# Characterizing the heterogeneous contamination of commercial paper and board food packaging at different scales

Lucas Biant, Phuong-Mai Nguyen, Murielle Hayert, Sandra Domenek, Olivier Vitrac

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## To cite this version:

Lucas Biant, Phuong-Mai Nguyen, Murielle Hayert, Sandra Domenek, Olivier Vitrac. Characterizing the heterogeneous contamination of commercial paper and board food packaging at different scales. Food additives & contaminants. Part A. Chemistry, analysis, control, exposure & risk assessment, 2025, 42 (3), pp.382-403. 10.1080/19440049.2025.2451639. hal-04970423

HAL Id: hal-04970423

https://hal.science/hal-04970423v1

Submitted on 25 Jul 2025

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# Characterizing the heterogeneous contamination of commercial paper and board food packaging at different scales

Lucas Bianta, Phuong-Mai Nguyenb, Murielle Hayerta, Sandra Domeneka and Olivier Vitraca

aUMR SayFood 0782, Université Paris-Saclay, INRAE, Palaiseau, AgroParisTech, France; bLaboratoire National de Métrologie et d'Essais (LNE), Trappes, France

## ABSTRACT

Assessing the contamination of paper and board (P&B) food packaging materials poses significant challenges due to the sensitivity limits of analytical methods and the low precision of sampling processes. This study aims to enhance the understanding of P&B food packaging contamination by investigating the distribution of contaminants at different scales using a combination of chromatographic and spectroscopic techniques. A total of 36 substances were targeted, including phthalates, photoinitiators, and bisphenol A. Key findings reveal that intra-packaging variability can lead to concentration variations by a factor of 2–7. Recycled materials generally exhibit higher contamination levels, with concentrations of certain contaminants reaching up to 700mg/kg. Microscopy, including confocal laser scanning microscopy (CLSM) and Raman micro-spectroscopy, enabled the identification and differentiation of contaminants, highlighting specific marker molecules and characteristic Raman bands. The study underscores the necessity of comprehensive sampling strategies, advocating for the grinding of entire packaging to obtain representative samples. The introduction of contamination "fingerprints," based on occurrences and correlations between concentrations, offers a promising approach for hazard identification and risk assessment. Overall, the findings contribute to the development of safer and more sustainable food packaging solutions, emphasizing the need for improved analytical techniques and standardized sampling methods in the context of increasing use of recycled materials.

## ARTICLE HISTORY

Received 1 August 2024

Revised 21 December 2024

Accepted 5 January 2025

## KEYWORDS

Cellulosic materials; recycling; multi-scale analysis; variability; microscopy; Raman spectroscopy

## Introduction

Papers and boards (P&B) have been implicated in multiple food contamination crises over the last two decades. These include contamination by printing inks (Deshwal et al. 2019), phthalates (Fierens et al. 2012), photoinitiators (Bradley et al. 2013), and mineral oil hydrocarbons (MOH) (Vollmer et al. 2011), prompting new regulations measures in Europe (EDQM CoE 2021), France (DGCCRF 2019), Germany (BfR 2021), and Switzerland (DFI 2016). Among these contaminants, MOH has received particular attention due to their prevalence in recycled P&B materials (FCM). Despite risk assessment by the European Food Safety Authority (EFSA 2012, 2023), significant data gaps persist regarding human exposure to MOH through food (Hochegger et al. 2021).

The contamination of P&B materials arises from various sources, including printed surfaces (Biedermann and Grob 2010; Biedermann et al. 2011), glued regions (Lommatzsch et al. 2016; Pivnenko et al. 2016), and recycled fibers, which may introduce contaminants more uniformly (Biedermann and Grob 2010). Unlike plastics, for which migration mechanisms are well understood, the transfer of contaminants from P&B remains poorly characterized due to the heterogeneous distribution of substances and their physicochemical properties, such as volatility and melting point.

