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# Quantification of polychlorinated alkanes in French market foodstuffs utilizing recent advances in liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry

Cherine Amoura, Emmanuelle Bichon, Antonin Padioleau, Philippe Marchand, Bruno Le Bizec, Gaud Dervilly, Ronan Cariou*

Oniris, Inrae, Laberca, 44300 Nantes, France

## ARTICLE INFO

**Keywords:**

Chlorinated paraffins

Vegetable oils

Foods of animal origin

Quantification

Dietary exposure

## ABSTRACT

Dietary intake has been highlighted as the main route of human exposure to polychlorinated n-alkanes (PCAs). In this study, we investigated the dietary exposure of the French population to PCAs by analysing 34 vegetable oil and margarine samples as well as in 50 foods of animal origin, often reported as the most contaminated food items in the human diet. Levels were determined using both LC/ESI-HRMS and GC/ECNI-HRMS couplings, for further comparison. For vegetable oil samples, precisions, trueness and uncertainties were < 15 %, ≤22 % and 7–25 %, respectively. For foods of animal origin, higher variability was observed between short- and medium-chain PCAs, as well as between instruments. Average estimated daily intakes were 7.9 and 100 ng per kg of body weight per day for $\sum$PCAs-C$_{10-13}$ and $\sum$PCAs-C$_{14-17}$, respectively. Preliminary risk characterisation indicated no health concern for the considered food categories as regards $\sum$PCAs-C$_{10-13}$ and $\sum$PCAs-C$_{14-17}$.

## 1. Introduction

Chlorinated paraffins (CPs) are industrial chemical mixtures produced in high volume since the 1930s. They are mainly used as plasticisers, flame retardants and lubricants in metallurgical industries. The main constituents of CP mixtures are polychlorinated *n*-alkanes (PCAs, $C_nH_{2n+2-x}Cl_x$), although additives or by-products such as branched alkanes among others have also been detected (Sprengel & Vetter, 2020; Zikto & Arsenault, 1974). Depending on the carbon chain length, mixtures are classified as short-chain CPs (PCAs-C$_{10-13}$), medium-chain CPs (PCAs-C$_{14-17}$) and long-chain CPs (PCAs-C$_{18-30}$). The chlorine content can vary from 30 to 70 % by mass.

In 2017, SCCPs have been classified as persistent organic pollutants (POPs) within the Stockholm Convention, limiting their production and use as chemicals. More recently, the persistent organic pollutants review committee recommended the listing of MCCPs with chlorine content exceeding 45 % by mass to the Annex A of the Stockholm convention (POPRC-19, 2023). At the time of the writing, no restrictions are in force for LCCPs.

Short and medium-chain PCAs meet the requirements of Annex XIII based on the REACH criteria of persistence, very high persistence,

bioaccumulation, very high bioaccumulation and toxicity. It is therefore necessary to assess the exposure of populations to these contaminants of emerging concern. Human exposure to PCAs through food, dust ingestion, inhalation and skin has already been reported, and food consumption is the main route of exposure (Yuan et al., 2022). In recent years in Europe, the occurrence of PCAs-C$_{10-17}$ was reported in foodstuffs in Sweden (Swedish Food Agency, 2017), Germany (Krätschmer et al., 2021b), Belgium (McGrath, Limonier, et al., 2021) and Czech Republic (Tomasko et al., 2021). Among food categories, numerous studies have demonstrated that animal products and fat-containing products such as fat and vegetable oil show the highest PCA levels (McGrath, Limonier, et al., 2021; Shen et al., 2023; Yuan et al., 2022).

The most widely used analytical techniques for determining PCA homologue group patterns are liquid chromatography (LC) (Perkons et al., 2019) and gas chromatography (GC) (Xu et al., 2019) coupled with high-resolution mass spectrometry (HRMS). However, the quantification of PCAs still remains an analytical challenge. According to Krätschmer and Schächtele (2019), it is difficult to obtain consensual values between laboratories, the choices of calibration standards and of the instrument being among the main factors influencing the results.

Based on these results and in order to investigate the dietary

<footnote>* Corresponding author at: Laboratoire d'Étude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, Route de Gachet, Nantes F-44307, France. E-mail addresses: laberca@oniris-nantes.fr, ronan.cariou@oniris-nantes.fr (R. Cariou).</footnote>
exposure of the French population to PCAs-C10-17, we selected 84 food items including 34 vegetable oils and margarines and 50 foods of animal origin. Sample extracts were split and analysed using both LC/HRMS fitted with chloride-enhanced electrospray ionisation (ESI) and GC/HRMS fitted with electron capture negative ionisation (ECNI) in order to compare method performances and quantitative results as secondary objective.

## 2. Material and methods

In the present article, we refer to Fernandes et al. (2023) for terms and abbreviations related to CPs and PCAs.

### 2.1. Standards and chemicals

Technical mixtures of SCCP (nominal chlorination degrees of 51 %, 55.5 % and 63 % Cl) and MCCP (42 %, 52 % and 57 % Cl) from Dr. Ehrenstorfer (Augsburg, Germany) were used as calibration standards. Intermediate chlorination mixtures were prepared by combining the medium chlorinated mixtures with the low or high chlorinated ones (1:1, w/w), according to McGrath et al. (2020). ¹³C₃-Labelled congeners of hexachlorinated alkanes from C₁₁ to C₁₆ were purchased from Chiron AS (Trondheim, Norway) and used as internal or recovery standards (Table S1). Details on these standards as well as on solvents and other chemicals are provided in the Supplementary material.

### 2.2. Samples

All samples were selected based on the third French individual and national food consumption survey (INCA3) conducted by the French agency for food, environmental and occupational health and safety in 2014–2015 (Anses Dubuisson et al., 2017). A detailed description of the samples can be found in Tables S2 and S3.

Thirty-four samples including 28 vegetable oils and 6 margarines were purchased in 2023 from supermarkets around Nantes, France. For vegetable oils, it included 7 olive oils (5 extra virgin oils, 1 refined oil and 1 mix extra virgin/refined oil), 7 sunflower oils (5 refined and 2 extra virgin oils), 7 rapeseed oils (5 refined and 2 extra virgin oils), 2 oil mixtures, 2 cooking oils and 3 palm oils (2 refined and 1 crude oil).

Fifty food samples of animal origin were purchased from French markets or supermarkets in 2021–2022. The sampling plan has been detailed in the publication of Godéré et al. (2024). Briefly, it included 3 animal fats (butter), 3 egg samples, 21 fish and fish products, 17 meat and meat products and 6 milk and dairy products.

