# Quantification of chlorinated paraffins by chromatography coupled to high-resolution mass spectrometry – Part B: Influence of liquid chromatography separation

Cherine Amoura, Frédéric Larvor, Philippe Marchand, Bruno Le Bizec, Ronan Cariou, Emmanuelle Bichon

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## To cite this version:

Cherine Amoura, Frédéric Larvor, Philippe Marchand, Bruno Le Bizec, Ronan Cariou, et al.. Quantification of chlorinated paraffins by chromatography coupled to high-resolution mass spectrometry – Part B: Influence of liquid chromatography separation. Chemosphere, 2024, 352, pp.141401. 10.1016/j.chemosphere.2024.141401. hal-05133608

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Submitted on 27 Jun 2025

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# Quantification of chlorinated paraffins by chromatography coupled to high-resolution mass spectrometry – Part B: Influence of liquid chromatography separation

Cherine Amoura, Frédéric Larvor, Philippe Marchand, Bruno Le Bizec, Ronan Cariou, Emmanuelle Bichon*

Oniris, INRAE, LABERCA, F-44300, Nantes, France

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## HIGHLIGHTS

* The mobile phase, in LC conditions, influences the detection of homologue groups.

* The selectivity of the stationary phase influences the homologue group pattern.

* A C30 column reveals more PCAs–C₁₀–C₁₃ compared to other columns.

* Systematic bias in the quantification of ΣPCAs-C₁₀–C₁₃ and ΣPCAs-C₁₄–C₁₇ was observed.

* LC- and GC-HRMS methods give complementary results for the detection range of PCAs.

## GRAPHICAL ABSTRACT

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## ARTICLE INFO

Handling Editor: Leondios Leondiadis

**Keywords:**

Chlorinated paraffins
Polychlorinated alkanes
LC-ESI-HRMS
Homologue group pattern
Quantification

## ABSTRACT

The analysis of chlorinated paraffins (CPs) is today an analytical challenge. Indeed, it is still impractical to describe their real composition in terms of polychlorinated alkanes (PCAs) homologue groups, which dominate technical mixtures. The co-elution of PCA congeners generates interferences due to the competition phenomena which occur during the ionisation process as well as to the dependence of the ionisation sources on the PCA chemistry. Therefore, the aim of this study was to investigate the influence of chromatographic separation, by LC-ESI-HRMS coupling, on the PCA homologue group pattern and, eventually, on their determination in food samples from interlaboratory studies. For this, three different mobile phases and six LC chromatographic columns were studied in order to optimise the analysis of CP mixtures. The first results showed that the use of a MeOH/H₂O mobile phase reveals more appropriately the higher chlorinated PCAs. However, using ACN/H₂O led to less ion species, with almost exclusively [M + Cl]⁻ adducts, formed using post-column dichloromethane addition. Regarding the choice of the stationary phases, Hypercarb column provided a completely different homologue group pattern from the other chromatographic columns, in relation with the stronger retention of PCAs. Among the other columns, the C30 column better highlighted the short-chain PCAs compared to the C18 column conventionally used. Because the regulations now concern short-chain CPs, the quantification of food samples was then carried out on the C30 column. The optimised LC-ESI-HRMS conditions using C30 column and

<footnote>* Corresponding author.
E-mail addresses: amoura.cherine@gmail.com (C. Amoura), laberca@oniris-nantes.fr, emmanuelle.bichon@oniris-nantes.fr (E. Bichon).</footnote>
MeOH/H₂O solvent mixture led to a quantification of PCAs in samples from interlaboratory studies with satisfactory accuracy (|Z-score| ≤ 2) and precision (<15%).

# 1. Introduction

Chlorinated paraffins (CPs) are complex mixtures of polychlorinated *n*-alkanes (PCAs) with the general formula $C_nH_{2n+2-x}Cl_x$, also abbreviated $C_nCl_x$. The carbon chain length is comprised between $C_{10}$ and $C_{30}$, and the chlorine level can represent 30%–70% of the mass fraction. They are conveniently classified according to their carbon chain length into short-chain (SCCPs, $C_{10}-C_{13}$), medium-chain (MCCPs, $C_{14}-C_{17}$), and long-chain (LCCPs, $C_{18}-C_{30}$) subclasses. In the present article, we refer to Fernandes et al. (2023) for terms and abbreviations related to CPs and PCAs.

CPs have been produced since the 1930s mainly for their properties as flame retardants (rubber, paints and adhesives), plasticisers and high temperature lubricants for metallurgy. Since then and until 2015, the global production volume of SCCPs is estimated to be more than $2.5 \times 10^6$ metric tons (Glüge et al., 2016). Due to these high production volumes, a considerable amount of CPs has been released into the environment, as they were reported in sediment (Hüttig and Oehme, 2006; Wang et al., 2020), air (Niu et al., 2020; van Mourik et al., 2020) or water (Labadie et al., 2019; Vorkamp et al., 2019; Wang et al., 2019), even in remote locations, highlighting their long range transport capacity (Tomy et al., 1999). In addition, some studies have shown their occurrence in fauna, including different animal species among which reptiles (Du et al., 2020), birds (Zhou et al., 2016) and mammals (Yuan et al., 2019). Bioaccumulation and biomagnification properties of PCAs are already proven (Gawor and Wania, 2013; Ma et al., 2014). For example, Mézière et al. (2021a, 2021b) revealed that PCAs from SCCPs, MCCPs and LCCPs bioaccumulate in tissues of laying hens after dietary exposure. They also showed a transfer of PCAs into the eggs, leading to a potential human exposure.

The occurrence of PCAs in food of animal origin has already been reported (Krätschmer et al., 2018; Lee et al., 2020; Li et al., 2020). It constitutes the main route of external human exposure to those contaminants (EFSA, 2020). Food intake has also been identified as a major route of exposure of infants to PCAs–$C_{10-13}$ and PCAs–$C_{14-17}$ Gao et al. (2018); Li et al. (2020); Recently, Perkons et al. (2022) investigated the occurrence of PCAs in 86 baby foods, belonging to different food categories from 22 European countries. They highlight no health risk of SCCPs and MCCPs contamination in food. Those results are consistent with the ones obtained by Han et al. (2021) and Krätschmer et al. (2021). Regarding the internal exposure, several research groups have reported the presence of PCAs in human blood and breast milk (Thomas et al., 2006; Xu et al., 2019a; Xu et al., 2021). In China, the PCAs–$C_{10-13}$ median concentrations in breast milk were 393 and 525 ng g⁻¹ lipid and the PCAs–$C_{14-17}$ median concentrations were 472 and 567 ng g⁻¹ lipid in urban and rural areas, respectively (Xu et al., 2021). Following these results, a significant health risk exposure of infants to PCAs–$C_{10-13}$ via breastfeeding and the potential toxicity of PCAs–$C_{14-17}$ were highlighted.

Due to their persistence in the environment, their bioaccumulation, biomagnification and long range transport and toxicity, SCCPs have been classified as persistent organic pollutants (POPs) by the Stockholm Convention in May 2017. Regarding MCCPs, the 19th meeting of the persistent organic pollutant review committee, held on October 2023, recommended to the Conference of the Parties that it consider listing MCCPs, at or exceeding 45% Cl by weight, in Annex A of the Stockholm Convention (POPRC-19, 2023). With the complete ban of SCCPs and the future restriction expected for MCCPs and perhaps LCCPs, a robust quantification of PCAs will be necessary to assess the exposure of the population to these substances.

