# Ex-ante assessment of sample pooling implementation on the dietary exposure to nDL-PCB in meat

Hassan Hachem, Romy Lynn Chaib, Erwan Engel, Isabelle Albert

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Hassan Hachem, Romy Lynn Chaib, Erwan Engel, Isabelle Albert. Ex-ante assessment of sample pooling implementation on the dietary exposure to nDL-PCB in meat. Food Research International, 2025, 214, pp.116599. 10.1016/j.foodres.2025.116599. hal-05070754

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Submitted on 16 May 2025

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# Ex-ante assessment of sample pooling implementation on the dietary exposure to nDL-PCB in meat☆

Hassan Hachem<sup>a,b,c</sup>, Romy Lynn Chaib<sup>d</sup>, Erwan Engel<sup>e</sup>, Isabelle Albert<sup>a,*</sup>

## ARTICLE INFO

Keywords:

Probabilistic approach

Food safety surveillance

Prospective development of pork industry

Contaminant monitoring system

Human health

PCB (polychlorinated biphenyl)

## ABSTRACT

The implementation of sample pooling - a group testing strategy where multiple samples are combined into a pool and tested with a single analysis - was shown to be an effective strategy for both food companies and food safety authorities to strengthen the surveillance of food chemical safety. This paper aims to assess the benefit that would have a surveillance based on sample pooling for human health, using the exposure to meat Polychlorinated biphenyls (PCB) as a case study. It evaluates the exposure to non-dioxin-like (nDL-) PCB in pork meat in France by comparing sample pooling (18,275 tested carcasses) with current (731 tested carcasses) and systematic testing (23 million tested carcasses), along with two scenarios reflecting most plausible developments in the pork industry: Two-faced Sector scenario and Regional Magnet. For this purpose, a probabilistic approach using simulations driven by survey data and scenarios assesses the adults' exposure. The results show that sample pooling is an effective monitoring method for reducing exposure, with one scenario reducing the number of French adults exceeding the Tolerable Weekly Intake (TWI) for dioxins and dioxin-like PCBs by 10,000, and the other scenario by 20,000. In both prospective scenarios, sample pooling lies between current and systematic testing, not far from systematic testing.

## 1. Introduction

In response to a growing societal demand, particularly in Europe and China, to improve food chemical safety, stakeholders, whether health authorities or food industries, must implement cost-effective solutions to intensify the surveillance of priority contaminants. The issue is particularly critical for sensitive populations, such as infants, whose dietary exposure to chemicals during the first 1000 days of life significantly impacts long-term health (ANSES, 2006). Current food chemical safety surveillance generally relies on highly sensitive but expensive analytical methods with low throughput. This restricts the deployment of food safety authorities' surveillance and virtually prevents industrial self-monitoring (Engel et al., 2022). To overcome this difficulty, sample pooling, introduced by Dorfman (Dorfman, 1943), is a group testing strategy where multiple samples are combined into one pool tested with a single analysis (Bilder, 2019). This approach reduces costs by

excluding many negative samples while detecting a few positive ones (Chong et al., 2020). Re-popularized by the COVID-19 crisis (Chong et al., 2020; Mallapaty, 2020), recent work carried out within the framework of French and Sino-European projects shows that this approach can be extremely effective in strengthening both the surveillance of chemical safety by food safety authorities (Ratel et al., 2024) and self-monitoring by food companies (Galharret et al., 2025). However, even though the application of sample pooling could reduce the costs of analyzing non-Dioxin-Like polychlorinated biphenyls (nDL-PCB) in meat by almost 19-fold (Ratel et al., 2024), it is legitimate to question the impact of increasing the frequency of surveillance of this contaminant/food pair by food safety authorities on the medium-term exposure level of consumers. nDL-PCBs are persistent organic pollutants that accumulate in fat tissue and have been linked to several adverse health effects, including endocrine disruption, immune system impairment, and possible carcinogenicity. Specifically, in vivo and in

<footnote><sup>a</sup> Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, F-91120 Palaiseau, France</footnote>

<footnote><sup>b</sup> Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Qatar Foundation - Education City, Doha, Qatar</footnote>

<footnote><sup>c</sup> Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York City, New York, USA</footnote>

<footnote><sup>d</sup> Observatoire des marchés du CIRAD, UMR GECO, F-34000, Montpellier, France</footnote>

<footnote><sup>e</sup> INRAE, UR QuaPA, F-63122, Saint-Genès-Champanelle, France</footnote>

<footnote>* This article is part of a Special issue entitled: 'Infant Food Risk Control' published in Food Research International.</footnote>

<footnote>* Corresponding author.
E-mail address: isabelle.albert@inrae.fr (I. Albert).</footnote>
vitro investigations have demonstrated that PCBs primarily disrupt endocrine functions related to the thyroid and reproduction (Djordjevic et al., 2020).

To evaluate such impacts, an ex-ante evaluation approach is adopted (Macombe et al., 2013), providing valuable tools to explore the potential effects of increased surveillance (Chaib, 2023). The aim is in fact to compare possible future scenarios including higher surveillance frequencies with reference scenarios under an unchanged monitoring system (Chaib, 2023; Macombe et al., 2018). By accounting for the complex dynamics of the value-chain and integrating various assumptions and uncertainties about future developments (Godet, 2007; Godet et al., 2008), the goal is to identify unexpected risks or benefits -both quantified- associated with more frequent surveillance (Chaib, 2023). This helps in pinpointing the most impactful control strategies to reduce consumer exposure to contaminants. The scenarios are created using the French prospective participatory Godet method, a framework that facilitates collaborative exploration of future possibilities (Godet, 2007; Godet et al., 2008).