Existing studies have shown that contamination variability within P&B can influence food safety. Nguyen et al. (2017) demonstrated non-intuitive mass transfer pathways, such as contamination occurring in direct contact with
packaging and food, while Lorenzini et al. (2013) reported that packaging orientation significantly impacts contamination levels. Biedermann et al. (2011) further observed higher contamination in noodles stored in corners or top/bottom locations of secondary packaging. Additionally, specific surface areas, such as closure systems, can contribute disproportionately to overall migration (Biedermann et al. 2021). Forensic analyses have emphasized the need to consider layer order and food contact conditions when interpreting contamination sources (Eicher et al. 2015).

While macroscopic contamination heterogeneities are relatively well documented, recycled materials introduce a supplementary level of variability at the fiber scale. The European Council (EDQM CoE 2021) and French authorities (DGCCRF 2019) recommend the use of UV absorption microscopy to detect residual traces of aromatic substances on recycled fibers, while Food Standards Australia New Zealand (FSANZ 2018) advocates for visible microscopy to identify previously printed spots. Contaminants, such as fillers, processing aids, and special refining agents, may be uniformly distributed, while ingredients of inks and adhesives often appear in localized hotspots. This study hypothesizes that recycled cellulosic fibers contribute to a more even distribution of contaminants, which may increase the risk of misidentifying hazards and underestimating associated risks. However, the extent and distribution of heterogeneities at the microscopic scale remain insufficiently explored.

This study addresses these gaps by evaluating the contamination heterogeneity in commercial packaging sampled from the French market as part of the collaborative research project FoodSafeBioPack (Ref. ANR-20-CE21-0009). To investigate these heterogeneities, we examined contaminant variability across multiple scales, from packaging items and specific parts to individual cellulosic fibers. Contamination at the macroscopic scale was assessed using solvent extraction and targeted chemical analyses following French regulatory requirements. At the microscopic scale, advanced methods such as confocal laser scanning microscopy (CLSM) and Raman spectroscopy provided insights into contaminant states and spatial distributions within fibers.

By integrating macroscopic and microscopic approaches, this study provides a detailed understanding of contamination profiles in recycled P&B packaging. Such insights contribute to improving safety assessments, developing optimized sampling strategies, and informing decisions on fiber sourcing, treatments, and decontamination processes. These findings are particularly relevant in the context of growing reliance on recycled materials and the need for safer, more sustainable food packaging solutions.

## Macroscopic description

Since pioneering work by Vollmer et al. (2011) showing the presence of MOH in high concentrations in paper and board (P&B) packaging, numerous studies have characterized the contamination of P&B intended for food contact. Table S1 of the Supplementary Material summarizes the findings of the main studies conducted from 2010 to 2020. These studies focused on contamination by mineral oil saturated hydrocarbons (MOSH), mineral oil aromatic hydrocarbons (MOAH), and diisopropylnaphthalene isomers (DIPN), which are ubiquitous contaminants in recycled P&B. These last compounds were used several years ago as solvents of carbonless copy papers (Barp et al. 2015a).

The concentration values reported in Table S1 range from 2mg/kg up to 1100mg/kg of P&B. These concentrations represent the average determination from one sampling or the arithmetic average of several samples. Recently, Kourkopoulos et al. (2022) highlighted the high variability and the lack of standardization in sampling and analysis procedures, which could explain the discrepancies observed in the safety assessments of P&B food contact materials (FCM). The concentration determinations reported in Table S1 are associated with three different sampling and extraction strategies:

a. Aggregation strategy. The entire packaging is shredded into small pieces and homogenized, and an aliquot (1–1.5g) is analyzed (Biedermann et al. 2011; Fengler et al. 2015). Hence, the obtained concentration refers to the whole packaging, not a specific region/part.
b. Focused sampling on suspected areas. Different studies used this strategy to compare contamination in printed and unprinted parts of the same packaging side or infer the contamination of P&B after peeling printed areas (Suciu et al. 2013). This approach facilitates the identification of sources of the migration into food.

c. Random or unspecified sampling involving the extraction of 1 g of packaging (Lorenzini et al. 2010; 2013; Barp et al. 2015a, 2015b; Conchione et al. 2020).

