accurate. The ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) is a chromatographic separation technique with fast separation, high sensitivity and high resolution (Khan and Ali, 2015). UHPLC uses a column with particles size less than 2 m, high-performance pump and typically elevated temperatures during the analyses.

Smaller particle size in the columns gives higher resolution and an increased sensitivity

(Cudiamat, 2013). High-performance pump means that the system can handle more pressure

while elevated temperatures give a lower viscosity of the mobile phase which allow it to move faster through the column. With QTOF/MS, the analysis is more selective and more accurate

(Constans, 2006). The quadrupole mass filter consists of 4 rods with electric voltage. Those

rods work in pairs and only ions of proper m/z value can pass through the analyzer. Quadrupole therefore separates ions by ion motion in electric fields.

QTOF is a connection between the collision cell from the quadrupole with time-of-flight analyzer (Skoog, 2012). Time-of-flight traps ions based on flight time in a tube and measure its velocity without influence of electric or magnetic fields. The ions of different masses are separated according to their kinetic energy and the time it takes for each ion to reach the detector in a known distance.

1.4. Sample extraction

Prior to instrumental analysis, samples must be extracted. Solid-phase extraction (SPE) is a commonly used method for this type of exposure studies. SPE is a step-wise chromatography used to extract, partition and/or adsorb components from a liquid phase onto stationary phase

(Fifield and Haines, 2000). SPE uses prepackaged disposable cartridges or columns containing

bonded silica sorbents (Mitra, 2003). It is a nonequilibrium extraction technique and it is used to remove chemical constituents from a flowing liquid sample. It is a fast and inexpensive extraction method. The organic solvent consumption and disposal are reduced in SPE extraction and this results in reduced analyst exposure to organic solvents and the formation of emulsions. SPE extraction improves qualitative and quantitative analysis because it will concentrate the

analytes and remove interferences and thus, simplify chromatography and improve quantitation

(Fifield and Haines, 2000).

1.5. Human exposure during pregnancy

During the pregnancy, the fetus in the first trimester is particularly more prone to getting exposure to those health risks, due to the natural development of the fetus and its nervous system, since it is rapidly developing (Perkins, 2017). Any negative health risk can affect the fetus and result in birth defects or even adverse health outcome later in life.

In order to evaluate the impact of exposure during fetus development, studies of the maternal exposure are needed, combined with information of the health outcome of the babies. The human serum in this study is a part of the Diabetes Prediction and Prevention study (DIPP study) which was aimed for the newborn susceptibility to the type I diabetes (Haller and Schatz,

2016). Ten thousand of infants have been identified for an increased genetic risk and 14% of

them have seroconverted to at least one autoantibody and 5.2% have been developed multiple autoantibodies. The DIPP study includes the children in all phases of the type I diabetes process, from the genetic susceptibility without any signs of an active disease process to the children with newly diagnosed type I diabetes. Human serum from pregnant women in the risk group and controls were used for the suspect screening analysis.

2. Aim

The purpose of this project is to characterize the environmental pollutant profile in serum extracts of pregnant women, using a suspect screening approach with chromatography combined with high-resolution mass spectrometry.

3. Method

3.1. Sample preparation

Each serum sample of 150 µL was thawed and spiked with 10 µL of 0.2 ng/mL internal standard in methanol and were added to a 25 mg Ostro Protein Precipitation and Phospholipid Removal well plate. The well plate was pre-conditioned with 450 µL acetonitrile. A 450 µL aliquot of acetonitrile containing 1% formic acid was added to the well plate and mixed with the samples using an automated pipette. The samples were extracted with a 10” vacuum manifold for 5-7 minutes. Aliquots of 600 µL of the eluate from each well was then collected and transferred to glass LC-vials and evaporated to 250 µL by using nitrogen. The purified extracts were then diluted with 750 µL of 2 mM ammonium acetate in water and were ultrasonicated for 10 minutes prior to instrumental analysis.