In realistic medium-term scenarios (approximately 5 years from now), we focus on PCBs because they are priority contaminants in the current European monitoring system due to their safety risks, particularly in animal-derived food. While the 12 Dioxin-Like PCB congeners have been associated to significant adverse health impacts, such as endocrine system disruption and carcinogenicity (Bhavsar et al., 2008), the toxicity of nDL-PCB (non-Dioxin-Like-PCB), associated with adverse neurodevelopmental effects (Yang et al., 2009), should not be overlooked. Animal-derived foods are the main source of human exposure to these harmful pollutants, with meat being one significant contributor (Engel et al., 2015). This article examines the contribution of pork products to nDL-PCBs exposure in the French population because, for thousands of years, pork has been a staple of human diets and currently represents one of the world's largest livestock industries (Szúcs & Vida, 2017). France is an interesting case study for its high pork consumption, its geographical diversity in terms of pork production and the availability of consumption and contamination data. Pork products (e.g., sausages, ham, and "charcuterie") are in fact staples: according to KANTAR (Worldpanel, 2019), a panel consumption database that records food purchases, and INCA2 (ANSES, 2006), a French national individual survey of dietary intake conducted in 2006-2007, 95 % of respondents stated eating pork meat and/or pork products at least once a week.

The primary focus of this paper is to examine sample pooling's influence on human health by assessing the exposure of the French adult population to nDL-PCB from pork meat consumption. To do so, two other testing techniques are compared with sample pooling by evaluating their influence on human health: the current testing method (testing only a few carcasses) and a hypothetical systematic testing method (ensuring no carcass exceeds the Maximum Limit (ML)). This analysis is carried out under two prospective scenarios, reflecting potential developments in the pork industry and reshaping the French pork production map. A probabilistic approach is used to quantify population exposure to nDL-PCBs, based on survey data on meat consumption and PCB concentrations in pork, in order to assess the high exposures that may occur due to variability in consumption and contamination across the territory (Cullen & Frey, 1999). In a novel way, it also incorporates simulated data due to the prospective nature of the study. Firstly, the envisioned industry scenarios are outlined and an overview of the testing tools that could be implemented in these scenarios is provided. Secondly, the exposure assessment methodology used in this paper is introduced using a probabilistic approach from available data (consumption and PCB concentration levels), prospective contamination prevalence rate by scenario (proportion of carcasses exceeding the ML), and simulated data representing the French herd under future scenarios. Finally, the exposure of French adults to nDL-PCBs is then assessed based on survey data and simulated data when sample pooling, systematic testing or current testing are carried out for comparative purposes.

# 2. Materials and methods

## 2.1. Two prospective scenarios

Doing an ex-ante evaluation of the surveillance tools presented in this paper requires an anticipation phase, not to predict the future but rather to mobilize collective intelligence (Barré, 2000; De Jouvenel, 1964) to identify key factors that impact the future of the value-chain and those that depend on it (Godet, 2007). This implies developing different paths of evolution of the targeted value-chain, here the French pork value-chain. In this way, the goal is to create so-called reference or framework scenarios (Godet, 2007), in which PCB detection tools would be introduced to examine their potential effects. Those frameworks are then presented to a panel of experts, composed of specialists from various fields of pork industry, to choose two contrasted ones, selected from six scenarios. This strategic prospective approach is inspired by the Godet method (Godet, 2007; Godet et al., 2008; Godet & Durance, 2011) and also builds upon the thesis work of (Chaib, 2023).

Consensus favored the two contrasting framework scenarios named "A Two-faced Sector/Industry" and "Regional Magnet/Compromise".

### 2.1.1. A two-faced sector/industry

In this framework scenario, society's high expectations of the pork industry (organic farming, animal welfare, less pollution, etc.) are leading to a new distribution pattern across the country: the large-scale operations in the west of the country are undergoing little development, while small to medium-sized farms are spreading throughout the country, using multi-species slaughterhouses and local processing plants. Overall, production quantities are stable. On average, consumers are consuming as much as today and paying more for it.

The originality of the Two-faced Sector framework scenario is that it considers a relative stagnation across the country. France continues to produce approximately 23 million pork carcasses annually (Rieu et al., 2014), however, production is distributed unevenly across the country. 9 million carcasses are produced in the "Diagonale du vide" ("Empty Diagonal") (see Fig. 1). Table 1 gives the main production characteristics and distribution for this scenario.

### 2.1.2. Regional magnet/compromise

In the Regional Magnet/Compromise framework, the industry remains concentrated in today's major pork production zones, with production quantities staying stable and no significant technological progress: France still produces 23 million pork carcasses per year. The Brittany region still produces 13 million carcasses, with 2 million of these focused on animal welfare. In this scenario the farm's size is fixed to the current mean over the French territory, that is 4700 pigs by farm per year, because it does not change significantly over the country in this scenario. The consumption of French adults also remains the same in this scenario. Table 1 also gives the main production characteristics and distribution for this scenario.