### 2.3. QA/QC samples

For each of the three analytical batches of vegetable oils and margarines, a procedural blank and a quality control (QC) sample were used. The procedural blank was put through all the purification steps, and did not contain any matrix. The QC sample was a coconut oil sample supplemented with technical mixtures of SCCP 55.5 % and MCCP 57 % Cl from Dr. Ehrenstorfer prepared by the European Union reference laboratory for halogenated persistent organic pollutants in feed and food (EURL POPs). More details about the sample composition can be found in the publication of Krätschmer and Schächtele (2019).

All food samples of animal origin were analysed within five analytical batches, each including a procedural blank and a QC sample. Procedural blanks were submitted to the experimental procedures, from the extraction step to the analysis, and were analysed like the samples. A fish tissue (ERM-100CE, JRC, Geel, Belgium) was used as QC sample. The certificate provided reference values for $\sum PCAs-C_{10-13}Cl_{4-12}$ (31 ± 9 ng/kg) and $\sum PCAs-C_{14-17}Cl_{4-10}$ (44 ± 17 ng/kg).

### 2.4. Sample preparation

Food samples (except oil and fat samples) were freeze-dried for about 72 h, crushed using a stainless-steel mechanical grinder and stored at -20 °C until analysis (Godéré et al., 2024). Fat and PCA co-extraction was carried out by microwave assisted extraction (Monowave Extra, Anton Paar Graz, Austria) from 2 g of dried sample spiked with the internal standard mixture. Extraction was realised twice with a mixture of toluene/acetone (7:3, v/v) at 120 °C during 15 min. Separation of the phases was achieved after 10 min of centrifugation at 900g. Then, the organic phases were combined in a pre-weighed flat-bottom flask and concentrated to ~1 mL using a rotary evaporator and then left to dry under fume hood for 48 h.

The butter and margarine lipid phases were separated at 80 °C. One-gram portions of the lipid phases were aliquoted for purification.

The purification steps, involving acidic silica and Florisil columns, were performed according to Mézière (2020), starting from extracted lipids or 1 g aliquot for butter, margarine or vegetable oil. All extracts were reconstituted in 30 µL of a solution of recovery standard (¹³C₃-C₁₃Cl₆) in acetonitrile.

### 2.5. Instrumentation

The purified extracts were split into two equal volumes for LC/ESI-HRMS and GC/ECNI-HRMS analyses along with standard calibration curves, according to previously optimised methods (Amoura et al., 2024a, 2024b). For the LC coupling, analyses were performed on an Ultimate 3000 LC system coupled to a Q Exactive Orbitrap HRMS (Thermo Fischer Scientific) fitted with an ESI probe. Extracts were loaded on an Accucore C30 chromatographic column (100 mm × 2.1 mm, 2.6 µm; Thermo Scientific) and separated using water (A), acetonitrile (B) and isopropanol/acetone (1:1, v/v) (C). For the GC coupling, analyses were performed on a 1310 GC device equipped with a TriPlus RSH autosampler and coupled to a Q Exactive mass spectrometer (Thermo Fisher Scientific, San José, CA, USA). Separation occurred on an Optima 5HT column (12.5 m × 0.25 mm, 0.10 µm; Macherey Nagel, Germany). PCAs were ionised by ECNI at 200 °C with methane as reagent gas (flow rate of 1.2 mL/min). Further details are provided in the Supplementary material.

### 2.6. Signal deconvolution

Proprietary data files from LC- and GC/HRMS were converted into the mzXML format using MSConvert (v3.0.23056-80d75d6) and extensive peak integration was carried out using CP-Seeker software v2.1.1 (McGrath et al., 2024). For LC/ESI-HRMS data, the $[M + Cl]⁻$ adduct ion was considered. For GC/ECNI-HRMS data, $[M - Cl]⁻$ and $[M - HCl]⁻$ ions were considered and the quantification was based on the sum area of those two ions. CP-Seeker parameters were selected as follows: mass tolerance, ±3 ppm; peak width, 5–300 s; retention time, 1–27 min; missing scans, 10. Each PCA homologue group was considered as identified above a minimum pattern score of 70 %.

### 2.7. Calibration method

The quantification of $\sum PCAs-C_{10-13}$ and $\sum PCAs-C_{14-17}$ was based on the method developed by Reth et al. (2005) and performed according to Amoura et al. (2024a). Briefly, the quantification of PCAs is based on a double regression calculation method. The first regression correlates the abundances of $\sum PCAs-C_{10-13}$ and $\sum PCAs-C_{14-17}$ with the concentrations in the corresponding technical mixtures. Then, the second regression corrects the slopes of each first linear regressions with the apparent chlorination degree of technical mixtures. In the present study, nine external calibration points were used for each PCA subgroup: 0.2; 0.5; 1.0; 2.5; 5; 10; 15; 20 and 30 ng/µL. The first regression was modelled by reporting the sum areas of PCAs-C10-13 or PCAs-C14-17,
corrected by the corresponding sum for internal standards (see §2.1), according to the concentrations of the calibration solutions. The second regression represents the slope of the first regression with the apparent chlorination degree. Samples falling outside the range of apparent chlorination degree were considered outliers. For these outliers, indicative quantification was extrapolated considering the response factor of the highest chlorine content calibration point.

## 2.8. Method validation

The performance parameters determined in this work were the limit of quantification (LOQ), the apparent recovery rate, the precision, the trueness and the uncertainty on the ΣPCAs-C₁₀–₁₃ and ΣPCAs-C₁₄–₁₇. Details about the calculation of each parameter are given in the Supplementary material.

## 2.9. Dietary intake

Estimated daily intakes (EDIs) were calculated using the average amount of each food sample type consumed daily for adults and the maximum concentration of PCAs. Sample grouping and consumption data are detailed in Table S4. For samples below LOQ, the concentration was set to the LOQ value (upper bound approach). For outlier samples, the concentration was set to the LOQ value as well, considering that it did not significantly influence the estimates. Such deterministic approach was considered sufficient for a preliminary assessment of the dietary exposure.

# 3. Results

LC/ESI-HRMS and GC/ECN-HRMS are the main analytical techniques used for PCA quantification (Krätschmer et al., 2021b; Li et al., 2017). Krätschmer and Schächtele (2019) highlighted that the choice of the instrument, the data processing method and the choice of calibration standards are the main factors influencing the quantification results. In order to study the influence of the instrumental choice on PCA quantification, food samples extracts were injected on both LC- and GC/HRMS couplings. Therefore, the same sample preparation methods, labelled internal and recovery PCA standards, calibration solutions and data processing method were used for evaluating and comparing method performances.