However, the quantification of PCAs still remains an analytical

challenge. Indeed, the technical mixtures that are released into the environment are composed of thousands of congeners, which could also be diastereoisomers or enantiomers (Shojania, 1999; Yuan et al., 2020). Complex technical mixtures are often used for quantification purposes, without knowing the exact composition of each mixture (proportion of isomers, homologue groups or single chains). One of the fundamental issues is that when the homologue group composition of the technical mixture differs from that of the sample to be analysed, the quantification errors are very substantial (Tomy et al., 1997; Coelhan et al., 2000).

The most widely used analytical techniques for determining PCA homologue group patterns are gas chromatography (GC) (Xu et al., 2019b; McGrath et al., 2020) and liquid chromatography (LC) (Perkons et al., 2019; Matsukami et al., 2020) coupled with high-resolution mass spectrometry (HRMS). It is therefore now possible to identify each PCA homologue group present in complex mixtures. However, their response factors may vary depending on the ionisation technique and/or the matrix effect (Wu et al., 2019). Interlaboratory studies (ILs) set up by the European Union reference laboratory for halogenated persistent organic pollutants (EURL POPs) in feed and food highlighted a significant variability of the results compared to a given theoretical value (Krätschmer and Schächtele, 2019). They concluded that the choice of the calibration standards, the data processing methods and the choice of the instrument are the main factors influencing the results.

As the aim of our study was to develop a reliable quantitative method, we assessed the impact of various liquid chromatographic separations on the characterisation of PCAs in complex CP technical mixtures. Therefore, special attention regarding interpretations should be paid to the naturally contaminated samples, which may contain PCAs that are not detected in the CP technical mixtures. For this, the influence of two mobile phase mixtures, ACN/H₂O and MeOH/H₂O, on the ionisation of PCAs, was studied. In addition, six LC columns were tested in order to highlight the influence of the chromatographic separations on the homologue group pattern. The optimised conditions were compared with those developed on a GC-ECNI-HRMS coupling published in a companion paper (Amoura et al., 2024). Finally, we present a general comparison of the homologue group patterns obtained with the optimised parameters for both methods and highlight the differences that could influence the quantification of PCAs.

# 2. Material and methods

## 2.1. Chemical and reagents

Dichloromethane (DCM), hexane and magnesium sulphate (Florisil) were purchased from LGC Promochem (Picograde quality, Wesel, Germany). Sulphuric acid (98%) was provided by Panreac (Barcelona, Spain). Silica gel (70/230 mesh, Fluka, Buchs, Switzerland) was purchased from Merck (Darmstadt, Germany). LC-MS grade acetonitrile (ACN) and LC-MS grade methanol were purchased from Carlo ERBA Reagents and VWR Chemicals BDH, respectively. Distilled water was prepared by an IQ 7000 Milli-Q water purification system (Merck). ¹³C-γ-Hexabromocyclododecane (HBCDD) and $d_{18}$-β-HBCDD were purchased from Wellington Laboratories Inc. (Guelph, Ontario, Canada) and were used as internal and recovery standards, respectively.

A CP standard mixture was prepared by combining six technical mixtures from Dr. Ehrenstorfer (Augsburg, Germany) at equal proportions, including two SCCP (51.5% and 63% Cl), two MCCP (42% and 57% Cl) and two LCCP mixtures (36% and 49% Cl).
## 2.2. Samples

The samples chosen in this study come from Interlaboratory studies (ILs) supervised by the EURL POPs. A detailed description of coconut oil (CFA to CFE) as well as lard samples (LAA to LAE) fortifications can be found in the publication of Krätschmer and Schächtele (2019). Briefly, five samples of coconut fat were provided to assess the level of CPs (CFA: not fortified; CFB: fortified with SCCP 55.5% Cl from Dr. Ehrenstorfer GmbH, Augsburg, Germany; CFC: fortified with MCCP 57% Cl from Dr. Ehrenstorfer GmbH, Augsburg, Germany; CFD: fortified with SCCP and MCCP standards, CFE: fortified with two different SCCP standards). For lard samples, five samples were also provided (LAA: not fortified; LAB and LAC: fortified with a mix of single-chain CP standards synthesised by the University of Hohenheim, Germany; LAD: fortified with CP standards and PCBs; LAE: fortified with CP standards and PBDEs). For the pork sausage sample (PO), the consensus value of CPs natural contamination of the sample was obtained. Lastly, for the infant formula, one sample was analysed (IFB: fortified with SCCP and MCCP standard mixtures).

## 2.3. Sample preparation

PCAs were co-extracted with fat by pressurised liquid extraction (SpeedExtractor E-914, Büchi) from the infant milk and the freeze-dried sausage samples. The operating conditions included three extraction cycles with a cycle time of 5 min at 120 °C and a pressure of 100 bar. The extraction solvent was toluene/acetone mixture (7:3, v/v). Then, for all samples, purification involved Florisil and acidic silica purification columns as described by Mézière et al. (2020). Extracts were reconstituted in 25 µL of ACN and stored at -20 °C until analysis.

## 2.4. Instrumentation

### 2.4.1. Chromatographic separation

A 1260 quaternary HPLC system (Thermo Fisher) equipped with a GECKO 2000 thermostated column compartment was used for this study.

Six analytical columns were assessed, among which five Thermo Scientific columns: Hypersil Gold C18-like (100 mm × 2.1 mm, i. d. 1.9 µm), Hypersil Gold PFP (100 mm × 2.1 mm, i. d. 1.9 µm), Accucore C30 (100 mm × 2.1 mm, i. d. 2.6 µm), Accucore CN (150 mm × 2.1 mm, i. d. 3 µm), Hypercarb Porous Graphitic Carbon (100 mm × 2.1 mm, i. d. 5.0 µm) and a Macherey-Nagel Nucleodur Phenyl-Hexyl (100 mm × 2.0 mm, i. d. 1.8 µm) column. The chromatographic conditions are reported in Table S1. Injection volume was 5 µL. The column compartment temperature was set at 40 °C except for the Hypercarb column (30 °C). Indeed, DCM was added in the mobile phase with the Hypercarb column to elute PCAs. Decreasing the temperature was thus necessary to prevent bubble formation due to the low boiling point of DCM (39.6 °C).

The complex mixture was also injected without stationary phase. The injection occurred with 100% of MeOH at 40 °C.

### 2.4.2. Mass spectrometry analysis

In order to promote the formation of [M + Cl]⁻ ions, an ACN/DCM mixture (50:50, v/v) was added post-column (Zencak and Oehme, 2004; Mézière et al., 2020). Analytical developments were done on a LC-ESI-Orbitrap-HRMS system, whereas the quantification of the ILs sample was done on a LC-ESI-Q-Orbitrap-HRMS for its higher sensitivity.

**ESI-Orbitrap-HRMS.** Mass spectra were acquired on an Orbitrap Exactive mass spectrometer equipped with a heated electrospray (HESI) source (Thermo Fisher Scientific) operated in the negative mode. Capillary voltage was -2.5 kV. Instrumental parameters were set as follows: sheath gas and the auxiliary gas flow rates were 30 and 12 arbitrary units (AU) respectively. HRMS data were acquired in full scan mode over the m/z range 200-1500. AGC target was set at 1 × 10⁶ and at a resolving power set at 100,000 full widths at half maximum (FWHM)

at m/z 200.