## 2.2. Surveillance Tool

### 2.2.1. Current testing

Nowadays, the annual surveillance and control plans for food contamination in France involve testing only a few specific food samples to assess and manage potential risks. These plans target representative populations for surveillance and focus on higher-risk sub-populations for control (DGAL, 2023). In 2023, only 731 of these carcasses were tested by French national reference laboratories with reference GC-HRMS reference method (Dervilly-Pinel et al., 2017), for surveillance and possible exceedance of their nDL-PCB content compared to the ML. The allocation of carcass testing per region is proportionate to the region's production and is distributed based on the size of farms. For instance, if a region comprises 50 % of medium-sized farms and 50 % small farms, then 50 % of the tests will be conducted on medium-sized
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Maps of France. On the left: Map of France highlighting the departments of the "Diagonale du vide" in white along a northeast/southwest line, where population densities are relatively low compared to the rest of France. And on the right: PCB emissions into the atmosphere in France in 1990, as reported by the European Environment Agency (EEA, 1990). The red square figures the Chartres area. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

**Table 1**

Distribution of the produced carcasses across the regions of France, considering the Two-faced Sector scenario and Regional Magnet scenario. *: Centre-Val de Loire (225,000 carcasses), Grand-Est (300,000 carcasses), Bourgogne-Franche Comté (424,000 carcasses), Nouvelle Aquitaine (446,000 carcasses), Auvergne-Rhône Alpes (339,000 carcasses) and Occitanie (382,000 carcasses), **: Centre-Val de Loire (2,338,624 carcasses), Nouvelle Aquitaine (2,269,840 carcasses), Auvergne-Rhône Alpes (825,396 carcasses) and Occitanie (1,444,446 carcasses).

<table><thead><tr><th>Nb. of pigs/year</th><th>Region</th><th>Farm Type</th><th>Nb. of pigs/year/farm</th></tr></thead><tbody><tr><td colspan="4" style="text-align: center; font-weight: bold;">Two-faced Sector scenario</td></tr><tr><td>4,000,000 (17.40 %)</td><td>Brittany</td><td>Very big farm</td><td>5000</td></tr><tr><td>10,000 000 (43.49 %)</td><td>Rest of France</td><td>Big farm</td><td>4700</td></tr><tr><td>2,116,000 (9.20 %)*</td><td>Empty Diagonal (ED)</td><td>Small farm</td><td>1008</td></tr><tr><td>6,878,306 (29.91 %) **</td><td>4 Regions in ED**</td><td>Very small farm</td><td>850</td></tr><tr><td colspan="4" style="text-align: center; font-weight: bold;">Regional Magnet scenario</td></tr><tr><td>13,000,000 (56.52 %)</td><td>Brittany</td><td>Big farm</td><td>4700</td></tr><tr><td>10,000 000 (43.48 %)</td><td>Rest of France</td><td>Big farm</td><td>4700</td></tr></tbody></table>

farms, and the remaining 50 % on small-sized farms. All carcasses contain PCB, but the concentration levels are relatively low, typically lower than the ML set by the authorities for nDL-PCBs content in pork meat (ML = 40 pg/g in France). There is a slight possibility that the carcasses may be heavily contaminated (exceeding the ML). At present, the prevalence rate of carcass contamination (exceeding the ML) in France is approximately 0.1 % according to data from the DGAL, 2020 report (DGAL, 2020). In the following, we referred to "current testing" when only 731 individual tests per year were realized in the country to compare sampling pooling with current monitoring. As at present, with the "current testing" tool, the distribution of carcass tests by region will be proportional to regional production and is distributed according to

farm size, both quantities being different in each prospective scenario, like also the prevalence rate of carcass contamination in the country.

### 2.2.2. Sample pooling

The surveillance strategy, known as "sample pooling", involves analyzing a composite of samples (with 25 samples deemed a suitable compromise in the context of nDL-PCB in meat (Ratel et al., 2024)) instead of individual samples (Dorfman, 1943). This approach allows testing 25 times more carcasses with the same first number of tests. Consequently, it is assumed that, through this method, a total of 18,275 carcasses will be tested in the future (calculated as 25 × 731, 731 tests are done today, for a first constant analyzing cost). In line with the current situation, it is assumed that the number of carcasses to be tested is proportional to the region's production. If the region includes various types and sizes of farms, testing is distributed based on the percentage represented by each type and size. The number of tests done per region will be deduced by rounding the quotient obtained by dividing the number of carcasses to be tested by 25. After doing these first 731 tests, if tests are positive, 25 other ones are done per positive group test from the carcasses used to do the first test to find individually which ones are positive.

Therefore, to calculate the number of carcasses to be tested in the Two-faced Sector scenario, it is assumed that the distribution of the number of carcasses to be tested is proportional to the production in each region under the scenario, meaning a decrease of production in Brittany, an increase of production in regions of the Empty Diagonal, and an unchanged production in other regions (see the 2.1.1 for the scenario's description and Table 1). In (3trois3, 2023), the actual production per region is given based on an old regional cutout (on a map of France from (3trois3, 2023) in Supplementary Materials 1). Today's production is recalculated in the new cutout regions (which are gatherings of old regions). From this information and considering the production movements in the Two-faced Sector scenario, the production per region under this framework is calculated (see Supplementary Material 2: Table S2). Then the number of carcasses to be tested per region is
calculated and distributed according to farm types as discussed previously (see Supplementary Material 2: Table S2).