Most of the studies used one gram of P&B, which represents from 1 to 10% of the total weight of the packaging. No study involves the complete extraction of the packaging. At best, an importance sampling is applied to get a correct representation of printed parts. Only the study of

Suciu et al. (2013) separates the printed outer surface from the inner parts.

The variability in concentration determinations for P&B materials, as detailed in Table S1 of the supporting information, is analyzed specifically in Figure 1. This analysis involves plotting the max-to-mean ratio (MMR) against the average concentrations across all determinations for similar P&B packaging systems, which may correspond to different food products. MMR values are independent of the level found in P&B and range from 1.7 to 10.6. Without information on the type of study, it is reasonable to assume an MMR value equals the arithmetic average of all reported MMR values, which is 4.53. This value has been assigned to studies (empty symbols) that did not report any concentration ranges (studies labeled 1-3 and 6-7 in Figure 1).

Data clustering, performed only on the 18 fully documented datasets, shows that the variability

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Variability of contamination in paper and board food contact materials reported in nine studies of the literature, from 1: Biedermann et al. (2011), 2: Lorenzini et al. (2010), 3: Vollmer et al. (2011), 4: Lorenzini et al. (2013), 5: Suciu et al. (2013), 6: Barp et al. (2015a), 7: Barp et al. (2015b), 8: Fengler et al. (2015), 9: Conchione et al. (2020). The figure depicts the max-to-mean ratio (MMR) plotted against the average concentration measured in samples (mg/kg). Symbols represent different studies: circles for non-specified paper, triangles for virgin paper, and squares for recycled paper. Colors indicate the type of contaminant: blue for DIPN, orange for MOAH, brown for MOSH, purple for BPA, and green for DEHP. Filled symbols represent experimental values, while empty symbols indicate estimated values. Dashed lines show key percentiles (10th, 25th, 50th, 75th, and 90th). The overall mean MMR value is also indicated.</figcaption></figure>
between studies (inter-variability) is commensurable to the variations inside the same study (intra-variability). The interpretation of the origin of these variabilities is challenging. MMR values far above unity can suggest analytical difficulties, which are unlikely in this meta-analysis involving independent studies. The large MMR values could also imply that there may be contamination hotspots corresponding to regions (potentially inside a given sample) with exceptionally high levels of contaminants compared to the average. This could be due to under-optimized recycling processes, variability in the source materials, variability in storage conditions (temperature, exposure to air flow), or localized contamination. No effect of the origin of the paper, virgin or recycled, can be evidenced.

The overview of contamination concentration ranges illustrates the difficulty of closing the mass balance between the packaging and the food to establish the origin of the migration. Demonstrating causality is particularly difficult for the most volatile compounds, which can be transferred to the surroundings or between parts of the packaging material.

## Microscopy description

### Overview of microscope techniques to detect the contamination in P&B

Direct observations and localized measurements can offer an alternative to blind sampling and sample homogenization. Pigments and coloring substances can be identified visually in printed/ colored areas but tracking when these substances migrate is difficult. Similarly, substances not interacting with visible light are difficult to observe under a microscope. Mid-infrared microscopy uses vibrational spectroscopy at the scale of tens of micrometers in reflection mode or total reflection mode to identify functional groups and chemical substances. It has been successfully used to detect and identify pigment molecules in paint products (Harkins et al. 1959) and painting materials (Newman 1979).