**ESI-Q-Orbitrap-HRMS.** The ILs samples were analysed on a HPLC-ESI-Q-Orbitrap-HRMS. Data were acquired in full scan mode over the m/z range 300-1500 in the negative mode, at a resolving power set to 140,000 FWHM at m/z 200.

## 2.5. Quantification method

Quantification of PCAs was performed as described in the companion paper on GC optimisations (Amoura et al., 2024), which is inspired by the methods developed by Reth et al. (2005) and McGrath et al. (2020). The quantification was done in triplicate, except for the pork sausage sample. Only the global ΣPCAs-C₁₀-₁₃ and ΣPCAs-C₁₄-₁₇ were quantified due to the lack of LCCP standards.

## 2.6. Data processing

For samples analysed by direct injection, the integration of spectro-metric data was achieved manually on the most abundant [M + Cl]⁻, [M - H]⁻ and [M + HCOOH - H]⁻ ions, using Skyline 20.2 software. The studied parameters (retention time, FWHM) were obtained thanks to the MZmine 3.3.0 software. For column comparison and quantification purposes, the integration of the peak areas (±5 ppm) was carried out automatically using CP-Seeker software (v1.1) on the most abundant ion, [M + Cl]⁻. For identification, the minimum pattern score was set at 70%. More information can be found in McGrath et al. (submitted) and Amoura et al. (2024).

To compare the chromatographic columns tested, a multivariate analysis was realised using the Statistica software. For this, all relative areas of homologue groups (ΣPCAs-C₁₀Cl₄ to C₂₁Cl₁₁, in columns) have been listed for each chromatographic column (in rows). A multivariate analysis was then performed in this data table using a principal component (PC) analysis. Relative responses were calculated by normalising the area of each homologue group toward the global ΣPCAs.

# 3. Results and discussion

## 3.1. Influence of the mobile phase on the detection of homologue groups

Nowadays, ACN/H₂O (Perkons et al., 2019; Mézière et al., 2020) and MeOH/H₂O mixture (Li et al., 2017; Zheng et al., 2019; Matsukami et al., 2020) are the two main mobile phases used in LC-MS for PCA analysis. In general, most studies dealing with the analysis of PCAs by LC-MS methods use a dopant, added either post-column (Mézière et al., 2020) or directly into the mobile phase (Zheng et al., 2019; Matsukami et al., 2020; McGrath et al., 2022), in order to promote the formation of the chlorine adducts ions [M + Cl]⁻ (Zencak and Oehme 2004). Recently, Perkons et al. (2019) showed that the [M - H]⁻ ions were also formed with the addition of a post-column dopant, whatever the mobile phase used. Then, we first intended to assess whether the presence of ACN or MeOH in the mobile phase affects the ionisation of PCAs in the same way. The interpretation was based on the analysis of their homologue group patterns. For this, a solution containing SCCP, MCCP and LCCP technical mixtures was analysed, by direct injection-ESI(-)-HRMS, with 100% ACN or 100% MeOH. The injections were done alternatively, in triplicate. No water was added into the mobile phase for direct injection. Indeed, adding water does not significantly influence the homologue group pattern (Fig. S1). A mixture of ACN/DCM was systematically and continuously injected into the source in order to promote [M + Cl]⁻ adducts.

The results first showed that the two main expected ions species ([M + Cl]⁻ and [M - H]⁻) were observed for both mobile phases (Fig. 1). According to Perkons et al. (2019), the formic adduct ions ([M + HCOOH - H]⁻) are also formed when MeOH/H₂O is used as mobile phase, due to a possible oxidation of MeOH in the ionisation source. In our case, those ions were observed with both MeOH and ACN solvents,
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Absolute area of [M + Cl]⁻ adduct ions (blue) and [M − H]⁻ ions (orange) observed with (a) ACN and (b) MeOH in the mobile phase. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

with a higher proportion of the [M + HCOOH – H]⁻ ions, compared to [M + Cl]⁻ and [M – H]⁻ ions when MeOH was used (Fig. S2). Following these observations, we hypothesise that the detection of [M + HCOOH – H]⁻ ions could be linked to the presence of residual formic acid in the high-pressure liquid chromatographic system.

In more details, the absolute intensities of the [M + Cl]⁻ ions exceeded that of the [M – H]⁻ ions using ACN or MeOH (Fig. 1a). However, the proportion of the [M – H]⁻, relative to the global sum of [M + Cl]⁻ and [M – H]⁻ ions, was higher using MeOH compared to ACN as mobile phase, especially for PCAs–C₁₀–17 homologue groups (Fig. 1 and S2). Using MeOH, the relative intensities of the [M – H]⁻ species were higher for PCAs–C₁₀–13 homologue groups compared to PCAs–C≥18. These results are in accordance with the ones published by Mézière et al. (2020). The latter showed no formation of the [M – H]⁻ ions for lower and higher chlorinated LCCP mixtures due to their less acidic properties, whereas [M – H]⁻ were still observed in our study whatever the chain length considered.

For both mobile phases, a shift of the homologue group patterns towards higher chlorinated PCAs was observed when the [M – H]⁻ ions were monitored, with almost no detection of the very lowly chlorinated PCAs under this form (Fig. 1). Indeed, lower chlorinated PCAs can only be ionised by chlorine adduct, whereas deprotonation seems to be promoted for the higher chlorinated PCAs. This last observation is in

accordance with Huang et al. (2021) who observed a higher abundance of the [M – H]⁻ ions when the degree of chlorination increases. According to Schinkel et al. (2018), the acidic strength of PCAs increases with the chlorination degree. Conversely, Perkons et al. (2019) showed that PCAs with a higher chlorination rate are less prone to deprotonation, which is likely due to the fact that they did not use chlorine adduct enhancement conditions.

In order to enhance the response of PCAs, some authors proposed to add a salt directly into the mobile phase (Zheng et al., 2019; Huang et al., 2021) or to replace chlorine adducts by bromine adducts (Yuan et al., 2018). For example, Huang et al. (2021) showed that using tetramethylammonium chloride as phase modifier leads to the formation of almost exclusively [M + Cl]⁻ adducts, which allows a better ionisation efficiency of PCAs. Adding a salt directly into the mobile phase could also be interesting in order to avoid the use of organic solvents, which are often toxic and polluting. However, vigilance on the use of salts in high-performance liquid chromatographic systems is required as they have an impact on their lifespan.

As the response factors of the homologue groups seemed to be different depending on the ion monitored (deprotonation or chlorine adduct), quantification would be affected in the end. This aspect must be taken into account when comparing LC-ESI-HRMS results. Perkons et al. (2019) studied the [M + Cl]⁻/[M – H]⁻ ratio for PCAs–C₁₀–17 from
mixtures of SCCPs and MCCPs, at different concentrations without chlorine adduct enhancement. They showed no influence of the concentration and the carbon chain length on this ratio. It would be interesting to extend those experiments to chlorine enhancement conditions. In addition, interpreting the results on distinct isomers would provide more insight. Indeed, the calculation of this ratio may bring information in the isomeric composition of a given homologue group. To respond to this hypothesis, deeper investigations are expected, which were not in the scope of this study.