To calculate the number of tests in the Regional Magnet scenario with the sample pooling strategy, it is assumed that the distribution of carcasses to be tested is proportional to today's production in each region (calculated just before) as pork productions' distribution does not vary compared to now (more details in 2.1.2.) In this scenario, a single farm size is considered (4700 pigs per year and per farm), so tests are distributed uniformly across farms in each region. In supplementary Material 2, Table S2 gives the number of tests to be done in each French region under this scenario with the sample pooling tool.

### 2.2.3. Systematic testing

Implementation of this tool is still hypothetical and implies that all carcasses would be subjected to testing. This “ideal” tool, which does not currently exist, would allow for systematic monitoring of all products on the market and would lead to the assumption that no product placed on the market could expose the consumer to anything beyond the ML of nDL-PCB content.

## 2.3. Exposure assessment

Exposure of an individual i, $E_i$, in pg/week/kg of body weight, drawn at random from the French adult population, is calculated as follows (EFSA, 2010):

$$E_i = P_i \times \left( \frac{Q_i \times m_i}{w_i} \right), \qquad (1)$$

where $P_i$ is the probability that individual i consumes pork, $Q_i$ is the pork consumption of this individual (g/week), $m_i$ is the nDL-PCB concentration (pg/g) in the products consumed by the individual, and $w_i$ is the body weight of the individual, set at 70 kg. To carry out this exposure calculation under the scenarios considered, the quantities consumed must be established, and so do the nDL-PCB concentrations in the pork consumed, and the probability of consuming pork for a French adult taken at random from the general population.

### 2.3.1. Pork consumption and nDL-PCB data

The weekly pork consumption among the French population is determined through data from INCA2 (ANSES, 2006). Within this database, a total of 2457 adult individuals, among whom 2264 consume pork are identified (considering all pork-containing foods such as pork itself, cold meats, sandwiches/pizzas/quiches containing pork, etc.). To have a probabilistic approach, and to take into account the variability in a population's consumption, a log-normal distribution is fitted to this pork consumption data. It will be used to sample the $Q_i$ values for the population exposure assessment.

To determine the nDL-PCB concentrations, data comes from a previous nationally funded project (SoMeat, http://www.someat.fr/) in which a study is conducted to determine nDL-PCB concentrations in meat products. The representativeness of the sample concerning the national production level sampling procedure was ensured (see (Tressou et al., 2017) for more details). All samples ($n = 96$) were analyzed for nDL-PCB concentrations at the same French accredited national reference laboratory (Dervilly-Pinel et al., 2017). To have a probabilistic approach, and to take into account the concentration variability, a log-normal distribution is fitted to the nDL-PCB concentration found in these sample for the concentration values from uncontaminated carcasses (i. e., whose nDL-PCB concentration is below the ML of 40 pg/g actually fixed by the sanitary authorities in France). Indeed, a conditional approach is necessary to determine the $m_i$ as the small size of the SoMeat dataset, no meat sample was found above the ML of 40 pg/g.

Then, another distribution is needed for concentration values from the contaminated carcasses (exceeding the ML). If the meat is contaminated, its nDL-PCB concentration distribution is established based on

previous work (Ratel et al., 2024), considering concentrations higher than the ML dose set at 40 pg/g. A log-normal distribution with a mean of 0.3 (in logarithmic units, equivalent to a mean<fnref n="1" /> of 1.94 pg/g and a standard deviation of 0.85 (in logarithmic units, equivalent to a standard deviation<fnref n="2" /> of 1.99 pg/g) is used. This distribution is truncated on the left at 40 pg/g, as per the literature (Ratel et al., 2024). This distribution is given in Supplementary Materials 3.

The contamination prevalence in the country is a key factor when determining the nDL-PCB levels in French produced carcasses because nDL-PCB concentrations are conditional to the meat's contamination status (below or above the ML dose). This prevalence has to be calculated in both new scenarios.

### 2.3.2. Contamination prevalence rate by scenario

As established in numerous studies (Covaci et al., 2002; Drotman et al., 1983; Gilbertson, 1991; Tlustos, 2009), it is hypothesized that PCB contamination mainly originates from animal feed and varies across the French territory depending on feed sources. To obtain the new prevalence rate in France under the Two-faced Sector scenario, the current PCB regional contamination rates must be first obtained to then deduce the new prevalence rate in France. In each French region, two-thirds of pigs are fed with grain from the Chartres area, an intensive grain production zone (INTERBEV, 2023) (red square on Fig. 1). Pigs produced in Brittany, however, are exclusively fed by feed from the Chartres region. Due to this dependence, currently, 13,000,000 carcasses produced in Brittany, plus two-thirds of the 10,000,000 carcasses produced outside Brittany (Rieu et al., 2014), are contaminated. For the rest of the animals, in each French region, the number of animals depending on local feed varies according to the current regional pig production (see Supplementary Material 2: Table S2). Sadly, the PCB contamination rate in each French region is unknown. DGAL, 2020 (DGAL, 2020) gives, however, an estimate of 0.1 % for the whole French territory. To assess the contamination rates of French regions, the global DGAL estimation, regional pig production, and the map of PCB emissions in the atmosphere in France in 1990, provided by the European Environment Agency (EEA, 1990), are used (see Fig. 1). To do this, a weighted calculation is performed, assuming that the prevalence of each region is proportional to the ratio of the number of average dark and average light tiles observed in the area in the map of the PCB emissions. Taking the Chartres area as an example, for this region, an average dark/average light ratio of 9/2 is given (assuming that a very dark tile is equivalent to two medium dark squares. Likewise, subsequently in regions where there are light tiles, one light tile is equivalent to half as much as an average light tile). This is a rather large approximation, but it is of the order of the accuracy of the PCB emission map. Therefore, the following formula is obtained to determine regional prevalences:

$$Prod_{\text{Chartres}} \times \frac{9}{2} \times k + \frac{1}{3} \times \sum_{i=1}^{N} \left( Prod_{i} \times \frac{D_{i}}{L_{i}} \right) \times k = 0.1\%, \qquad (2)$$

where k is the proportionality coefficient of the regional prevalence to the dark/light ratio, N is the number of regions, except Brittany and Île-de-France that do not have pork production, $Prod_i$ is the production percentage of region i, $D_i$ is the equivalent number of average dark tiles in region i, $L_i$ is the equivalent number of average light tiles in region i, $Prod_{\text{Chartres}}$ is the percentage of production that depends on the Chartres region, 85.5 % calculated previously. From this formula (2), k is deduced, then each regional contamination rate by $\frac{D_i}{L_i} \times k$. From these

<footnote n="1">¹ Mean of a log-normal distribution = $\exp(\mu + \frac{\sigma^2}{2})$, where $\mu$ is the logarithmic mean and $\sigma^2$ is the logarithmic variance.</footnote>

<footnote n="2">² Standard deviation of a log-normal distribution = $\sqrt{(\exp(\sigma^2) - 1) \times \exp(2 \times \mu + \sigma^2)}$, where $\mu$ is the logarithmic mean and $\sigma^2$ is the logarithmic variance.</footnote>
current regional contamination rates and the new percentages of production by region in the Two-faced sector scenario (see Supplementary Material 2: Table S2), a new global contamination rate over the French territory will be deduced for this scenario.

In the Regional Magnet scenario, as mentioned previously (see 2.1.2), the production does not move on the French territory and considering that meat contamination comes from the animal's diet, this does not alter the contamination of the consumed meat compared to the current situation. The overall contamination prevalence in the country is then as the current one: 1/1000.

### 2.3.3. Simulated data

As the context is prospective, the French herd needs to be replicated in both scenarios. To do so, the French herd comprising 23,000,000 carcasses is simulated in both scenarios (see 2.1.1 and 2.1.2), each with their assigned nDL-PCB levels based on the calculated regional feed contamination rates. The simulation accounts for farm type distributions per scenario ("Very big farms", "Small farms" and 2/3 of "Big farms" use feed from Chartres' region, while "Very small farms" and 1/3 of "Big farms" use locally sourced feed), and the log-normal distribution of nDL-PCB concentration in meat assumed in the case of contaminated carcasses or not. Lots of carcasses are then drawn by region and farm size, following the number of tests calculated previously (see 2.2). If a test is positive (i.e., containing at least one positive carcass) then it is assumed that the number of tests will be increased by 25 in the Two-faced Sector scenario because then all carcasses in the pooling sample will be tested. In both scenarios, when a drawn carcass is found to be contaminated (nDL-PCB level above the ML), the equivalent of the farm's total production from which de positive carcass originated is removed from the French consumable herd. To account for contamination aggregation on a farm due to the environmental context, 1/10 of this production is assumed to be contaminated. Then, all the drawn carcasses (contaminated or not) will be replaced by healthy uncontaminated carcasses, reflecting the benefit of sanitary measures introduced after a positive test detection. Once all replacements are done, the new distributions of the nDL-PCB levels in the French herd are obtained according to the scenarios and implemented tools.

Finally, for each scenario with an implemented tool, the exposure of 50 million French adults, which is approximately the current estimated number of adults in France, is calculated from Eq. (1) with 50 million samplings in their Q's distribution and m's distribution.

## 3. Results

### 3.1. Pork consumption

In order to assess the exposure to nDL-PCBs for French individuals, it is necessary to know their consumption patterns. In light of this, and based on the INCA2 dataset, the pork meat consumption probability, $P_i$ (see Eq. (1)), is estimated to be 0.94 (2264/2457 i.e., 2264 individuals consumed pork out of 2457) (see 2.3.1) which is consistent with the penetration rate reported for pork in (Volatier, 2000).

The $Q_i$'s values from Eq. (1) which measures the individual pork consumption is obtained from the fitted log-normal distribution to the pork consumption data from our extraction of the INCA2 database. Fig. 2 (b) represents this adjustment to the data in blue. The fitted distribution has a mean of 5.72 (in logarithmic units), indicating that, on average, a French adult consumes 423 g/week,<fnref n="3" /> with a standard deviation of 0.81

(in logarithmic units), equivalent to 407.6 g/week.<fnref n="4" /> These results align with those from the INCA2 study (Volatier, 2000). Regarding consumption quantity, the data relied on numerous observations, but it's worth noting that INCA2 data was collected in 2006–2007, and messages advocating for a reduction in meat consumption have become more prevalent since then.