Alternatively, and at much higher resolution, Raman spectroscopy detects the inelastic scattering of light to determine the vibrational modes of

molecules. It has been developed since the end of the last century to characterize the chemical treatment applied to fibrous materials (Lang et al. 1986), to study the mechanical behavior of polyolefin materials used in packaging (Li-Chan 1996), to determine diffusivities in plastic (LLDPE) packaging material (Mauricio-Iglesias et al. 2009) or to detect non-intentionally added substances (NIAS) in LDPE films intended for food contact (Portesi et al. 2019). The application of Confocal Raman Microscopy (CRM) to determine the diffusivity of dye molecules in cellulose fibers (Yang et al. 2020) encourages its use to characterize paper-based materials. Due to the recent progress in the development of analytical tools, Raman spectroscopy could be used as a qualitative technique to identify unknown samples, using the spectrum as a fingerprint of the component (Rostron et al. 2016). Implementing large databases could help improve Raman analysis quality (Orlando et al. 2021).

### Capabilities offered by Raman Spectroscopy Imaging

The contamination variability at the microscopic scale has only been studied recently by Hodes et al. (2020) using Raman Spectroscopy Imaging. The authors discarded mid-infrared spectroscopy due to its poor spatial resolution and lack of sensitivity to detect MOSH in P&B. Raman spectroscopy imaging offered better performances to evaluate the migration of ink and paper ingredients along cross-sections of different paper materials (coated or not, with fillers or not). The peak at 96 cm⁻¹ was used to detect aliphatic mineral oils (MOSH). It corresponds to the C-C torsion. Pigment components were monitored in the 1517 cm⁻¹–1552 cm⁻¹ region, where the peak at 1528 cm⁻¹ is typical of C=C and C-N stretching modes of aromatic pigments (Bouchard et al. 2009; Kunicki et al. 2013). The authors could demonstrate on aged samples that the presence of fillers and coatings could alter the internal migration of mineral oil from the printed surface. Different distributions of mineral oils were obtained over time, confirming a source of heterogeneity across the thickness. Due to their complex composition, the method
was not applied to commercially available P&B materials.

# Materials and methods

## Cellulosic materials

Blotting paper (100% cellulose, 90g/m²) was used as a reference material. The main characteristics of commercial packaging bought in local supermarkets in 2021 are presented in Table 1. Each packaging is named using a code from P1 to P7.

In addition to analyzing a random sample of one gram of each packaging, different parts of P1 and P5 (Figure S1) were sampled specifically. Figure S2 details the sampling process in the example of P5. Samples were taken in the printed area in the front face of each box (Figure S2c), glued parts and non-glued parts of the top and bottom closures (Figures S2d and S2e), and external and internal layers of the packaging side, separated by tear-offs (Figure S2f). For each part, at least one gram was collected, and repetitions from three packages of the same batch (purchased simultaneously in the same supermarket) were analyzed. Table 2 summarizes the nomenclature and description for each packaging part analyzed in confocal microscopy.

## Reference molecules

The molecules assessed in commercial packaging are separated into ten different classes. Table 3 summarizes their identities. All reference substances were purchased per analysis grade. Methanol (MeOH) purchased from VWR Chemicals is HPLC grade. Dichloromethane (DCM) was purchased from Thermo Fisher Scientific as a 99+%, extra pure solvent stabilized with ethanol.

<table><caption>**Table 1.** Summary of studied cellulosic materials purchased on the French market in 2021.</caption><thead><tr><th>Packaging code</th><th>Component</th><th>Paper grade</th><th>Food type in contact</th><th>Contact type</th></tr></thead><tbody><tr><td>P1</td><td>External box</td><td>Virgin</td><td>French fries</td><td>Direct</td></tr><tr><td>P2</td><td>External box</td><td>Virgin</td><td>Smoked salmon</td><td>Indirect</td></tr><tr><td>P3</td><td>External box</td><td>Virgin</td><td>Yogurt</td><td>Indirect</td></tr><tr><td>P4</td><td>External box</td><td>Recycled</td><td>Chocolate cake preparation</td><td>Indirect</td></tr><tr><td>P5</td><td>External box</td><td>Recycled</td><td>Popcorn</td><td>Indirect</td></tr><tr><td>P6</td><td>External box</td><td>Recycled</td><td>Rice</td><td>Direct</td></tr><tr><td>P7</td><td>External box</td><td>Recycled</td><td>Pet food</td><td>Direct</td></tr></tbody></table>