This step was prepared prior to this project and was stored at -80 °C until analysis.

Before instrumental analysis, the samples were sonicated in 10 minutes and centrifuged at 8000 r.p.m. for 3 minutes. The total number of samples was 206 and they were divided into 3 batches, to facilitate the data analysis.

3.2. Instrument analysis

The sample analysis was done by Agilent LC 1290 infinity II coupled with Agilent Q-TOF LC/MS 6545. LC-QTOF/MS used Dual electrospray ionization source operated in the negative polarity to the mass analyzer and detector. The experiment was operated in negative mode which facilitates the ionization of acidic compounds. A 1µL aliquot of the sample was used for each duplicate injections of the sample into a Kinetex EVO C18 100 Å (2.1 × 100 mm, 1.7 µm) column at 55°C with the flow rate of 0.400 mL/min. Gradient elution (Table 1) of two mobile phases was used for the chromatographic separation. Mobile phase A consisted of water and 0.05% ammonium acetate and mobile phase B was methanol with 0.05% ammonium acetate. The TOF-MS scanning range from 80-1000 m/z was collected at high resolution for elution out of the LC from 1-14 minutes with auto MS/MS mode. Table 2 shows the setting parameters for the QTOF LC/MS. The LC-QTOF/MS run produces a total ion chromatogram with the accurate mass of each compound, peak area, retention time and spectral data on the parent ion and fragment ions, including isotopic pattern for each sample.

Table 1: The elution gradient used in this study.

Time A [%] B [%] 0.00 95.00 5.00 0.50 95.00 5.00 1.50 70.00 30.00 4.50 30.00 70.00 7.50 0.00 100.00 10.00 0.00 100.00 14.00 95.00 5.00

Table 2: Agilent Q-TOF LC/MS 6546 source parameters.

Parameter Value

Mode 2 GHz Extended dynamic range; high

sensitivity slicer mode

Tune 50-250 m/z; Fragile ions

Drying gas temperature 225 °C

Drying gas flow 3.0 L/min

Sheath gas temperature 125 °C

Sheath gas flow 3.0 L/min

Nebulizer pressure 15 psi

Capillary voltage 3 643 V

Nozzle voltage 1 500 V

Fragmentator 175 V

Skimmer 65 V

Oct 1 RF Vpp 750 V

Acq mass range 80 – 1 050 m/z

Acq rate 4 spectra/s

3.3. Data analysis

The candidate compound list used for the suspect screening analysis was made based on literature. The data from the standard compounds used for the identification have a mass-to-charge ratio regarding the negative polarity. The suspect screening list of the human serum composed of 38 candidate compounds which can be seen in the appendix, Table A1.

Data processing (Table 3) was done by MzMine2 software for mass detection, integration and identification of the compounds.

Table 3: The data setting used for the analysis.

Values to match

Negative ions Charge carrier - 1.007 (-H) Mass detection Intensities less than this interpreted as noise 900

Minimum time span 0.08 min

Chromatogram builder m/z tolerance 0.009 m/z or 12 ppm Chromatographic threshold 60 %

Chromatogram Search minimum in retention time range 0.06 min deconvolution Minimum relative height 0.01 %

Minimum ratio of peak top 1.3

Peak duration range 0.08 – 5 min m/z tolerance 0.005 m/z or 5 ppm Isotopic peaks grouper Retention time tolerance 0.05 min

Maximum charge 2

Weight for m/z 2

Join alignment Weight for retention time 1 Minimum peaks in row 15

Peak list row filter m/z 150 – 900

Retention time 1 – 11 min

m/z 0.009 m/z or 15 ppm

Identification Retention time duration 0.5 min

3.4. Quality control

Several quality control samples were used in the study: standards with labeled internal standards (Appendix tables A2 and A3), reference plasma samples, NIST samples, water blanks and methanol blanks. The reference plasma samples were pooled serum samples and the NIST samples are from National Institution and Technology, which are the standard reference samples. All samples, internal standards, reference plasma samples, NIST samples, water and methanol blanks were treated in the same way, as the samples.