This consumption distribution (Q's distribution) will be the same for all six four scenario-tool pairs (2 prospective scenarios × 3 surveillance tools) because of unchanged assumed consumption in both prospective scenarios as mentioned in 2.1.1. and 2.1.2.

### 3.2. nDL-PCB intakes by scenario and monitoring tool

First, the aim is to estimate the nDL-PCB levels in the carcasses consumed by a French individual in order to assess its intake ($m_i$ in Eq. (1)). The nDL-PCB concentration's distribution depends on the nDL-PCB concentration of the carcasses (contaminated or not), on the ML exceedance prevalence in France under the considered prospective scenario and on the average number of positive tests found according to the tool used (as contaminated carcasses are replaced by non-contaminated ones). As mentioned earlier, if the meat tests positive (exceeding the ML), its nDL-PCB concentration distribution is established as described in (Ratel et al., 2024) (see 2.3.1). Otherwise, if the meat tests negative, nDL-PCB concentration distribution is established from the SoMeat's samples. Fig. 2 (a) represents this adjustment to the data. From the nDL-PCB concentration data, a log-normal distribution with a mean of -0.38 (in logarithmic units) is fitted, corresponding to an average of 0.98 pg/g on the nDL-PCB concentrations. The standard deviation is 0.85 (in logarithmic units), equivalent to 1.01 pg/g. Then, to assess the exposure of French adults in the prospective scenarios which differs from current conditions, it is essential to estimate the overall contamination prevalence in France under both scenarios to know in which distribution to draw depending on its contaminated status as explained in 2.3.1.:

- in the Two-faced Sector scenario, the new contamination prevalence rate in France depends on the current regional contamination rates and the new percentages of production by region as explained in 2.3.2. Then, using Eq. (2), Table 3, and Fig. 1, the current PCB regional contamination rates in pig feed produced on farms are obtained. Supplementary Material 4 (Table S4) provides the current PCB regional contamination rates for pig feed produced on farms. For more details about the calculations, please refer to the Supplementary Material 4. Based on these calculations and the new regional productions under the Two-faced Sector scenario (see Supplementary Material 2: Table S2), a prevalence over the country of 0.71/1000 is obtained for this scenario;

- in the Regional Magnet scenario, contamination of meat consumed is unchanged from the current situation as mentioned 2.3.2. Then it is still 0.1 %.

To see the intake differences between scenarios, Table 2 gives the mean, minimum, maximum and selected quantiles—namely the first quartile (Q1, the 25th percentile, meaning that 25 % of the data falls below the first quartile), the median (Q2, the 50th percentile, meaning that 50 % of the data falls below the second quartile), and the third quartile (Q3, 75th percentile, meaning that 75 % of the data falls below the third quartile)—of the intake distributions under each scenario once replacements have been made if positive tests are found, as explained in section 2.3.3, in accordance with the tool. In both scenarios, sample pooling results in nDL-PCB levels exceeding the ML (40 pg/g), while systematic testing does not exceed the ML. With sample pooling, 16 initial positive tests led to 16 × 25 supplementary tests in some regions and farm types for the Two-faced Sector scenario, and 20 initial positive tests led to 20 × 25 supplementary tests for the Regional Magnet

<footnote n="3">³ Mean of a log-normal distribution = $\exp(\mu + \frac{\sigma^2}{2})$, where $\mu$ is the logarithmic mean and $\sigma^2$ is the logarithmic variance.</footnote>

<footnote n="4">⁴ Standard deviation of a log-normal distribution = $\sqrt{(\exp(\sigma^2) - 1) \times \exp(2 \times \mu + \sigma^2)}$, where $\mu$ is the logarithmic mean and $\sigma^2$ is the logarithmic variance.</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Adjusted distributions from survey data. On the right: Consumption in g/week of pork meat from INCA2 data, with its adjusted distribution. And on the left: nDL-PCB concentrations in pg/g based on SoMeat data and the fitted distribution.</figcaption></figure>

**Table 2**

Summary statistics for the nDL-PCB intakes (pg/g) after the implementation of sample pooling and systematic testing under the Two-Faced Sector and Regional Magnet scenarios.

<table><thead><tr><th>Summary Statistics</th><th>Sample pooling</th><th>Systematic testing</th></tr></thead><tbody><tr><td colspan="3"><strong>Two-faced Sector</strong></td></tr><tr><td>Minimum</td><td>0.006</td><td>0.006</td></tr><tr><td>Q1 (25th percentile)</td><td>0.386</td><td>0.386</td></tr><tr><td>Median (50th percentile)</td><td>0.684</td><td>0.683</td></tr><tr><td>Mean</td><td>1.005</td><td>0.981</td></tr><tr><td>Q3 (75th percentile)</td><td>1.214</td><td>1.212</td></tr><tr><td>Maximum</td><td>225.871</td><td>39.906</td></tr><tr><td colspan="3"><strong>Regional Magnet</strong></td></tr><tr><td>Minimum</td><td>0.007</td><td>0.007</td></tr><tr><td>Q1 (25th percentile)</td><td>0.386</td><td>0.385</td></tr><tr><td>Median (50th percentile)</td><td>0.684</td><td>0.683</td></tr><tr><td>Mean</td><td>1.006</td><td>0.982</td></tr><tr><td>Q3 (75th percentile)</td><td>1.214</td><td>1.213</td></tr><tr><td>Maximum</td><td>181.962</td><td>39.993</td></tr></tbody></table>