As regards the mobile phase composition, ACN/H₂O mixture seemed more appropriate to gain in sensitivity for quantifying PCAs due to the prevalence of the [M + Cl]⁻ ion. However, a discrimination of the higher chlorinated PCAs is expected, with almost no response factors for these heaviest PCAs (e.g., PCAs–C₁₃-₂₀Cl₁₁). The use of a MeOH/H₂O mixture as mobile phase, despite a lower sensitivity related to the formation of [M + Cl]⁻ and [M – H]⁻ ions, allowed the detection of PCAs towards the most chlorinated ones. Moreover, in order to compare the LC columns in the best conditions, using ACN as mobile phase is not favoured. Indeed, the –C≡N bound is capable of π-π interactions with three out of six of the tested columns (Table S2). Therefore, this will annihilate some of the stationary phase properties. Regarding the two last aspects, MeOH/H₂O mobile phase mixture was chosen for further work.

## 3.2. Influence of the stationary phase on the homologue group patterns

Ionisation competition can occur in ESI-, particularly when complex mixtures are analysed. The efficiency of the analytical separation often remains a lever to improve the MS measurement. To the best of our knowledge, the influence of the LC chromatographic separation of PCAs has only been studied once, by Matsukami et al. (2020). They showed that the use of a Zorbax SB-CN RRHD column generates thinner peaks, improving the separation and the detection of homologue groups, compared to the conventionally used C18-like columns. In order to assess the influence of the stationary phases on the PCA response, we compared their retention properties toward the number of carbon and chlorine atoms, and full width at half maximum (FWHM). Then, the impact of each studied stationary phase on the pattern was assessed by principal component (PC) analysis. For this study, a mixture of SCCPs, MCCPs and LCCPs technical mixtures was analysed in triplicate, with optimal conditions specific to each column (Table S2).

### 3.2.1. Retention behaviour according to hydrophobic interactions

In order to better understand the interactions between PCA congeners and LC stationary phases, a screening of six LC columns, with a wide selectivity range, was realised. The first selection of the LC columns was based on their increasing hydrophobic properties: C18-like, C30 and Hypercarb columns. In fact, it is well known that the hydrophobicity of PCA congeners is mainly linked to the number of carbon atoms, and in the second level, to the number of chlorine atoms, as already stated in the literature by the calculation of the otanol/water coefficient (Bettina et al., 2011).

The second selection was based on polar properties of the stationary phases. In a recent study, Hammer et al. (2021) highlighted the polar nature of certain PCA congeners, with hydrogen donor properties, via the study of different GC chromatographic columns. In order to highlight possible polar interactions by LC chromatography, we selected a cyanopropyl phase (CN) for the large dipole moment of the –C≡N group (Marchand et al., 2005a). A pentafluorophenyl column (PFP) was also selected based on its strong dipole interactions and its high selectivity regarding halogenated compounds (Marchand et al., 2005b). Then, the phenyl-hexyl column was selected based of the higher polarisability of the aromatic group compared to aliphatic groups (Marchand et al., 2005b).

Before discussing retention, the global information given on the homologue group pattern with each column was roughly studied because the highest chlorinated PCAs were often undetected using

Hypercarb and CN columns (Fig. S3), leading to an uncompleted qualitative pattern compared to the other columns. Both were then excluded from further quantification purposes.

Retention was then more precisely studied on the four other columns. A relative retention time (RRT) (reported to the highest homologue group retention time (RT) of the same analysis) was calculated for each homologue group. The retention time of a given homologue group was measured at the apex of the chromatographic hump. The results are illustrated on Fig. 2 and S4. As expected, the retention globally increased with the size of the PCAs. More precisely, the increase of RRTs seemed to be tightly linked with the number of carbon atoms for all columns, highlighting that major hydrophobic interactions take place for the separation of PCAs (Fig. 2, Table S3). Looking at Fig. S4, the extent of the RRTs was greater on the C30 column compared to the other columns, whatever the chlorination degree (e.g., from 13% to 100% and around 20%–100% for the C30 and other columns, respectively, PCAs–C₁₀-₂₀Cl₅). In parallel, RRTs of PCAs–C₁₀-₁₃ increased for a same chain length with the chlorine atom number on these four columns, whereas PCAs–C₁₄-₁₇ presented different elution profiles between C18-like and C30 columns compared to the phenyl-hexyl and PFP columns. Surprisingly, there was a slight decreasing of the RRT with the number of chlorine atoms between 4 and 7. For example, a continuous decrease was observed for PCAs–C₁₆ when the chlorine number increased (e.g., from 78 to 69% and 66 to 52% for C18-like and C30 columns, respectively). In contrast, the RRT still increased when the chlorine atom number increased for phenyl-hexyl and PFP columns (e.g., RRT in continuous increasing from 45% to 54% and from 44% to 52% for phenyl-hexyl and the PFP columns respectively). Concerning PCAs–C≥₁₈, the decrease of the RRT was still observed on the C18-like and C30 columns, with a more pronounced effect on the C30 column (e.g., 100%–92% and 100%–86%, respectively, for PCAs–C₂₀Cl₅-₈ homologue groups), while the RRT still increased for the phenyl-hexyl column (e.g., 88%, 89%, 89%, 90%, PCAs–C₁₉Cl₅-₈) or stabilised on the PFP one (e.g., 92%, 91%, 91%, 91%, PCAs–C₁₉Cl₅-₈). Then, for higher chlorinated PCAs–C≥₉, the RRTs increased or stabilised whatever the chain length, for all columns (Fig. S4).

It has already been demonstrated that the apolar character of PCAs increases with the number of carbon and chlorine atoms, as shown by the calculated octanol/water partition coefficients (K<sub>ow</sub>, Bettina et al., 2011). Therefore, it was expected to observe an increase in retention with the number of carbon and chlorine atoms on the C18-like and C30 columns, which selectivities depend essentially on hydrophobic interactions (Table S2). This result could be explained by a possible folding of the PCAs–C≥₁₄Cl₄-₇, thus reducing their retention time in the chromatographic column. Concerning the phenyl-hexyl and PFP columns, the number of chlorine atoms did not reverse the retention for PCAs–C≥₁₄. Therefore, different interactions between PCAs with the stationary phases could explain the results observed or, possibly, there might not be any folding of PCAs–C≥₁₄ on these columns.

### 3.2.2. Chromatographic full width at half maximum (FWHM)

The sensitivity of the PCAs measurements could be affected by co-elutions and ion suppression between PCA congeners. While the retention capability is crucial, as we just showed, the chromatographic peak shape and width can also influence the response factors. In this study, it was assumed that the width of a chromatographic peak depends on both the amount of homologue groups present in the initial bulk mixture as well as the number of existing isomers for each homologue group. As the number of isomers increases with the number of carbon and/or chlorine atoms, it is expected that the FWHM increases with the carbon chain length and the chlorination degree. This phenomenon was particularly well observed with the Hypercarb column (Fig. 3a and S5), which shows the highest retention for PCAs (from 2 min for the short chains (e.g., PCAs–C₁₀Cl₅) to 50 min for longer chains (e.g., PCAs–C₁₈Cl₇)). Therefore, this column led to a better chromatographic separation of isomers belonging to a same homologue group. Indeed, a marked broadening
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** XY scatter chart representing the relative retention time according to the homologue group obtained for each column. C18-like in blue, C30 in orange, CN in grey, Phenyl-hexyl in yellow, PFP in purple and Hypercarb in green. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

was noticed from n = 14 and/or x = 10 (Fig. S5). The Hypercarb column could then be very interesting to go further in the comprehension of the isomer composition of a given homologue group, via structural analysis for example. In addition, Fernandes et al. (2022) have shown that the response factors of isomers can vary greatly. Thus, it could be one of the reasons explaining the originality of the homologue group pattern obtained with this column. In contrast, lower FWHMs were obtained for phenyl-hexyl and PFP columns (Fig. 3b and c). For the phenyl-hexyl column (Fig. 3b), the FWHM seemed to be less impacted by the number of carbon atoms. There was also a slight tendency to increase in peak

width with the number of chlorine atoms. Larger peak widths were observed for the higher chlorinated PCAs–C20-21 (remaining lower than 4 min). For the PFP column (Fig. 3c), a bell shape of peak widths according to the number of carbon atoms was observed (excluding PCAs–C21). The broadening of the peaks was correlated with the number of chlorine atoms, with a bell effect too. In general, for both columns, the number of carbon and chlorine atoms seemed to have less impact on peak width compared to the four other columns. This result could be attributed to a proportional relation between peak width, isomers number and quantity in a same homologue group. However, this