## 3.1. Method performances

Many authors validate their method by determining LOD and/or LOQ, apparent recovery rates, precision and trueness. In the present work, we also determined the measurement uncertainties in the quantification of PCAs using LC- and GC/HRMS couplings thanks to fortified samples from interlaboratory studies and a CRM. Indeed, uncertainty measurement is rarely reported while the quantitative results require this information.

### 3.1.1. Procedural blank and LOQ determination

#### 3.1.1.1. Vegetable oils and margarines.
For vegetable oils, the ΣPCAs-C₁₀–₁₃ and ΣPCAs-C₁₄–₁₇ levels in the three procedural blanks were below the lowest point of the external calibration (equivalent to 6 ng/g lipid weight – l.w.). Indeed, the average concentrations of ΣPCAs-C₁₀–₁₃ and ΣPCAs-C₁₄–₁₇ for the three procedural blanks were 0.6 ± 0.6 ng/g and 1.6 ± 0.3 ng/g, respectively, for the LC coupling (Table S5). Regarding the GC coupling, the average concentrations were 0.15 ± 0.07 ng/g and 1.2 ± 0.7 ng/g, respectively. Therefore, the limit of quantification (LOQ) was determined as the first external calibration point. These LOQ values are similar to those obtained by Shen et al. (2023) who analysed vegetable oils by LC/ESI-HRMS (8 and 7 ng/g l.w.

for ΣPCAs-C₁₀–₁₃ and ΣPCAs-C₁₄–₁₇, respectively). Because the ΣPCAs-C₁₀–₁₃ and ΣPCAs-C₁₄–₁₇ concentrations in the blank samples were from ten to one hundred times lower than the LOQ, the results regarding vegetable oils and margarines samples were not blank-corrected.

#### 3.1.1.2. Foods of animal origin.
For foods of animal origin, the average concentrations of ΣPCAs-C₁₀–₁₃ in procedural blanks were 3.4 ± 1.5 ng/g and 0.4 ± 0.2 ng/g for LC and GC couplings, respectively. The average concentrations were 24.8 ± 10.2 ng/g and 42 ± 24 ng/g for ΣPCAs-C₁₄–₁₇, using LC and GC couplings, respectively (Table S5). One out of the 5 values was discarded from these calculations due to an atypical and non-representative homologue group pattern (vinyl chloride oligomers likely arising from column clamps) (Fig. S1). The PCA contamination in the procedural blanks was higher in batches of food samples of animal origin. The main differences between sample operating procedures were lyophilisation and extraction steps. The lyophilisation did not seem to be responsible of PCA contaminations (Padioleau, 2024). Therefore, MAE associated to concentration before purification were identified as a significant source of contamination. Contamination patterns were mostly composed of PCAs-C₁₄–₁₅ (Fig. S1a). Background contamination of PCAs has already been assessed in other studies (McGrath et al., 2022; McGrath, Limonier, et al., 2021) and the results are consistent with our findings despite paid particular attention (dedicated over-pressurised room, bench and glassware cleaning).

For ΣPCAs-C₁₀–₁₃, the LOQ was determined at the first external calibration point (0.2 ng/µL or 6 ng/g). For ΣPCAs-C₁₄–₁₇, due to a higher procedural contamination, the LOQ was fixed at 2.5 ng/µL (75 ng/g). For foods of animal origin, except butter samples, which were not extracted, a blank correction was applied. The average relative area of homologue groups, towards the sum of appropriate internal standard, detected in the blank samples, was subtracted to the relative area of the corresponding homologue group detected in the sample.

### 3.1.2. Apparent recovery rates

In vegetable oil and margarine samples, apparent recovery rates for PCAs-C₁₀–₁₃ were 140 ± 27 % and 108 ± 20 %, using LC and GC couplings, respectively (Table S6). For PCAs-C₁₄–₁₇, recoveries rates were 128 ± 28 % and 159 ± 36 % using LC and GC coupling, respectively. In foods of animal origin, apparent recovery rates for PCAs-C₁₀–₁₃ were 117 ± 17 % and 166 ± 19 % using LC and GC couplings, respectively (Table S7). For PCAs-C₁₄–₁₇, apparent recovery rates were 118 ± 21 % and 186 ± 20 % using LC and GC couplings, respectively.

For vegetable oil and margarine samples, the results were consistent between both couplings. However, for foods of animal origin, recoveries obtained by GC/HRMS were significantly higher than those obtained by LC/HRMS. This discrepancy lies in the fact that these numbers stand for apparent recovery rates, which differ from recovery rates as they are not dissociated from matrix effects. Therefore, the higher apparent recovery rates observed are likely due to ion enhancement from matrix effects. Nevertheless, the results were considered satisfactory in terms of repeatability (CV ≤ 36 %).

### 3.1.3. Precision

Acceptable precision of quantified values (<15 %) were obtained for ΣPCAs-C₁₀–₁₃ for both couplings and matrices (Tables S8 and S9). Similar results were obtained for ΣPCAs-C₁₄–₁₇, (<15 %, n = 3) in coconut fat matrix. However, for the fish tissue matrix, the relative standard deviation values were comprised between 37 and 42 % using both methods.

### 3.1.4. Trueness

In our study, the trueness was calculated for each PCA subgroup with both couplings (Tables S8 and S9). For coconut fat D, the relative biases
$\sum$PCAs-C$_{10-13}$ were 20 % and 22 % using LC- and GC/HRMS, respectively, and were lower than $\pm$10 % for $\sum$PCAs-C$_{14-17}$ using both couplings. According to the EURL POPs criteria on relative bias in food and feed, which are in between $\pm$50 %, the trueness on coconut fat was considered satisfactory (EURL POPS, 2021). For fish tissue, the relative bias remained acceptable for $\sum$PCAs-C$_{10-13}$ using LC/HRMS and GC/HRMS analysis ($\pm$7 and $\pm$27 %, respectively). For $\sum$PCAs-C$_{14-17}$, the results exhibited relative biases up to $\pm$21 % and $\pm$131 % for LC/HRMS and GC/HRMS, respectively.