4. Results

The method used in this study was based on previous study (Gerona et al., 2018). However, some modifications have been made for this project. After optimization of the method parameters, the pooled samples and the standards were tested in different ESI polarities, to examine which of the modes works best for the human serum analysis. The negative polarity was shown to give better detection rate for the human serum and was thus chosen as the operating mode, and since the compounds of interest are easily made negatively charged, due to the ability to lose a hydrogen ion. The number of peaks increased in the negative polarity than the positive, as demonstrated in Figures 1 and 2.

Figure 1: Overlay of the base peak chromatogram for standards and pooled samples run in negative

mode.

Figure 2: Overlay of the base peak chromatogram for standards and pooled samples run in positive

4.1. Quality control

The variation of the peak areas for the internal standards was checked to see whether the variation was correlating with the running order, however, there is no correlation with the injection order of the analysis, as shown in the Figure 3.

The picture shows that most of the samples have the same detector response for the internal standards, thus some with higher peak heights. However, as illustrated, there are no differences between the batches.

Figure 3: The displaying plot of the internal standard variation (M2-4:2 FTS) giving for an overview of

the data trends, where the x-axis is the 206 samples and y-axis is the peak height.

Next, the blank samples were studied to see possible contamination due to the sample preparation and analysis. From the 206 samples with 38 candidates, eight fatty acids were found. Figure 4, shows the average peak area of the fatty acids detected in the blanks, methanol blanks, samples and the QC-samples. Some of the fatty acids were found in both the blanks and the methanol blanks, which could be due to the skincare product from the analyst. However, the detections of the fatty acids in those blanks were much lower than the real samples, which concludes that those fatty acids were present in the samples.

Figure 5, shows the average peak area of the environmental pollutants detected in the blanks, methanol blanks, samples and the QC-samples. Two compounds, namely, perfluorohexanoic acid (PFHxA) and perfluorobutane sulfonic acid (PFBuS) could be detected in the blank samples which could be due to contamination during the sample preparation.

Figure 6 illustrates the overlaid chromatograms of a sample and an extracted blank containing PFHxA. They both showed identical chromatogram for PFHxA, thus, PFHxA was assumed to be a contamination from the solvent (water) or from the sample preparation materials.

Figure 7 show the chromatograms of a sample, water blank and methanol blank containing PFBuS. The sample and the blanks show almost identical base peaks and peak intensity, therefore, this compound was concluded to be contamination and not part from the human serum.

Two more compounds had detectable levels in the instrument blank, and could be derived from the background noise or blanks, namely, 3,5-ditertbutyl salicylic acid and monooctyl phthalate. Chromatograms of 3,5-ditertbutyl salicylic acid in QC-sample, water blank and methanol blank can be seen in Figure 8. As illustrated in the figure, the base peak intensity of the sample is

As illustrated in Figure 9, monooctyl phthalate peaks in the samples and the extracted blank are not similar, which verifies that the compound is present in the serum samples and have been identified.

Figure 4: The average peak area of the fatty acids detected in the blanks, methanol blanks, samples and

QC-samples.

Figure 5: The average peak area of the environmental pollutants detected in the blanks, methanol blanks,

samples and QC-samples. 0 2000000 4000000 6000000 8000000 10000000 12000000 Av er ag e pe ak ar ea ( a. u)

Free fatty acids

Figure 6: Overlap of chromatograms of PFHxA in sample and water blank.

Figure 8: Overlap of chromatograms of 3,5-Ditertbutyl salicylic acid in sample, water blank and

methanol blank.

Figure 9: Overlap of chromatograms of monooctyl phthalate in sample, water blank and methanol

4.2. Suspect screening and identification

A total of 12 compounds out of the 38 candidates were found in this analysis in 206 human serum samples, whereas 8 of them are fatty acids and the rest are environmental pollutants. The fatty acids with high detection rate were three saturated fatty acids and five unsaturated fatty acids. Oleic acid and stearic acid were found in all the samples, as shown in Table 4.