## Sample extraction and gas chromatography analysis

Approximately one gram of the packaging component, previously cut into small pieces, was placed in a glass vial and extracted with 15 mL of a 3:1 v/v DCM/MeOH mixture using an orbital stirrer at 200 rpm for two days at ambient temperature. The extracts were passed through PTFE filter syringes (20 µm and 0.45 µm pore sizes), and then the solvent was evaporated with a vacuum evaporation system (RapidVap, Labconco™) to enhance analytical signals. The evaporation was performed at 30°C with successive steps at 400 mbar for 30 min, 300 mbar for 30 min, and 100 mbar for 120 min. Once the extracts reached dryness, 1 mL of a solution with internal standards was added for chromatographic analysis. The internal standard solution contained acetophenone-d8, diacetylbenzene, and hexabromobenzene at a concentration of 100 mg/L to correct eventual dilution and injection errors. The overall extraction yield for unknown

<table><caption>**Table 2.** Summary of packaging parts for the second step of chromatographic analysis and confocal microscopy analysis.</caption><thead><tr><th>Sample code</th><th>Packaging part</th><th>Sample name</th><th>Thickness (µm)</th><th>Description of part analyzed in confocal microscopy</th></tr></thead><tbody><tr><td>P1-int</td><td>Side</td><td>Internal layer</td><td>197 ± 25</td><td>—</td></tr><tr><td>P1-ext</td><td>Side</td><td>External layer</td><td>116 ± 16</td><td>—</td></tr><tr><td>P1-print</td><td>Front</td><td>Printed</td><td>329 ± 12</td><td>—</td></tr><tr><td>P1-glu</td><td>Top/bottom</td><td>Glued</td><td>563 ± 21</td><td>—</td></tr><tr><td>P1-noglu</td><td>Top/bottom</td><td>Non-glued</td><td>331 ± 19</td><td>—</td></tr><tr><td>P5-intsurf</td><td>Side</td><td>Internal surface</td><td>—</td><td>Internal surface in contact with the food</td></tr><tr><td>P5-extsurf</td><td>Side</td><td>External surface</td><td>—</td><td>External surface printed in blue</td></tr><tr><td>P5-int</td><td>Side</td><td>Internal layer</td><td>110 ± 36</td><td>Middle layer, attached to the internal part</td></tr><tr><td>P5-ext</td><td>Side</td><td>External layer</td><td>101 ± 6</td><td>Middle layer, attached to the external part</td></tr><tr><td>P5-print</td><td>Front</td><td>Printed</td><td>409 ± 9</td><td>—</td></tr><tr><td>P5-glu</td><td>Top/bottom</td><td>Glued</td><td>698 ± 8</td><td>Fiber layer attached to the glue spot</td></tr><tr><td>P5-noglu</td><td>Top/bottom</td><td>Non-glued</td><td>418 ± 17</td><td>Internal surface of the closure part</td></tr></tbody></table>