### 3.1.5. Uncertainty

In oil, whatever the PCA chain length and the instrument, uncertainties ranged from 8 to 25 % (Table S8). For food of animal origin, the measurement uncertainties were similar between instruments, i.e. 37 % and 29 % in LC and GC couplings, respectively, for $\sum$PCAs-C$_{10-13}$ (Table S9). Those results showed that both couplings give similar analytical performances regarding the quantification of $\sum$PCAs-C$_{10-13}$ in food matrices, which included precision and uncertainty. Regarding $\sum$PCAs-C$_{14-17}$, uncertainty values were similar whatever the instrument, with 57 % and 59 % in LC- and GC/HRMS, respectively. The main component of the uncertainty for $\sum$PCAs-C$_{14-17}$ seemed to be the higher

uncertainty on the reference value (39 %).

## 3.2. Foodstuffs

### 3.2.1. $\sum$PCAs-C$_{10-13}$

Using the LC coupling, the overall detection frequency (DF) of PCAs-C$_{10-13}$ was 18 % (15 out of 84 samples, Fig. 1, Table S10). $\sum$PCAs-C$_{10-13}$ were quantified in 10 out of 34 vegetable fat samples (29 %). The concentrations ranged from 5.2 to 302 ng/g w.w.. The highest levels of $\sum$PCAs-C$_{10-13}$ were found in the crude palm oil and an extra-virgin sunflower oil (302 and 89 ng/g w.w., respectively). Concerning foods of animal origin, $\sum$PCAs-C$_{10-13}$ were quantified in 5 out of 50 samples (10 %). PCAs-C$_{10-13}$ were not quantified in butter samples. The values ranged from <1 ng/g w.w. (egg, shrimps, trout and milk) to 6.1 ng/g w. w. (salmon), with a median concentration of 0.9 ng/g w.w., all food categories combined (Fig. 2, Table S11).

Using the GC coupling, PCAs-C$_{10-13}$ were quantified in 17 % of the samples (14 out of 84; Fig. 1). For the vegetable oils and margarines, PCAs-C$_{10-13}$ were detected in 3 out of 7 olive oils, 1 out of 7 sunflower oils and 1 out of 3 palm oil samples. The concentrations ranged from 9.6 to 287 ng/g w.w. (Table S10). As observed using the LC coupling, the

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Number of quantified samples for $\sum$PCAs-C$_{10-13}$ (a) and $\sum$PCAs-C$_{14-17}$ (b), number of samples with value <LOQ for $\sum$PCAs-C$_{10-13}$ (c) and $\sum$PCAs-C$_{14-17}$ (d), and number of outlier samples for $\sum$PCAs-C$_{10-13}$ (e) and $\sum$PCAs-C$_{14-17}$ (f) using LC and GC couplings. The proportion of samples calculated in each category and coupling is given in bracket. The total number of samples was 84. Outliers are samples falling outside the calibration range of overall chlorination degree.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Minimum, median and maximum log-scale concentrations (quantified values; ng/g w.w.) of ΣPCAs-C10-13 and ΣPCAs-C14-17 for each food category, analysed either by LC or by GC couplings. EV: extra-virgin; RF: refined; Marg.: Margarines; Shellf.: Shellfishes; prod.: products.</figcaption></figure>

same crude palm oil and extra virgin sunflower samples showed the highest concentrations of ΣPCAs-C10-13 (287 and 71.1 ng/g w.w., respectively). Regarding foods of animal origin, PCAs-C10-13 were not detected in butter and egg samples. In the same way as with LC/HRMS coupling, ΣPCAs-C10-13 were quantified in two shrimp samples, salmon and merguez. Using GC/HRMS, ΣPCAs-C10-13 were quantified in the third shrimp sample, queen scallop, two tuna samples and one milk sample. The concentrations ranged from 0.24 to 14 ng/g w.w., with a median concentration of 0.72 ng/g w.w.. Pork chop and goat cheese fell outside the chlorination degree with GC/HRMS and were quantified using LC/HRMS only. For the GC coupling, the highest concentration of ΣPCAs-C10-13 was found in a salmon sample (14 ng/g w.w.) (Fig. 2, Table S11).

### 3.2.2. ΣPCAs-C14-17

For LC coupling, PCAs-C14-17 appeared more prevalent than PCAs-C10-13 in all food categories, with a DF of 46 % (39 out of 84 samples; Fig. 1). PCAs-C14-17 were detected in 100 % of the olive, sunflower and rapeseed oils, whether extra virgin or refined. They were also detected in 100 % of the oil mixtures, cooking oils, margarines and in the crude palm oil. The concentration range was comprised between 5.1 and 792 ng/g w.w.. The highest levels of ΣPCAs-C14-17 were detected in the two extra-virgin sunflower oils and in the crude palm oil (792, 405 and 556 ng/g w.w., respectively; Fig. 2, Table S10). Regarding foods of animal origin, ΣPCAs-C14-17 were quantified in 7 out of 50 samples (14 %). Like for PCAs-C10-13, there were no PCAs-C14-17 in butter samples. ΣPCAs-C14-17 were quantified in 4 fish and fish products and in 3 meat and meat products. The concentrations ranged from 1.6 to 27.0 ng/g w. w., with a median concentration of 17 ng/g w.w.. The highest concentrations of ΣPCAs-C14-17 were found for the pork chop, salmon, trout and bacon samples, with concentrations of 27, 19, 18 and 17 ng/g w.w., respectively (Fig. 2, Table S11).

Using the GC coupling, PCAs-C14-17 were quantified in 48 % of the

samples (40 out of 84; Fig. 1). ΣPCAs-C14-17 were quantified in 100 % of the olive oil samples, whether extra virgin or refined and 100 % of the cooking oil samples. The concentrations ranged from 4.7 to 986 ng/g w. w.. As for the LC coupling, the highest levels of ΣPCAs-C14-17 were observed in the two extra-virgin sunflower oil samples and in the crude palm oil (986, 722 and 507 ng/g w.w., respectively; Fig. 2, Table S10). Regarding foods of animal origin, ΣPCAs-C14-17 were quantified in 13 out of 50 samples (26 %). ΣPCAs-C14-17 were quantified 4 fish and fish products samples, 7 meat and meat product samples, 1 egg sample and 1 dairy product. The concentrations ranged from 2 to 52 ng/g w.w., with a median value of 19 ng/g w.w.. The highest concentrations of ΣPCAs-C14-17 were found in pork chop, pork liver, pork smocked lardon and trout, with concentrations of 52, 35, 33 and 33 ng/g w.w., respectively (Table S11).