Table 4: Fatty acids found in this study and its detection percentage. Identity (main ID)

Number of detected peaks

Samples with detected peaks

Percentage of samples with detected peaks Batch 1 Batch 2 Batch 3

Oleic acid (OA, C18:1) 69 68 69 206 100 Stearic acid (SA, C18:0) 69 68 69 206 100 Palmitic acid (PA, C16:0) 69 68 56 193 94 Linoleic acid (LIA, C18:2) 69 68 47 184 89 Myristic acid (MA, C14:0) 69 68 35 172 83 Palmitoleic acid (POA, C16:1) 69 62 35 166 81 Linolenic acid (LNA, C18:3) 69 57 35 161 78 Docosahexaenoic acid

(DHA, C22:6) 45 58 32 135 66

OA is an unsaturated fatty acid and was detected in all the 206 samples. Figure 10 shows the peak area of the OA in each sample.

SA is a saturated fatty acid and has a detection rate of 100%. Figure 11 shows how the peak area varies throughout the 206 samples.

Figure 11: Distribution of peak area of SA in the samples.

PA was the third most detected compound. It is also a saturated fatty acid and its detection rate was 94%. Figure 12 shows how its area varies in the samples.

LIA is an unsaturated fatty acid detected and its detection rate was approximately 90%. The area of LIA spread throughout the samples is shown in Figure 13.

Figure 13: Distribution of peak area of LIA in the samples.

MA is a saturated fatty acid and it has a detection rate of 84%. Figure 14 shows the areas and the variation of MA throughout the serum samples.

PA is an unsaturated fatty acid and it was found in roughly 80% of the human serum samples. Figure 15 shows the area of the PA in each sample.

Figure 15: Distribution of peak area of PA in the samples.

LNA was found in 78% of the samples. It is an unsaturated fatty acid and Figure 16 shows the variation throughout the samples.

Figure 16: Distribution of peak area of LNA in the samples.

DHA has a detection rate of 65% which is the lowest detection rate detected for fatty acids. Figure 17 shows the area of DHA in the serum samples.

Figure 17: Distribution of peak area of DHA in the samples.

Table 5 shows the identity of the environmental pollutants found in the samples and their detection rate percentages. Only four environmental pollutants were detected. The detection rates of the environmental pollutants are far less than the fatty acids and ranges from 9% to 62%.

Table 5: The identity of the environmental pollutants found in this study and their detection rate

percentages.

Identity (main ID)

Number of detected peaks Total number peaks found in the samples

Percentage of the samples found

Batch 1 Batch 2 Batch 3

PFOA 42 59 27 128 62

Monooctyl phthalate 20 35 55 27

3,5-Ditertbutyl salicylic acid 23 23 11

PFOA is one of the most common PFASs found in humans (Yuan and Leanderson, 2017). PFOA was found in all three batches and with a detection rate of 62%. The peak area of PFOA in each sample can be seen in Figure 18.

Figure 18: Distribution of peak area of PFOA in the samples.

Monooctyl phthalate was the only phthalate detected in this study. The detection rate of monooctyl phthalate is 29% and Figure 19 shows the variation of the phthalate found in the samples.

3,5-Ditertbutyl salicylic acid was found in 11% of the serum samples. Figure 20 shows the peak area of the 3,5-Ditertbutyl acid in each sample.

Figure 20: Distribution of peak area of 3,5-ditertbutyl salicylic acid in the samples.

PFOS is another common PFAS. The detection rate of PFOS is 9%, which is the lowest detection of all compounds found in this study. Figure 21 shows the area of PFOS in each sample.

5. Discussion

The 12 identified compounds were both environmental pollutants and fatty acids. This demonstrates that the method can detect both environmental contaminants as well as endogenous metabolites in the same analysis.