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** 3D-histograms representing the FWHM according to the number of carbon and chlorine atoms for each LC-column: a) Hypercarb; b) Phenyl-hexyl; c) PFP; d) C30; e) C18-like; f) CN.</figcaption></figure>
hypothesis has to be confirmed when a known amount of homologue
groups can be mixed. Nevertheless, as the isomers of a same homologue
group are eluted in a shorter time, a stronger competition for ionisation
can take place within a given homologue group using these two col-
umns. For the C30 column (Fig. 3d), we observed an enlargement of the
peaks according to the number of carbon and chlorine atoms, up to n =
18 (except for the PCAs–Cl₄) and n = 17, respectively. Then, we
observed a bell shape according to the number of chlorine atoms. Lastly,
for the C18-like (Fig. 3e) and the CN (Fig. 3f) columns, no particular
trend could be observed. Therefore, in order to diminish ionisation
competition between isomers, a good compromise would be to use the
C30 column.

3.2.3. *Comparison of the homologue group patterns*

A comparison of the homologue group patterns was carried out by PC
analysis and the results are shown in Fig. 4. PCs were built from the
results obtained on the six chromatographic columns and the direct-
Injection conditions (Fig. 4a). The first and second PCs represented
69% and 18% of the global variability, respectively. Whereas the
phenyl-hexyl, C30, Hypersil Gold, CN and PFP columns plotted nearby
on these two axes, a high variability of patterns was underlined after a
separation on a Hypercarb column or without any separation step
(direct-injection condition, no column). After direct injection, the lower
chlorinated PCAs were not or barely detected (for PCAs–Cl₄) (Fig. S6).
This could be explained by the more pronounced interferences between
PCAs that occur during the ionisation process. Separation on the
Hypercarb column also led to a different homologue group pattern
compared to the five other columns, as we have already shown (Figs. S3
and S6). Therefore, as the results obtained in direct-injection and with
the Hypercarb column represent the main part of the variability for the
first PC, these two datasets were set aside to compare the 5 other col-
umns with each other through a second PC analysis (Fig. 4b). This time,
the two PCs represented 43% and 37% of the variability. According to
the first component, the major differences appeared between the PFP
and the CN columns. Looking at the circle of correlations (Fig. S7), the
first component seemed to separate the homologue groups according to
their chlorine level. Indeed, the lower chlorinated homologue groups
seemed to have a positive correlation according to the first component,
whereas the strongly chlorinated homologue groups seemed to have a
negative correlation. This observation was again confirmed by looking
at the homologue group pattern (Fig. S6). Indeed, the CN column
seemed to better reveal the higher chlorinated PCAs–Clunlike the
PFP column.

Concerning the second component, the C30 and the C18-like showed

the highest variabilities with the C30 column positively correlated and
the C18-like negatively correlated to it (Fig. 4b). Looking at the circle of
correlations (Fig. S7), PCAs–C10–13 have positive coordinates according
to the second component (e.g., PCAs–C10Cl8, C10Cl9, C11Cl7, C11Cl9,
C12Cl5, C13Cl6) whereas the PCAs with longer chains (PCAs–C18–20)
present negative coordinates. Therefore, the C30 column seems to better
reveal PCAs–C10–13 while PCAs–C≥18 seem to be better revealed using
the C18-like column. This conclusion is well illustrated on the homo-
logue group pattern shown on Fig. S6, where the relative areas of the
lower chlorinated PCAs–C10–13 (PCAs–C10Cl7–8, C11Cl5–8, C12Cl5–7 and
C13Cl5–6) are higher than on the other columns. Based on these results,
the choice fell on the C30 column to carry out the quantification studies
in order to diminish the ionisation competitions between isomers and
because it seemed to better reveal the PCAs–C10–13 compared to the C18-
like column conventionally used.

3.3. *LC-ESI-HRMS method verification*

The performances of the selected method (MeOH/H₂O mobile and
C30 stationary phases) under optimised conditions were assessed
through an application to samples from interlaboratory studies (ILs)
previously set up by the EURL POP.

Firstly, the range of PCAs quantified in the ILs samples included ΣPCAs-C10-13 and ΣPCAs-C14-17 subclasses with a number of chlorine atoms between 4 and 12. The pork sausage and IFB samples were not selected to compare the quantitative performance of our method because of the high uncertainty on these values. Two different types of patterns were revealed, depending on whether the samples were environmentally contaminated or spiked. The quantitative results with the associated standard deviation and Z-scores values are reported in Table 1.

The quantification of the ILs samples fell within the acceptability
criteria for the method used in this publication (|Z-score| ≤ 2), except
for the quantification of ΣPCAs-C10-13 for the LAE sample, with a Z-score
= -2.1. The relative standard deviations were lower than 15%, which
indicates satisfying precision in the quantification of ΣPCAs-C10-13 and
ΣPCAs-C14-17 (Table 1).

A linear correlation was observed between the calculated values and the assigned values for ΣPCAs-C10-13 (R² = 0.98) and ΣPCAs-C14-17 (R² = 0.99) on the fortified samples (coconut fat and lard), as shown in Fig. 5. Considering the slopes of the linear regressions, a tendency to the underestimation of the concentrations was observed for the quantification of the ΣPCAs-C10-13 (slope = 0.67) and even more for the quantification of the ΣPCAs-C14-17 (slope = 0.5). These slopes correspond to

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Principal component analysis carried out with a) C18-like, C30, CN, phenyl-hexyl, PFP and Hypercarb LC columns and a no column condition, and b) without the Hypercarb LC column. The variables correspond to the relative abundances of each homologue group detected (C10Cl4 to C21Cl11) toward the global ΣPCAs, and the individuals correspond to the columns tested.</figcaption></figure>
<caption>**Table 1**</caption>

Average chlorine content (%), concentrations of ΣPCAs-C<sub>10-13</sub> and ΣPCAs-C<sub>14-17</sub>, standard deviation (SD) and associated mean Z-scores from analysis of EURL ILs samples.