### 3.3. Dietary intake

Table 1 provides EDIs obtained for adults based on an upper bound approach and on maximum concentration of PCAs for each food sample type as detailed Table S12. The daily intake of ΣPCAs-C14-17 was approximately 13 times higher than PCAs-C10-13. Regarding PCAs-C10-13, the relative contribution of each food category was as follows: vegetable oils and fats (42 %) > meat and meat products (21 %) > milk and milk products (18 %) ≈ fish and fish products (17 %) > eggs (2 %). For PCAs-C14-17, it ranged similarly with vegetable oils and fats (48 %)

<caption>**Table 1**</caption>

Estimated daily intakes (EDIs) (mg/kg b.w./day) calculated for ΣPCAs-C10-13 and ΣPCAs-C14-17, benchmark dose (mg/kg b.w./day) (EFSA, 2020), and associated margin of exposure (MOE).

<table><thead><tr><th></th><th>EDI</th><th>BMDL10</th><th>MOE</th></tr></thead><tbody><tr><td>ΣPCAs-C10-13</td><td>7.9 × 10<sup>-6</sup></td><td>2.3</td><td>2.9 × 10<sup>5</sup></td></tr><tr><td>ΣPCAs-C14-17</td><td>100 × 10<sup>-6</sup></td><td>36</td><td>3.6 × 10<sup>5</sup></td></tr></tbody></table>
> meat and meat products (31 %) > milk and milk products (15 %) > fish and fish products (5 %) > eggs (2 %) (Fig. 3, Table S13).

# 4. Discussion

## 4.1. Limitations to the accurate determination of the PCA exposure

### 4.1.1. Calibration range according to the chlorination degree

Nowadays, the Reth et al. quantification method is among the two most used strategies for the quantification of PCAs, along with the Bogdal et al. method (van Mourik et al., 2015). Although this method takes into account the apparent chlorination degree in the quantification and by this way gives an incomplete correction of response factors depending on the instrument used, several limitations occur. Firstly, the observed homologue pattern in the sample should be similar to the one observed in the technical mixtures included in the calibration solutions to minimise the measurement bias (Coelhan et al., 2000). Secondly, a high number of external calibrate solutions is required to properly cover all the expected apparent chlorination degree in the food samples. In our study, 45 calibration points including 9 concentration levels and 5 different chlorine contents were used. Their analysis duration increased the batch acquisition time, which limits their applicability in routine analysis. Thirdly, many homologue groups were not detected in the calibration solutions, whereas they are present in the samples. The presence of these homologue groups in the samples can affect both the homologue pattern and the determination of the apparent chlorination degree. It is therefore necessary to take clear dispositions regarding those PCAs. In this study, we decided to disregard these homologue groups for the quantification purpose. The ranges of homologue groups detected and quantified in the Dr. Ehrenstorfer technical mixtures, using LC- and GC/HRMS couplings, are presented Fig. S2. Using LC/HRMS, the PCA detection range in all technical mixtures combined was PCAs-C₁₀Cl₅-₉, PCAs-C₁₁-₁₃Cl₄-(n-1) and PCAs-C₁₄-₁₇Cl₄-₁₂. For the GC/HRMS coupling, the PCA detection range was PCAs-C₁₀Cl₆-₁₀, PCAs-C₁₁-₁₃Cl₅-n and PCAs-C₁₄-₁₇Cl₄-₁₂.

Using LC- and GC/HRMS couplings, the first regressions were linear in the concentration range from 0.2 to 30 ng/µL (Fig. S3). Looking at the second regressions, both couplings showed an exponential correlation (Fig. S4), in line with the literature (Amoura et al., 2024a, 2024b; McGrath et al., 2020).

For the LC coupling, the slope increased by about 3.5 and 5.5 times

for PCAs-C₁₀-₁₃ and PCAs-C₁₄-₁₇ when the nominal chlorination degree increased from 51 to 63 % and 42 to 57 %, respectively (Fig. S5a). For the GC/HRMS coupling, the slope increased by factors of about 19 and 38 for PCAs-C₁₀-₁₃ and PCAs-C₁₄-₁₇, as the nominal chlorination degree increased from 51 to 63 % and 42 to 57 %, respectively (Fig. S5b). Those results highlight that the chlorination degree influences the slope for both couplings, with a major incidence in GC/HRMS as already reported (Wu et al., 2019; Zencak & Oehme, 2004). Therefore, these results confirmed that the chlorination degree must be considered in the calibration strategy when MS is used for PCA quantification. Nevertheless, it can be a source of quantitative distortion if the range is not fully covered or well described.

### 4.1.2. Apparent chlorination degree

The apparent chlorination degree calculated for the technical mixtures using LC- and GC/HRMS couplings were higher than the ones displayed by the supplier (Table S14), which were in line with previous studies (Gao et al., 2016; Reth et al., 2005). For the SCCP mixtures, the relative bias between the chlorine contents disclosed by the supplier and the apparent chlorination degrees ranged from 1.7 to 10.3 % with LC/HRMS and from 3.8 to 16.7 % with GC/HRMS. For the MCCP mixtures, the relative bias ranged from 3.0 to 21.9 % with LC/HRMS and from 5.6 to 31.2 % with GC/HRMS. Higher biases were observed with GC/HRMS. Moreover, these relative biases were greater for MCCP compared to SCCP technical mixtures. Indeed, MCCP technical mixtures contained a significant proportion of lower chlorinated PCAs (Cl<sub>≤4</sub>) that were poorly or not detected by those two analytical couplings. The apparent chlorination degree obtained with the GC coupling, for the same technical mixtures, were similar to the ones obtained by Gao et al. (2016) who used GC/ECNI-TOF-MS. However, in other studies, the apparent chlorination degrees were similar to those displayed by the provider (Matsukami et al., 2020; Yuan et al., 2017). These authors attributed a response factor (RF) to each detected homologue group that takes into account the chlorination degree. In the present work, we preferred not to use this approximation because, as already proven, the chemistry of the molecule, such as the position of the chlorine atoms, can greatly influence the RF of PCAs (Fernandes et al., 2022). As a result, the apparent chlorination degree induces an additional bias on the quantified values, which is less important in LC/ESI-HRMS but still exists.