scenario (see Table 3). These secondary tests detected a total of 16 positive carcasses for the Two-faced Sector scenario and 20 positive carcasses for the Regional Magnet scenario. The production of concerned farms was replaced by healthy carcasses in the “consumable herd”. Here, we consider that 1/10 of the production from these farms was also contaminated due to intra-farm contamination of the meat from the same animal feed (the ratio between intra- and inter-farm contamination is then between 71 and 100, depending on the scenario; a ratio that seems realistic to experts in the sector, but that would need to be confirmed by intra-farm evaluations of an entire herd). For systematic testing, if implemented, all carcasses would be tested, and all contaminated carcasses (16,247 for the Two-faced Sector scenario and 23,000 for the Regional Magnet scenario in the presented simulation) would be replaced with uncontaminated ones. Consequently, the French population would only consume carcasses declared below the ML (40 pg/g).

### 3.3. Exposure assessment by scenario and monitoring tool

From the product of the $Q_i$ and $m_i$ values (see Eq. (1)), Fig. 3 gives the

**Table 3**

Statistical summaries of French adult exposure to nDL-PCB from pork consumption in the Two-faced Sector (top) and Regional Magnet (bottom) scenarios with various monitoring tools. *: the positive first tests were developed in 25 times more secondary tests on carcasses of the first pooled sample.

<table><thead><tr><th>Monitoring tool</th><th>Mean exposure (pg/week/kg bw)</th><th>% of individuals with Exposure>TWI</th><th>Nb. of positive tests</th><th>Total Nb. of tests</th></tr></thead><tbody><tr><td colspan="5"><strong>Two-faced Sector</strong></td></tr><tr><td>current testing</td><td>5.49</td><td>8.02</td><td>0</td><td>731</td></tr><tr><td>Sample pooling</td><td>5.41</td><td>8.00</td><td>16 (first) and 16 (secondary)*</td><td>1131</td></tr><tr><td>Systematic testing</td><td>5.30</td><td>7.96</td><td>16,247</td><td>23,000,000</td></tr><tr><td colspan="5"><strong>Regional Magnet</strong></td></tr><tr><td>current testing</td><td>5.54</td><td>8.04</td><td>0</td><td>731</td></tr><tr><td>Sample pooling</td><td>5.44</td><td>8.00</td><td>20 (first) and 20 (secondary)*</td><td>1231</td></tr><tr><td>Systematic testing</td><td>5.30</td><td>7.95</td><td>23,000</td><td>23,000,000</td></tr></tbody></table>

exposure log-densities non-zoomed and zoomed on the right tail under both scenarios for the different tools. The focus is on scrutinizing the right tails of the exposure distributions, where the differences are expected to appear. The Tolerable Weekly Intake (TWI) of 14 pg WHO TEQ/kg bw, established by (SCF, 2001) for dioxins and dioxin-like PCBs, is represented as it is the only health threshold indicator in the context. Fig. 3 shows that above the 0.99 quantile, the exposure density, with the use of sample pooling and systematic testing, is lower than with current testing (731 carcasses tested). These results show that by increasing the number of pork meat samples by a factor of 25 ((Ratel et al., 2024) for the nDL-PCB concentration), sample pooling contributes to reduce the exposure of the French adult population to this persistent organic pollutant in the two prospective scenarios, particularly for the 0.01 %
Exposure log-densities

Quantiles: [0.99–1]

Quantiles: [0.999–1]

## Two-faced Sector

<figure><img src="image_7.png" /></figure>

## Regional Magnet

<figure><img src="image_8.png" /></figure>

**Fig. 3.** Under the Two-faced Sector (top) or Regional Magnet (bottom) scenario: exposure densities, exposure densities zoomed beyond their 0.99 quantile, and exposure densities zoomed beyond their 0.999 quantile, of the French adult population to PCB in log(pg/week/kg bw) via consumption of pork for different surveillance tools (in black, current testing; in red, sample pooling; and in blue, systematic testing). The green dotted vertical line at log(14) corresponds to log(TWI). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

most exposed individuals. Of course, sample pooling does not limit exposure as much as an ideal testing capable of controlling the entire production would, but it constitutes, given current knowledge and with a constant budget devoted to monitoring, a good existing solution in this context of low contamination prevalence (0.1 % for the upper one).