<table><thead><tr><th rowspan="2">EURL<br>sample</th><th rowspan="2">Sample</th><th colspan="3">ΣPCAs-C<sub>10-13</sub></th><th colspan="3">ΣPCAs-C<sub>14-17</sub></th></tr><tr><th>Average chlorine content<br>(%) ± SD (provider value)</th><th>Concentration in ng g<sup>-1</sup> lipids<br>± SD (consensus value)</th><th>Z-score (σ<br>= 25%)</th><th>Average chlorine content<br>(%) ± SD (provider value)</th><th>Concentration in ng g<sup>-1</sup> lipids<br>± SD (consensus value)</th><th>Z-score (σ<br>= 25%)</th></tr></thead><tbody><tr><td>CFA</td><td>Coconut</td><td>59 ± 2 (//)</td><td>21 ± 12 (//)</td><td>/</td><td>49 ± 3</td><td>15 ± 5 (//)</td><td>/</td></tr><tr><td>CFB</td><td>Fat</td><td>58 ± 0.2 (55.5)</td><td>192 ± 11 (300)</td><td>-1.4</td><td>53 ± 1</td><td>18 ± 3 (//)</td><td>/</td></tr><tr><td>CFC</td><td></td><td>59 ± 2.1 (//)</td><td>17 ± 5 (//)</td><td>/</td><td>57 ± 0 (57)</td><td>156 ± 9 (300)</td><td>-1.9</td></tr><tr><td>CFD</td><td></td><td>58 ± 0.1 (//)</td><td>82 ± 5 (120)</td><td>-1.3</td><td>58 ± 1</td><td>97 ± 7 (180)</td><td>-1.8</td></tr><tr><td>CFE</td><td></td><td>62 ± 3.1 (//)</td><td>202 ± 15 (200)</td><td>0.0</td><td>53 ± 0</td><td>12 ± 1 (//)</td><td>/</td></tr><tr><td>LAA</td><td>Lard</td><td>31 ± 45 (//)</td><td>3 ± (//)</td><td>/</td><td>27 ± 39</td><td>4 ± 6 (//)</td><td>/</td></tr><tr><td>LAB</td><td></td><td>59 ± 0.8 (//)</td><td>35 ± 3 (69)</td><td>-1.9</td><td>53 ± 1</td><td>36 ± 5 (56)</td><td>-1.4</td></tr><tr><td>LAC</td><td></td><td>58 ± 0.9 (//)</td><td>38 ± 3 (67)</td><td>-1.8</td><td>54 ± 0</td><td>110 ± 3 (203)</td><td>-1.8</td></tr><tr><td>LAD</td><td></td><td>59 ± 0.5 (//)</td><td>20 ± 1 (31)</td><td>-1.5</td><td>54 ± 0</td><td>53 ± 2 (94)</td><td>-1.8</td></tr><tr><td>LAE</td><td></td><td>58 ± 0.4 (//)</td><td>72 ± 6 (149)</td><td>-2.1</td><td>54 ± 0</td><td>60 ± 4 (121)</td><td>-2</td></tr><tr><td>PO-1905</td><td>Pork<br>sausage</td><td>NA</td><td>18 ± NA (13)</td><td>1.5</td><td>NA</td><td>27 ± NA (48.4)</td><td>-1.8</td></tr><tr><td>IFB</td><td>Infant<br>milk</td><td>62 ± 0.2 (//)</td><td>136 ± 12 (121)</td><td>0.5</td><td>55 ± 0</td><td>89 ± (163)</td><td>-1.8</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Relation of concentrations of a) ΣPCAs-C<sub>10-13</sub> and b) ΣPCAs-C<sub>14-17</sub> in ILs samples, obtained using C30 and MeOH/H<sub>2</sub>O chromatographic conditions vs. assigned values. Pork sausage and IFB samples were not taken into account due to high uncertainty on these values.</figcaption></figure>

the systematic bias obtained for the quantification of ΣPCAs-C<sub>10-13</sub> and ΣPCAs-C<sub>14-17</sub> using this developed method.

One of the hypotheses is that the fortified samples were enriched using technical mixtures from Dr. Ehrenstorfer which are characterised by GC-ECD. However, it has already been proven that CP impurities can be found in those mixtures (Zheng et al., 2019; Fernandes et al., 2022; McGrath et al., 2022). For example, McGrath et al. (2022) showed that “SCCPs only accounted for 71% of the CPs measured in the SCCP 55.5%CI standard, with MCCPs and LCCPs constituting 17% and 12% of the mixture, respectively”. Because those impurities are most probably taken into account during the analysis by GC-ECD, it could partly explain the underestimation of the levels. Moreover, by using LC-ESI-HRMS, the very lowly chlorinated PCAs–Cl<sub>≤3</sub> are not detected, which could explain the overestimation of the chlorine content obtained for these mixtures compared to the value assigned by the provider (Table 1).

## 3.4. Comparison with GC-ECNI-HRMS

### 3.4.1. Homologue group patterns comparison for fortified samples

In this part, a comparison of the homologue group patterns for the fortified ILs samples between the optimised LC-ESI-HRMS and GC-ECNI-

HRMS methods optimised in the present work and by Amoura et al. (2024) was carried out. For these two methods, similar sample preparation steps, quantification method and data processing methods were carried out. The main divergence lies in the choice of the internal standards (¹³C-HBCDD and ¹³C-HCB for LC-ESI-HRMS and GC-ECNI-HRMS, respectively). The homologue group patterns obtained for CFA and LAA samples are not shown as they are non-fortified samples.

For the fortified coconut oil samples (Fig. S8), the patterns according to chain lengths were similar to those observed by GC-ECNI-HRMS (Fig. S11 from Amoura et al., 2024), except for the CFB and CFE samples. For the CFB sample, enriched with a SCCPs 55.5% Cl technical mixture, the pattern obtained by GC-ECNI-HRMS showed a prevalence of chain lengths in the C<sub>13</sub> > C<sub>12</sub> > C<sub>11</sub> direction, whereas the pattern obtained by LC-ESI-HRMS showed a prevalence of chain lengths in the direction C<sub>11</sub> ≈ C<sub>12</sub> > C<sub>13</sub>. This observation was also valid for the CFE sample enriched with two SCCP technical mixtures (55.5% Cl and 63% Cl).

As for GC-ECNI-HRMS, there was a Gaussian-like distribution of the chlorine content for a given chain length observed by LC-ESI-HRMS. As already reported in the literature, using LC-ESI-HRMS methods made it
possible to detect the PCAs–Cl₄.₅ while they are not or poorly detected by GC-ECNI-HRMS for all the fortified samples (Reth et al., 2005; Hüttig and Oehme, 2006).

Looking at the distribution of homologue groups as a function of the number of chlorine atoms, the LC-ESI-HRMS methods seemed more representative of the chlorine content of the technical mixtures, especially for the lower chlorinated ones. For example, the expected dominant homologue groups for the SCCP 55.5% Cl technical mixture are PCAs–C₁₀Cl₄ (51% Cl), C₁₀Cl₅ (56% Cl), C₁₁Cl₅ (54% Cl), C₁₂Cl₆ (52% Cl), C₁₂Cl₅ (56% Cl), C₁₂Cl₅ (60% Cl), C₁₃Cl₅ (50% Cl), C₁₃Cl₅ (54% Cl) and C₁₃Cl₇ (58% Cl). The Gaussian pattern obtained by LC-ESI-HRMS showed a prevalence of PCAs–C₁₁Cl₅.₇, C₁₂Cl₅.₈ and C₁₃Cl₅.₈ (Fig. S8a) while PCAs–C₁₁Cl₇.₈, C₁₂Cl₇.₈ and C₁₃Cl₇.₉ were dominant by GC-ECNI-HRMS (Fig. S11a from Amoura et al., 2024). These results were also valid for the enriched coconut oil samples, and were even more obvious for the CFE sample enriched with both a lower and a higher chlorinated mixture (SCCP 55.5% Cl + SCCP 63% Cl). Indeed, higher chlorinated homologue groups were dominant by GC-ECNI-HRMS (PCAs–C₁₁Cl₇.₉, C₁₂Cl₇.₁₀ and C₁₃Cl₇.₁₁, Fig. S11d from Amoura et al., 2024) while a larger panel was observed by LC-ESI-HRMS (PCAs–C₁₁Cl₅.₈, C₁₂Cl₅.₁₀ and C₁₃Cl₅.₁₀, Fig. S8d), as expected.