In addition, the calculated apparent chlorination degrees covered a wider range with LC- than with GC/HRMS measurement. Indeed, it

<figure><img src="image_5.png" /><figcaption>Fig. 3. Relative contribution of each food category to the estimated dietary intake for ΣPCAs-C₁₀-₁₃ (left) and ΣPCAs-C₁₄-₁₇ (right).</figcaption></figure>
ranged from 56.7 to 64.1 % Cl and from 51.2 to 58.7 % Cl for SCCPs and MCCPs, respectively, while it ranged from 60.1 to 65.4 % Cl and from 55.1 to 60.2 % Cl for SCCPs and MCCPs, respectively, using the GC coupling (Table S14). Using LC/HRMS, it was possible to detect PCAs-Cl₄ whereas it was not the case with GC/HRMS (Fig. S2). Consequently, the homologue patterns in technical mixtures were shifted towards lower chlorinated PCAs with LC coupling compared to the GC coupling (Fig. S6), which highlights the weight of those lowly chlorinated homologue groups in the calculation of the apparent chlorination degree.

## 4.2. Method performances

Regarding the determination of LOQs, the values obtained for ∑PCAs-C₁₀–₁₃ in food samples (6 ng/g w.w.) are in accordance with those reported in the literature (Krätschmer et al., 2021b; McGrath, Poma, et al., 2021). However, our method showed a higher LOQ (75 ng/g w.w.) for the quantification of ∑PCAs-C₁₄–₁₇ in lyophilised and extracted food samples. Therefore, improvements regarding the sample preparation are necessary, in particularly as regards the handling of the procedural contamination. Importantly, our quantified values are in accordance with those of the literature for the fortified coconut fat samples (McGrath, Poma, et al., 2021) and the fish sample for ∑PCAs-C₁₀–₁₃ (van Mourik et al., 2018). Concerning the trueness criterion, the interlaboratory studies conducted by Krätschmer and Schächtele (2019) showed average biases of 33 ± 69 % and 22 ± 98 % for the quantification of ∑PCAs-C₁₀–₁₃ and ∑PCAs-C₁₄–₁₇ in the coconut fat D sample, respectively, compared to the reference value. However, those results seem difficult to compare with ours due to the great variability of the values between the different laboratories and methods. Compared to our previous study (Amoura et al., 2024b), a significant improvement regarding the trueness with the LC coupling for both PCA subclasses was obtained using the ¹³C-labelled PCAs as internal standards instead of ¹³C-hexabromocyclododecane. It was also the case for the quantification of those PCA subclasses with the GC coupling, where a systematic overestimation was observed using ¹³C-hexachlorobenzene (Amoura et al., 2024a). The selected ¹³C-labelled PCAs are structurally the closest available. They are co-eluted with the native PCAs (Fig. S7) and better correct for matrix effects. For the CRM sample, a higher bias was globally observed using both couplings (except for ∑PCAs-C₁₀–₁₃ quantified by LC/HRMS). However, the reference values of the CRM are still questionable due to the low number of valid datasets that were taken into account (<7; Ricci et al., 2022).

Recommendations for improving the quantification of ∑PCAs-C₁₀–₁₃ and ∑PCAs-C₁₄–₁₇ in food samples could be proposed. As already stated in the literature, the sample preparation (Krätschmer & Schächtele, 2019) and the quantification method (McGrath et al., 2022) have limited influences on final results. Our results show that the choice of the ¹³C-labelled standards greatly improves the quantification of PCAs in food samples. Moreover, quantified homologue groups should be detected in the calibration standards in order to avoid any bias in the calculation of the apparent chlorination degree.

## 4.3. Quantitative results in foodstuffs

### 4.3.1. Detection frequencies

With both couplings, the number of quantified samples was similar, i. e. 15 vs. 14 samples and 39 vs. 40 samples, respectively (Fig. 1). In addition, more samples presented PCA levels below LOQs with LC- compared to GC/HRMS, i.e. 56 vs. 27 and 43 vs. 30 for ∑PCAs-C₁₀–₁₃ and ∑PCAs-C₁₄–₁₇, respectively. The remaining samples, which were more numerous with the GC compared to the LC coupling (51 % vs. 15 % and 17 % vs. 3 %, for ∑PCAs-C₁₀–₁₃ and ∑PCAs-C₁₄–₁₇, respectively), were considered outliers as they overpassed the chlorination ranges of the calibration curves. For PCAs-C₁₀–₁₃, 3 out of the 15 samples quantified by LC/HRMS exhibited an apparent chlorination degree higher than the upper limit of the GC range (Fig. S8). For PCAs-C₁₄–₁₇, among

the 39 samples quantified by LC/HRMS, 6 were not quantified by GC/HRMS (Fig. 1). For all these samples, the apparent chlorination degree obtained by GC/HRMS was higher than the upper limit of the apparent chlorination degree range (Fig. S9).

These results appear to be due to the narrower chlorination range when GC/HRMS is used, and to the higher sensitivity of this method towards higher chlorinated PCAs. In order to circumvent this problem, an extrapolation could be carried out with a risk of underestimation of the PCA levels. A better solution would be to expand the calibration range towards higher nominal chlorination degrees.

### 4.3.2. Comparison between LC and GC couplings

In general, the concentrations remained consistent between LC and GC/HRMS measurements in all food categories (Fig. 2). For quantified samples using both couplings, the calculated bias in ∑PCAs-C₁₀–₁₃ levels between the two methods ranged from 5 % to 57 %, with a median of 25 %. For ∑PCAs-C₁₄–₁₇, this bias ranged from 0.3 % to 92 %, with a median of 32 %. Regarding the literature, Bogdal et al. (2015) found differences up to factors 4.4 and 16 for the concentrations of ∑PCAs-C₁₀–₁₃ and ∑PCAs-C₁₄–₁₇, respectively, determined by APCI-TOF-HRMS and GC/ECN-HRMS. More recently, the interlaboratory study conducted by van Mourik et al. (2018) showed coefficients of variation for ∑PCAs-C₁₀–₁₃ of 23–137 % and 50–86 % for two cleaned and fractionated fish samples. Considering that the same extracts were analysed by both couplings in the present study, the discrepancies were ascribed to the choice of the coupling, which influenced the closeness of the homologue pattern with those of the calibration standards.