Table 3 resumes these findings in a few statistics to better highlight the real influence of the different testing methods on French adults' exposure. A decrease is observed in the French population's average exposure to nDL-PCB with sample pooling implementation, along with a reduction in the percentage of French adult exposure exceeding the TWI under both scenarios. Specifically, in the Two-faced Sector scenario, the percentage exceeding the TWI decreases by 0.02 % with the use of sample pooling (equivalent to 10,000 fewer French adults exceeding the TWI), and by 0.06 % with systematic testing (which would correspond to 30,000 fewer French adults exceeding the TWI). In the Regional Magnet scenario, the percentage exceeding the TWI decreases by 0.04 % with sample pooling (equivalent to 20,000 fewer French adults exceeding the TWI), and by 0.09 % with systematic testing (which would correspond to 45,000 fewer French adults exceeding the TWI). The total number of tests in Table 3 refers to the total number of tests conducted in each testing method. With sample pooling, initially 731 tests are conducted to test 731 pools of 25 carcasses each (hence 18,275 carcasses are tested). If a pool test is positive, each carcass within that pool is tested individually. Therefore, in the Two-faced Sector scenario, the total number of

tests in this scenario is 731 + 25 × 16 (400) = 1131 tests because 16 first tests were positive. In the Regional Magnet scenario, the total number of tests conducted is 731 + 25 × 20 (500) = 1231 tests, with 20 initial positive tests followed by 500 secondary tests. The distribution of these supplementary tests in the regions is presented in Supplementary Material S5: Table S5. Lastly, for systematic testing aiming to test all consumed carcasses, 23,000,000 tests are performed.

When comparing the two scenarios, transitioning to the Two-faced Sector scenario (instead of the today's situation, which is the Regional Magnet scenario according to contamination prevalence with current testing) results:

- in a decreasing mean exposure (5.41 vs 5.44 pg/week/kg bw), hence, on average, French adults are less exposed to nDL-PCB in this scenario compared to the current situation, even with the use of sample pooling;

- in a 0.02 % decrease in the percentage exceeding the Tolerable Weekly Intake (from 8.04 % to 8.02 %). This implies that 10,000 fewer French adults will exceed the TWI.

When comparing the two scenarios, for the two other monitoring strategies, it is observed that the percentage exceeding the TWI remains unchanged regardless of the sector's evolution. For the systematic testing tool, the 0.01 % difference between the two scenarios is solely due to the drawing in the distribution of uncontaminated carcasses values drawn are slightly higher in the Two-faced Sector scenario, 7.96
compared with 7.95). However, the mean exposures are both the same (5.30 pg/week/kg bw). Unsurprisingly, in both prospective scenarios, the use of sample pooling as implemented here (with 18,275 tested carcasses in total) results in higher exposure to nDL-PCB for the French adult population compared to systematic testing, which tests each carcass. However, sample pooling serves to position its benefit between the current surveillance approach and the ideal “full control” scenario. With an intra-farm contamination rate of 1/10, the sample pooling seems a good compromise between the current testing and the systematic testing (weak decrease of the exposure with the systematic testing) because the contamination rate is low.

## 4. Conclusions

This paper evaluates the impact of sample pooling on human health by assessing the exposure of the French adult population to nDL-PCBs through pork consumption as a case study. The assessment considers two scenarios for the evolution of the sector and compares the current and systematic testing (where all carcasses exceeding the ML are replaced with uncontaminated ones) with sample pooling, which is designed to improve the surveillance of food chemical safety (Galharret et al., 2025; Ratel et al., 2024). A probabilistic simulation is proposed to evaluate this exposure based on real data quantifying contamination in meat and real consumption data, and simulated data due the prospective nature of the implementations.

Currently, the number of tests conducted to monitor pork meat contamination is very low (731 tests in 2023 in the surveillance plan) compared to the number of carcasses produced (around 23,000,000 carcasses). Then, the results indicate that using sample pooling (multiplying this low number by 25) would have a positive impact on consumer exposure in the context of nDL-PCB. This is because the current pork contamination prevalence closely mirrors that of the proposed Regional Magnet sector evolution.

In the French territory context, sample pooling emerges as a practical option, relatively easy to implement, potentially offering a compromise to increase the number of tested carcasses and reduce French adults' exposure. On the other hand, the studied systematic testing tool represents a theoretical maximum, as it tests all animals and consistently reduces consumer exposure compared to sample pooling. While this approach offers an ideal scenario, it is not a realistic option at present and serves only to position sample pooling between the current surveillance method and this ideal. Furthermore, it is observed that regardless of the surveillance tools implemented, the evolution of the sector toward a Two-faced Sector scenario reduces the exposure of French adults consuming pork meat because of a low contamination prevalence in France with this sector's transition, with more pig feed being produced on farms. However, the high weekly consumption by some people means that even with a drop in the prevalence of contamination in France (Two-faced Sector scenario) and more extensive (sample pooling) or more powerful (systematic testing) monitoring tools, high exposures remain numerous (around 8 % of French adults exceed the TWI) in our simulations.

As a prospective approach, it would be interesting to assess DL-PCB exposure after implementing controls on nDL-PCB, as higher nDL-PCB levels are linked to higher DL-PCB levels, according to SoMeat data.

### CRediT authorship contribution statement

**Hassan Hachem:** Writing – original draft, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Romy Lynn Chaib:** Writing – original draft, Validation, Methodology, Formal analysis.
**Erwan Engel:** Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.
**Isabelle Albert:** Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Erwan Engel reports financial support was provided by European Commission. Erwan Engel reports financial support was provided by French National Research Agency. If there are other authors, they 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

This work was supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement No 861917 – SAFFI as well as by the National Program on Key Research Project of China (2019YFE0103900). The author would also like to thank the French National Research Agency for funding this research (contract No. ANR-19-CEI21-0011; "SENTINEL: High-throughput screening tools for a reinforced chemical safety surveillance of food").

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodres.2025.116599.

### Data availability

Data will be made available on request.

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