Fatty acids were detected in high quantities which could be due to fact that they are essential to the humans. Most fatty acids occur naturally and come from animal, vegetable fats and oils, while some are present in living tissues.

More unsaturated fatty acids were detected than saturated fatty acids. This could be a result of the dietary omega fatty acids (Mensink et al., 2003).

The levels of environmental pollutants detected in this study were significantly lower than the fatty acids. In addition, the results indicate that the individuals in the current study were not exposed in high quantities to the environmental pollutants.

However, when comparing our results with other studies, it seems that the detection rate of the environmental pollutants in this study is different from previous studies.

Many studies show a detection rate of 100% for PFOS and PFOA (Hanssen et al., 2010;

Salihovic et al., 2013) while this study shows a detection rate of 9% for PFOS and 62% for

PFOA. This low detection rate was probably due to the low sensitivity of the method for these compounds. Another consideration that should be taken into account is the fact that the DIPP study samples were collected between the year 2013 to 2016 when the levels of exposure of PFOS and PFOA have already been decreasing.

PFAS and other POPs have shown decreasing trends since 2000 due the phase-out of PFOS-related production and the treaty, Stockholm Convention on POPs, a global treaty to protect human and the environment from chemicals that are persistent (Wang et al., 2009)

A declining trend of many PFASs in blood have been seen in primiparous women (Glynn et al.,

2012, 2015) However, the trace of those will remain in the foreseeable future due to their

persistence.

As illustrated in Figure 5, the PFOS levels in NIST samples and reference plasma samples were higher than the serum samples, which is due to the fact that the NIST samples and the reference plasma samples were collected in 2001 to 2004 while the samples were collected in 2013 to 2016. The NIST and the reference plasma samples were collected before the restrictions, and therefore, the content of PFOS in those samples was in higher.

Phthalates are used widely and are usually found in consumer products, food packaging and medical devices (Meeker et al., 2009). They are rapidly metabolized and can be excreted in urine and faeces and thus, the analysis of the phthalates exposure is usually done via urinary concentrations of phthalate metabolites. The detection rate of monooctyl phthalate in this study was 29%, and it had higher levels than in blank samples. However, it is also possible that this compound is a contamination from the blood sampling, since phthalates are usually found in medical devices and equipment. Polyvinyl chloride is one of the most common used materials in medical devices and phthalates leached from these devices are a direct source of exposure

(Chou and Wright, 2006).

Ditertbutyl salicylic acid is a phenolic compound and was found in 11% of the serum samples. Salicylic acids are commonly found in skin care products but also in systemic absorption (Bozzo

et al., 2011). Oral contraceptive of salicylic acid during pregnancy could pose a potential risk

2008). However, low-dose of salicylic acid during pregnancy is relatively safe and generate

positive effects on reproductive outcomes.

6. Conclusions

The suspect screening resulted in identification of both environmental pollutants and fatty acids and the detection rate of these two types of compounds differed.

The environmental pollutants detected in current study is low and are partly due to the restriction and the instrumental malfunctioning. However, it should be noted that the results are only qualitative because, a calibration standard of the study could not be made due to the high variation of the standards and the instrumental problems and therefore, a quantification of the compounds could not be done.

7. Acknowledgements

I would like to thank all the people who have helped me with this project. It has not been easy, but thanks to your help, this project become possible.

I am deeply grateful to my supervisor Dr. Dawei Geng, for guiding and helping me in the laboratory work and I own my deepest gratitude to my supervisor Professor Tuulia Hyötyläinen for helping me and making it possible to present the results from this project.

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9. Appendix

Table A1: The candidate compound list used for the suspect screening analysis, composed of 38

compounds and were based on literature. The list was made prior to the analysis of the samples and could have a shift in retention time.