### 4.3.3. Comparison with the literature

#### 4.3.3.1. Vegetable oils and fats.
The results obtained for ∑PCAs-C₁₀–₁₃ in vegetable oil and fats are consistent with the results from the literature. Like for Shen et al. (2023) who analysed oils from The Netherlands, olive oil presented the highest detection frequency, and the mean concentrations were similar (12 vs. 14 ng/g w.w.). In addition, like for the studies of Shen et al. (2023) and Tomasko et al. (2021), PCAs-C₁₀–₁₃ were not present or at very low concentration in sunflower and rapeseed oils (<10 ng/g w.w.), whatever extra virgin or refined. Only one sample of sunflower oil raised questions with levels of ∑PCAs-C₁₀–₁₃ of 71–89 ng/g w.w. (Table S10). Concerning PCAs-C₁₄–₁₇, our results are in line with the fact that they are reported at a higher amounts and at a higher frequencies compared to PCAs-C₁₀–₁₃ in oils collected in Europe (Krätschmer et al., 2021b; McGrath, Limonier, et al., 2021; Shen et al., 2023). Concerning oil categories, Shen et al. (2023) and Krätschmer et al. (2021b) reported that ∑PCAs-C₁₄–₁₇ levels were higher in olive oils, while our results are less categorical (Fig. 2).

The geographical origin did not seem to be a decisive contamination factor. Only the high levels of ∑PCAs-C₁₀–₁₃ (and ∑PCAs-C₁₄–₁₇) observed in the crude palm oil, from Guinea and purchased in an exotic grocery store, raised questions. It may be due to a lack of regulations and control on the production and use of SCCPs in African countries (Nevondo & Okonkwo, 2021).

One discussed equivocal contamination factor is the oil refining process. According to Krätschmer et al. (2021b) and Shen et al. (2023), PCA levels appear to be lower in refined oils compared to extra-virgin oils (olive, palm, sunflower and rapeseed oils), suggesting a partial depletion during the refining process. Our results support this assumption for sunflower oil with average ∑PCAs-C₁₄–₁₇ values determined by LC/HRMS of 598 ± 274 (n = 2) vs. 114 ± 67 (n = 5) ng/g w.w. in extra-virgin and refined sunflower oils, respectively. On the opposite, this assumption does not seem applicable to olive and rapeseed oils with 37 ± 21 (n = 5) vs. 126 ± 114 (n = 2) ng/g w.w. and 6 ± 1 (n = 2) vs. 75 ± 40 (n = 5) ng/g w.w. (extra-virgin vs. refined oils, respectively). Thus, our results appear equivocal as regards the influence of the refining process, which calls for in-depth studies monitoring batches along the
unitary operations. For oil mixtures and cooking oils, these are mainly composed of likely refined rapeseed and sunflower oils (Table S2). Regarding those samples, PCAs-C₁₀–₁₃ were not detected or not quantified (Table S10), which is consistent with the data obtained for the ΣPCAs-C₁₀–₁₃ in refined sunflower and rapeseed oils. For PCAs-C₁₄–₁₇, the concentrations are of the same order of magnitude in refined sunflower and rapeseed oils. For the refined palm oils no. 26 and 28, PCAs-C₁₀–₁₃ were not quantified. Concerning sample no. 28, PCAs-C₁₄–₁₇ were detected at very low levels (<10 ng/g w.w.). Therefore, further studies are needed to determine whether the palm oil refining process lowers the level of PCAs.

Margarines are industrial products further processed from likely refined rapeseed, sunflower and palm oils (Table S2). Very low levels ΣPCAs-C₁₀–₁₃ were found in margarine samples, in line with what has been quantified refined sunflower, rapeseed and palm oil samples (<10 ng/g w.w.) (Fig. 2). PCAs-C₁₄–₁₇ were detected in all margarine, refined sunflower and rapeseed samples, but not in refined palm oil. Therefore, it was impractical to conclude on how the emulsification process would influence the occurrence of PCAs.

#### 4.3.3.2. Foods of animal origin.
Data reported in previous studies showed that among foods of animal origin, the categories with the highest levels of ΣPCAs-C₁₀–₁₃ and/or ΣPCAs-C₁₄–₁₇ are often fish, meat and fat (Iino et al., 2005; Lee et al., 2020).

For PCAs-C₁₀–₁₃, no clear distinction between the different food categories could be observed in our study. Except for a salmon sample that reached 6.1–14 ng/g w.w., the other samples showed very low concentrations (≤2 ng/g w.w.). Those results are in line with those of McGrath, Limonier, et al. (2021) who reported low levels of ΣPCAs-C₁₀–₁₃ in meat, fish and milk products from Belgian food, ranging from 2.3 to 5.1 ng/g w.w.. Also, the results appeared consistent with the market basket study carried out in Germany, where the average levels of PCAs-C₁₀–₁₃ found in milk and meat samples were either below the method detection limit (0.13 ng/µL) or showed low levels (up to 13 ng/g w.w.) (Krätschmer et al., 2021b). Regarding fish samples, the levels of ΣPCAs-C₁₀–₁₃ found in salmon and trout samples showed higher levels compared to our study (mean of 77 and 16 ng/g w.w. for salmon and trout, respectively). However, the number of samples per fish category in our study is insufficient to carry out an in-depth comparison.

Regarding PCAs-C₁₄–₁₇ in fish, most samples were < LOQ and the highest levels were found for two salmonid samples (trout, 18–33 ng/g w.w.; salmon, 19–31 ng/g w.w.). Similar levels were reported in fish purchased in Belgium (<LOQ–73 ng/g w.w.; McGrath, Limonier, et al., 2021), Germany (<LOQ–48 ng/g w.w.; Krätschmer et al., 2021a), and China (mean of 55 ng/g w.w.; Cui et al., 2020). Due to the limited number of samples for each fish species, it is difficult to explain why PCA concentrations are higher for salmonids. According to Krätschmer et al. (2019), the higher lipid content is one hypothesis but many other factors could play a role. They also observed a higher PCA accumulation in farmed fish compared to wild fish. In the present study, no conclusion could be drawn considering that among the four salmonid samples, only one was a wild catch and it belonged to the two contaminated samples. Further researches are therefore necessary to better understand the factors influencing the PCA contamination of fishes. Regarding meat and meat products, PCAs-C₁₄–₁₇ were quantified in three pork samples (liver, chop and bacon), one rabbit leg, one merguez and two poultry samples. For pork and merguez samples, the levels of ΣPCAs-C₁₄–₁₇ were similar to those observed in contaminated salmonids. In the market basket study conducted in southern Germany, levels in poultry samples were two times higher compared to the other meat samples (Krätschmer et al., 2021b). In our study, the two poultry samples seemed to be less contaminated compared to pork and merguez meats. The levels in meat samples (mean of 27 ± 14 w.w. by GC/HRMS) were higher than the ones reported in Germany (mean of 8.6 ng/g w.w.; Krätschmer et al., 2021b) and Belgium (median of 7.7 ng/g w.w.; McGrath, Limonier, et al., 2021),

but significantly lower compared to South Korea (median of 89.8 ng/g w.w.; Lee et al., 2020) and China (mean of 100 ng/g w.w.; Cui et al., 2020). In their study, Krätschmer et al. (2021b) showed a higher PCA contamination for processed meat and poultry, while no tendency was highlighted in the present study.