This Gaussian-like distribution according to the number of chlorine atoms was also observed for fortified samples of lard (Fig. S9) and infant milk (Fig. S10), enriched with both SCCP and MCCP mixtures. As with coconut oil samples, a shift towards higher chlorinated homologue groups was observed between GC-ECNI-HRMS (Figs. S12 and S13 from Amoura et al., 2024) and LC-ESI-HRMS. As per the patterns according to the chain lengths, they were different whatever the methods used and no particular rule appeared for differentiating them. The patterns displayed by LC-ESI-HRMS showed a prevalence of PCAs–C₁₄Cl₆ for all the samples, whereas the PCAs–C₁₄Cl₈ were dominant in the LAB, LAC and LAD samples by GC-ECNI-HRMS. For the LAE and IFB samples, PCAs–C₁₂Cl₈ and C₁₁Cl₉ were dominant, respectively.

### 3.4.2. Comparison of homologue group patterns for the pork sausage sample (“naturally” contaminated)

As for the fortified samples, this part discusses the homologue group patterns of the pork sausage sample analysed by GC-ECNI-HRMS and LC-ESI-HRMS (Fig. S14 from Amoura et al., 2024 and Fig. S11). Globally, it can be seen that the detection range of PCAs was lower using the LC-ESI-HRMS coupling (C₁₀.₂₀ and Cl₄.₁₀) compared to the GC-ECNI-HRMS coupling (C₁₀.₂₀ and Cl₅.₁₃). More precisely, the GC-ECNI-HRMS method allowed to detect the PCAs–C₁₀.₁₇ to a larger extent. However, the LC-ESI-HRMS coupling remains more appropriate for PCAs–C≥₁₈. These two methods appeared complementary to widen the chlorine detection range of PCAs, from Cl₄ to Cl₁₃. However, some homologue groups were not detected by LC-ESI-HRMS (e.g., PCAs–C₁₀Cl₈.₉, C₁₁Cl₈.₁₀, C₁₂Cl₈.₁₁), while they were detected in the samples enriched with technical mixtures. This could be explained by a low response factor of these homologue groups or by a possible ion suppression, which can occur in ESI. The fact that higher chlorinated PCAs–Cl≥₁₁ were not detected by LC-ESI-HRMS in the pork sausage sample is in accordance with the results presented in Fig. 1, as only [M + Cl]⁻ ions were monitored.

### 3.4.3. Perspectives

To conclude, this part highlighted the main differences in the PCA homologue group patterns obtained by the GC-ECNI-HRMS and LC-ESI-HRMS methods. These two approaches appear to be complementary in order to have a broad view of the homologue groups that may be present in a given sample. It is possible to improve PCA analysis using these two methods by working on other parameters. For example, for the GC-ECNI-HRMS method, studying the pressure of the reactant gas (in our case methane) could improve the detection of lower chlorinated PCAs (Cl₄.₅). Indeed, according to Hites et al. (1988), increasing the source pressure increases the negative ion response. The choice of the reactant gas is also

relevant because it can affect both the pressure in the source and the PCA response factors. For example, Rosenfelder and Vetter (2009) showed that using nitrogen as a reactant gas allowed to gain in sensitivity in the detection of certain polybrominated flame retardants and to obtain more uniform response factors (except for the 2,4,6-tribromophenol) compared to methane. For LC-ESI-HRMS, one way to expand the detection range of PCAs will be to monitor both the [M + Cl]⁻ and [M – H]⁻ ions. The quantification with the sum area of the [M + Cl]⁻ and [M – H]⁻ ions would be interesting if the ratio [M + Cl]⁻/[M – H]⁻ remained constant for a given homologue group, whatever the concentration in chlorine enhancement conditions. Further improvements of chromatographic conditions and ionisation source parameters are needed to favour the formation of those two ions. In addition, this study made it possible to reveal the influence of the choice of the LC columns on the chromatographic parameters (retention, peak width) and the PCA homologue group patterns. The results show that using the C30 column, the obtained pattern will better highlight the PCAs–C₁₀–C₁₃ over the other PCA subclasses (PCAs–C₁₄–C₁₇ and PCAs–C≥₁₈). Moreover, through a better understanding of the mechanisms of chromatographic separation of PCA congeners by LC, future developments such as the use of LC-2D can be considered with the aim of going further in the simplification of technical mixtures of CPs. Thus, improvement in quantification of PCAs are expected. For PCA response factors, as the matching between the sample and the standard mixtures remains the main issue, further researches are necessary to better understand the ionisation mechanism in ESI and ECNI and the interferences between the PCA congeners.

## 4. Conclusion

An optimised LC-ESI-HRMS method was proposed in this study for the quantification of ΣPCAs in food matrices. After assessing the influence of the mobile phase on the analysis of PCAs, the use of a MeOH/H₂O mobile phase by direct-injection can broaden the detection range of PCAs by monitoring both [M + Cl]⁻ and [M – H]⁻ ions. We confirmed that the stationary phase has a major impact on the observed homologue group patterns. Using the C30 column gives the best results in terms of global elution of PCAs, with a wider retention window compared to other columns (Hypercarb excluded as already discussed). Moreover, compared to the C18-like column commonly used for the analysis of PCAs, the observed homologue group pattern tends to better reveal PCAs–C₁₀.₁₃ compared to PCAs–C≥₁₈, which seems to be more interesting since SCCPs are classified as POPs. For quantification purposes, satisfactory results were obtained in the quantification of both ΣPCAs–C₁₀.₁₃ and ΣPCAs–C₁₄.₁₇. A systematic bias was highlighted and further developments are needed to correct these biases. Improvements can be made, such as the use of the third generation internal standards (¹³C-labelled), which are more representative of CP products, as already stated in the companion paper.

## Funding

The authors acknowledge the French Ministry of Agriculture and Food, General Directorate for Food (DGAl) for its financial support.

## CRediT authorship contribution statement

**Cherine Amoura:** Writing – original draft, Methodology, Formal analysis, Conceptualization. **Frédéric Larvor:** Formal analysis. **Philippe Marchand:** Conceptualization. **Bruno Le Bizec:** Funding acquisition. **Ronan Cariou:** Writing – review & editing, Supervision, Project administration, Methodology, Conceptualization. **Emmanuelle Bichon:** Writing – review & editing, Supervision, Project administration, Methodology, Conceptualization.
Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2024.141401.