ID m/z RT Name 1 165,0560 2,66 Ethylparaben 2 138,0199 2,92 4-Nitrophenol 3 151,0403 3,38 Methylparaben 4 165,0924 3,66 4-Butoxyphenol 5 179,0716 4,52 iso-Propylparaben 6 179,0716 4,60 n-Propylparaben 7 287,0716 4,62 Benzylparaben 8 398,9369 4,66 Perfluorohexanesulfonic acid (PFHxS) 9 193,1237 4,66 4-Hexyloxyphenol

10 277,1448 4,82 Monoisooctyl phthalate or Monooctyl phthalate 11 448,9337 4,97 Perfluoroheptane sulfonic acid (PFHpS)

12 412,9667 5,02 Perfluorooctanoic acid (PFOA) 13 193,0873 5,15 Butylparaben

14 227,1081 5,17 Bisphenol A 15 193,0873 5,20 iso-Butylparaben

16 249,1499 5,27 3,5-Ditertbutyl salicylic acid

17 498,9305 5,31 Perfluorooctane sulfonic acid (PFOS) 18 462,9682 5,38 Perfluorononanoic acid (PFNA) 19 498,0000 5,46 Perfluorooctanesulfonamide (PFOSA) 20 263,0000 5,50 Perfluoro-n-pentanoic acid (PFPeA) 21 512,9603 5,59 Perfluorodecanoic acid (PFDA)

22 499,0000 5,85 1-Perfluoro-1-methyl-perfluorooctane sulfonic acid (1-PFOS) 23 313,1689 5,96 Perfluorohexanoic acid (PFHxA)

24 562,9587 5,97 Perfluoroundecanoic acid (PFUnDA) 25 177,1295 6,22 4-Hexylphenol

26 612,9549 6,22 Perfluorododecanoic acid (PFDoDA) 27 662,9507 6,46 Perfluorotridecanoic acid (PFTrDA) 28 227,2000 6,55 Myristic acid (MA, C14:0)

29 277,2182 6,64 Linolenic acid (LNA, C18:3)

30 299,2000 6,67 Perfluorobutane sulfonic acid (PFBuS) 31 253,2175 6,78 Palmitoleic acid (POA, C16:1)

32 327,2300 6,88 Docosahexaenoic acid (DHA, C22:6) 33 279,2332 6,92 Linoleic acid (LIA, C18:2)

34 255,2358 7,01 Palmitic acid (PA, C16:0)

35 363,0000 7,19 Perfluoroheptanoic acid (PFHpA) 36 281,2499 7,20 Oleic acid (OA, C18:1)

38 207,1393 7,59 4-Heptyloxyphenol

Table A2: Standards used for the quality control.

Standard compounds Methyl paraben Ethyl paraben Propyl paraben Isopropyl paraben Butyl paraben Isobutyl paraben Benzyl paraben Bisphenol A 4-butoxyphenol 4-heptyloxyphenol 4-hexylphenol 4-hexyloxyphenol 4-nitrophenol

Table A3: Native (CS), Internal standards (IS) and Recovery standards (RS) used for the quality control.

CS mix – Oxide

dimer acids CS 1 mix – PFCA; PFSA; FTSA; PFOSA IS mix – MPFCA; MPFSA; MFTS; MFOSA RS mix – MPFCA; MPFSA; MFTS

9Cl-PF3ONS PFBA M8FOSA-M M3PFBA

DONA PFPeA M2PFHxDA M3PFHxS

HFPO-DA PFHxA M2PFTeDA M8PFOS

PFHpA M2-6:2FTS M8PFOA

PFOA M2-8:2FTS M9PFNA

PFNA M3PFBS M6PFDA

PFDA M3PFPeA M7PFUnDA

PFUdA M4PFHpA M2 4:2 FTS

PFDoA M4PFBA M5PFHxA

PFTrDA M2PFHxA M3PFPeA

PFTeDA M4PFOA PFHxDA M5PFNA PFODA M2PFDA L-PFBS M2PFUndA L-PFHxS M2PFDoA L-PFOS 18O2-PFHxS L-PFDS M4PFOS FOSA 4:2FTS 6:2FTS 8:2FTS L-PFHpS L-PFDoS L-PFNS