## 4.4. Dietary intake

Obtained EDIs appeared in line with the recent publications in Sweden and Germany (Krätschmer et al., 2021b; Shen et al., 2023; Sprengel et al., 2023; Swedish Food Agency, 2017).

The relative contribution of vegetable oils and fats accounted for more than 40 % of the EDI when considering it with foods of animal origin and reaching together 26 % the total French diet (Fig. 3). This result was consistent with the total diet, meal and market basket study realised in southern Germany where fats and oils accounted for almost 40 % of PCA exposure (Krätschmer et al., 2021b). The maximum EDIs were obtained for vegetable oils and fats with 3.3 and 39 ng per kg of body weight per day (ng/kg b.w./day) for ΣPCAs-C₁₀–₁₃ and ΣPCAs-C₁₄–₁₇, respectively (Table S13). Regarding PCAs-C₁₀–₁₃, the maximum EDI was significantly lower than the 95th percentile EDI from the consumption of fried oils in China (780–38,000 ng/kg b.w./day; Cao et al., 2015), but in line with the EDI value obtained via consumption of vegetable oils in Germany (1–37 ng/kg b.w./day; Shen et al., 2023). For PCAs-C₁₄–₁₇, the maximum EDI in our study was consistent with the EDI for adults in Germany (1–90 ng/kg b.w./day; Shen et al., 2023).

The EDIs of PCAs-C₁₀–₁₃ through the consumption of milk, meat and fish products were lower compared to those determined in China and Sweden. In China, the EDIs of PCAs-C₁₀–₁₃ determined for milk, meat and fish categories were 2.1–79, 560 and 872 ng/kg b.w./day, respectively, compared to 1.1–1.3, 1.5–1.6 and 0.7–1.3 ng/kg b.w./day in our study (Table S13). For PCAs-C₁₄–₁₇, the calculated EDI for cattle milk (6.4 ng/kg b.w./day; Table S15) was similar to the average PCA-C₁₄–₁₇ EDIs for adults as a result of cow milk consumption in different Chinese provinces (0.2–4.9 ng/kg b.w./day; Dong et al., 2020). The milk and dairy product category is an important one to monitor, particularly when a risk assessment is carried out on children and toddlers. For example, Lee et al. (2020) showed that the consumption of milk products was responsible for approximately 50 % of the intake of PCAs-C₁₀–₁₃ in children aged between 1 and 5 years. Krätschmer et al. (2021b) highlighted that infants (0–11 months) and toddlers (1–3 years) were the age groups which had the highest daily exposure to PCAs-C₁₀–₁₇, probably linked to an elevated consumption of dairy products compared to adults.

Finally, the egg category contributed the least to PCA-C₁₀–₁₃ daily intake in this study. This is in line with the European studies highlighting low contamination levels of PCAs in egg samples (Krätschmer et al., 2021b; McGrath, Limonier, et al., 2021; Swedish Food Agency, 2017).

## 4.5. Preliminary risk characterisation and conclusions

Regarding the risk assessment of CP mixtures, the CONTAM panel of EFSA resorted to the concept of margin of exposure (MOE) in order to highlight possible safety issues arising from the presence of PCAs in human food (EFSA, 2020). The MOE is defined as the ratio between a toxicological reference point and the EDI. Benchmark dose lower bound values of 10 % (BMDL10) of 2.3 mg/kg b.w./day for increased incidence of nephritis and 36 mg/kg b.w./day for increased relative kidney weights were selected as reference points for SCCPs and MCCPs, respectively (EFSA, 2020). Using these reference points and our EDIs, we obtained preliminary MOEs of 2.9 × 10⁵ and 3.6 × 10⁵ for PCAs-C₁₀–₁₃ and PCAs-C₁₄–₁₇, respectively (Table 1). These MOEs highlighted no health concern from the consumption of vegetable oils and fats and foods of animal origin products in France, providing a few limitations that must be kept in mind considering that this remains a preliminary
assessment on the dietary exposure of the French population to PCAs.

EDIs were calculated using protective hypotheses (upper bound approach, maximum concentration of PCAs for each food sample type), which likely overestimate the exposure.

On the opposite, several elements could be a source of underestimation. A total diet study should cover all food categories (i.e. including breads, fruits, beverages, etc.). In this study, the food types (vegetable oils and fats and foods of animal origin) were selected according to their presumed higher contribution to consumer exposure, although they accounted for only 26 % of the total French diet (Table S4). Other food categories may add-up to the dietary exposure to PCAs. Particular attention must be paid to other food categories such as grain based products, vegetables or sugar/sweets, which can constitute a significant intake of PCAs as already stated in the literature (Cui et al., 2020; Lee et al., 2020; Mézière et al., 2020; Swedish Food Agency, 2017; Wang et al., 2019). Also, the category coverage was not complete for the selected categories (Table S4). In addition, we used average consumption values while a protective risk assessment for the population should consider high consumers of the most contaminated food items. In that respect, probabilistic studies would better describe the exposure of the population. Importantly as well, many samples were not quantified due to particular homologue patterns falling outside the calibration range of chlorination degree. Further qualitative interpretation of our results will shed light on these patterns (Amoura et al., In Prep.). Future analytical developments are then necessary to encompass the maximum number of homologue groups in EDI calculations, including the ones that are currently not detected in calibration standards, PCAs-C<sub>6-9</sub> and PCAs-C<sub>≥18</sub>. Last, EFSA highlighted the need of chronic toxicity and developmental neurotoxicity studies, which could decrease reference points in the future.

### CRediT authorship contribution statement

**Cherine Amoura:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Emmanuelle Bichon:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Antonin Padioleau:** Writing – review & editing, Conceptualization. **Philippe Marchand:** Resources. **Bruno Le Bizec:** Resources, Project administration, Funding acquisition. **Gaud Dervilly:** Writing – review & editing, Resources, Project administration. **Ronan Cariou:** Writing – review & editing, Visualization, Supervision, Software, Methodology, Conceptualization.

### Funding

This work was supported by the French Ministry of Agriculture and Food, General Directorate for Food (DGAL).

### 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.

### Acknowledgement

The authors are grateful to K. Barreau, S. Durand and A. Grivaud for technical support.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodchem.2025.144746.

### Data availability

Data will be made available on request.

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