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Supplementary information

Quantification of chlorinated paraffins by chromatography coupled to
high-resolution mass spectrometry –
Part B: influence of liquid chromatography separation

Cherine AMOURA, Frédéric LARVOR, Philippe MARCHAND, Bruno LE BIZEC, Ronan CARIOU,  
Emmanuelle BICHON

Oniris, INRAE, LABERCA, F-44300 Nantes, France
<table><caption>**Table S1.** Table representing the selected columns with the optimised chromatographic conditions used.</caption><thead><tr><th>Column</th><th>Manufacturer/Supplier</th><th>Dimensions</th><th>A</th><th>B</th><th>Chromatographic conditions</th></tr></thead><tbody><tr><td>C18-like</td><td>Thermo Scientific<br>C18 selectivity</td><td>100 mm × 2.1 mm, 1.9 µm</td><td rowspan="4">H<sub>2</sub>O</td><td rowspan="4">MeOH</td><td>70% B (1 min)<br>70% to 100% B (30 min)</td></tr><tr><td>Phenyl-hexyl</td><td>Macherey-Nagel<br>Nucleodur</td><td>100 mm × 2.0 mm, 1.8 µm</td><td>100% B (7 min)</td></tr><tr><td>C30</td><td>Thermo Scientific<br>Accucore</td><td>100 mm × 2.1 mm, 2.6 µm</td><td>80% B (1 min)<br>80% to 100% B (20 min)</td></tr><tr><td>PFP</td><td>Thermo Scientific<br>Hypersil Gold</td><td>100 mm × 2.1 mm, 1.9 µm</td><td>100% B (7 min)</td></tr><tr><td>CN</td><td>Thermo Scientific</td><td>150 mm × 2.1 mm, 3 µm</td><td rowspan="2">MeOH</td><td rowspan="2">DCM</td><td>50% B (1 min)<br>50% to 100% B (50 min)</td></tr><tr><td>Hypercarb</td><td>Thermo Scientific</td><td>100 mm × 2.1 mm, 5.0 µm</td><td>100% B (7 min)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>0% B (1 min)<br>0% to 60% B (60 min)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>60% B (6 min)</td></tr></tbody></table>

For column comparison, optimised elution gradients were used in order to compare the responses in the best analytical conditions. For the Hypersil Gold and the phenyl-hexyl columns, the same MeOH/water gradient was used (Table S1). For the C30 Column, a gradient with 10% less water at the first step was used, due to its more retentive properties. For the CN and the PFP columns, the improvement of the retention of PCAs was needed. For this, a higher proportion of water was added in the initial conditions (Table S1). Then, concerning the Hypercarb column, improving the elution of PCAs was required. Therefore, DCM was added into the mobile phase in order to elute the most hydrophobic PCAs, in particular PCAs-C≥18. We hypothesised that adding DCM into the mobile phase did not influence the ionisation of PCAs, because the DCM added post-column was estimated to be in excess.
<figure><img src="image_10.png" /><figcaption>**Figure S1.** Relative area of [M + Cl]⁻ ions of PCAs-C₁₀-₁₃ homologue groups, towards the global ΣPCAs-C₁₀-₁₃, with different proportions of water in a mixture with a) ACN and b) MeOH organic solvent (n = 3).</figcaption></figure>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Figure S2.** Relative areas of [M + Cl]⁻ (blue), [M − H]⁻ (orange) and [M + HCOOH − H]⁻ (grey) ions, towards the global sum of ions, obtained with a mixture of MeOH/H₂O (left) for each PCA subclass a) PCAs-C₁₀-₁₃, b) PCAs-C₁₄-₁₇ and c) PCAs-C≥₁₈, and ACN/H₂O (right) for each PCA subclass d) PCAs-C₁₀-₁₃, e) PCAs-C₁₄-₁₇ and f) PCAs-C≥₁₈.</figcaption></figure>
<table><caption>**Table S2.** Stationary phase selectivities.</caption><thead><tr><th></th><th>Hydrophobic</th><th>Shape</th><th>dipole-dipole</th><th>π-π</th><th>Ion-exchange</th><th>Hydrogen bonding</th></tr></thead><tbody><tr><td>C18</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C30</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td></tr><tr><td>CN</td><td>x</td><td></td><td>x</td><td></td><td>x</td><td>x (acceptor)</td></tr><tr><td>Phenyl-hexyl</td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>x (acceptor)</td></tr><tr><td>PFP</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x (acceptor)</td></tr><tr><td>Hypercarb</td><td>x</td><td></td><td></td><td>x</td><td></td><td></td></tr></tbody></table>
<figure><img src="image_13.png" /><figcaption>**Figure S3.** Relative retention time (RRT), reported to the highest homologue group retention time (RT), of detected PCA homologue groups represented by a graphic radar drawn from the lowest to the highest number of carbon and chlorine atoms. The RT was measured at the crude apex of the chromatographic hump. Each chromatographic column is represented with a given color: C18-like in blue, C30 in orange, CN in grey, Phenyl-hexyl in yellow, PFP in purple and Hypercarb in green.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Figure S4.** Number of chlorine atoms according to the relative retention time (RRT), reported to the highest homologue group retention time (RT), of detected homologue groups, for each chromatographic column: a) Hypercarb, b) C18-like, c) C30, d) Phenyl-Hexyl, e) PFP and f) CN.</figcaption></figure>
<table><caption>**Table S3.** Slopes of the linear regressions obtained from a multiple regression analysis: **a** correspond to the slope of the linear regression of the RRT of each homologue group according to the number of carbon atoms and **b** correspond to the slope of the linear regression of the RRT of each homologue group according to the number of chlorine atoms.</caption><thead><tr><th></th><th>C18-like</th><th>C30</th><th>CN</th><th>PH</th><th>PFP</th><th>Hypercarb</th></tr></thead><tbody><tr><th>a</th><td>0.064103</td><td>0.069081</td><td>0.067129</td><td>0.071815</td><td>0.074658</td><td>0.087221</td></tr><tr><th>b</th><td>0.023494</td><td>0.009605</td><td>0.026827</td><td>0.032222</td><td>0.026114</td><td>0.02042</td></tr></tbody></table>
<figure><img src="image_15.png" /><figcaption>**Figure S5.** Extracted ion chromatograms (± 10 ppm) of selected homologues groups with ten chlorine atoms for a) C18-like, b) CN, c) C30, d) phenyl-hexyl, e) PFP and f) Hypercarb.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Figure S6.** Influence of the column stationary phase composition on a) PCAs-C₁₀-₁₃, b) PCAs-C₁₄-₁₇ and c) PCAs-C≥₁₈ homologue group patterns. Each separation condition is represented by a colour: C18-like (blue), C30 (orange), CN (grey), phenyl-hexyl (yellow), PFP (purple), Hypercarb (green) and no column (brown).</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Figure S7.** Correlation circle with the variables corresponding to the relative abundance, toward the global ΣPCAs, of each homologue group detected (C₁₀Cl₄ to C₂₀Cl₁₁) and the individuals corresponding to the columns tested. The correlation circle was obtained from the principal component (PC) analysis that includes the relative areas of PCA homologue groups obtained from the C18-like, C30, Phenyl-hexyl, PFP and CN columns.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Figure S8.** Relative area, towards the global ΣPCAs, and relative standard deviations (n = 3) for each homologue group detected on a) CFB, b) CFC, c) CFD and d) CFE samples.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Figure S9.** Relative area, towards the global ΣPCAs, and relative standard (n = 3) deviations for each homologue group detected on a) LAB, b) LAC, c) LAD and d) LAE samples.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Figure S10.** Relative area, towards the global ΣPCAs, and relative standard deviations for each homologue group detected for the infant milk sample (n = 3).</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>**Figure S11.** Relative area of each homologue group detected for the pork sausage sample (n = 1).</figcaption></figure>