<table><caption>**Table 3.** List of substances assessed in commercial packaging.</caption><thead><tr><th>Code</th><th>Chemical name</th><th>Short name</th><th>CAS number</th><th>M (g/mol)</th><th>Class</th><th>Origin</th></tr></thead><tbody><tr><td>M01</td><td>2,4-Ditertbutylphenol</td><td>Prodox 146</td><td>00096-76-4</td><td>206.3</td><td>A</td><td>α,β</td></tr><tr><td>M02 *</td><td>Butylated hydroxytoluene</td><td>BHT</td><td>00128-37-0</td><td>220.2</td><td>A</td><td>α,β</td></tr><tr><td>M03</td><td>Octadecyl 3-(3,5-ditertbutyl-4-hydroxyphenyl) propionate</td><td>Irganox 1076</td><td>02082-79-3</td><td>530.5</td><td>A</td><td>α,β</td></tr><tr><td>M04 *</td><td>Tris(2,4-ditertbutylphenyl) Phosphite</td><td>Irgafos 168</td><td>31570-04-4</td><td>646.4</td><td>A</td><td>α,β</td></tr><tr><td>M05 *</td><td>Tris(2,4-ditertbutylphenyl) Phosphate</td><td>Oxydized Irgafos 168</td><td>95906-11-9</td><td>662.4</td><td>A</td><td>α,β</td></tr><tr><td>M06 *</td><td>Bisphenol A</td><td>BPA</td><td>00080-05-7</td><td>220.1</td><td>B</td><td>α,β</td></tr><tr><td>M07</td><td>Bisphenol S</td><td>BPS</td><td>00080-09-1</td><td>250</td><td>B</td><td>α,β</td></tr><tr><td>M08</td><td>Mineral oil aromatic hydrocarbons</td><td>MOAH</td><td>N/A</td><td>N/A</td><td>C</td><td>α,β,δ</td></tr><tr><td>M09</td><td>Mineral oil saturated hydrocarbons</td><td>MOSH</td><td>N/A</td><td>N/A</td><td>C</td><td>α,β,δ</td></tr><tr><td>M10</td><td>2-Furanmethanol</td><td>–</td><td>00098-00-0</td><td>98.1</td><td>D</td><td>–</td></tr><tr><td>M11</td><td>Hexanal</td><td>Caproaldehyde</td><td>00066-25-1</td><td>100.1</td><td>D</td><td>–</td></tr><tr><td>M12</td><td>Benzene carbaldehyde</td><td>Benzaldehyde</td><td>00100-52-7</td><td>106</td><td>D</td><td>–</td></tr><tr><td>M13</td><td>Octanal</td><td>–</td><td>00124-13-0</td><td>128.2</td><td>D</td><td>–</td></tr><tr><td>M14</td><td>Nonanal</td><td>–</td><td>00124-19-6</td><td>142.2</td><td>D</td><td>–</td></tr><tr><td>M15</td><td>Decanal</td><td>–</td><td>00112-31-2</td><td>156.3</td><td>D</td><td>–</td></tr><tr><td>M16</td><td>Di(ethylene glycol) dibenzoate</td><td>DEDGB</td><td>00120-55-8</td><td>314.3</td><td>E</td><td>α,β</td></tr><tr><td>M17</td><td>Dibutyl sebacate</td><td>DBS</td><td>00109-43-3</td><td>314.5</td><td>E</td><td>α,β</td></tr><tr><td>M18</td><td>Di(propylene glycol) dibenzoate</td><td>DPGDB</td><td>27138-31-4</td><td>342.1</td><td>E</td><td>α,β</td></tr><tr><td>M19</td><td>Di(ethylhexyl) adipate</td><td>DEHA</td><td>00103-23-1</td><td>370.6</td><td>E</td><td>α,β</td></tr><tr><td>M20</td><td>Tri(2-ethylhexyl) trimellitate</td><td>TOTM</td><td>03319-31-1</td><td>546.8</td><td>E</td><td>α,β</td></tr><tr><td>M21</td><td>Aniline</td><td>–</td><td>00062-53-3</td><td>93.1</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M22</td><td>o-Toluidine</td><td>–</td><td>00095-53-4</td><td>107.2</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M23</td><td>2,4-Diaminotoluene</td><td>–</td><td>00095-80-7</td><td>122.2</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M24</td><td>4-Chloroaniline</td><td>–</td><td>00106-47-8</td><td>127.6</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M25</td><td>5-Nitro-o-toluidine</td><td>–</td><td>00099-55-8</td><td>152.2</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M26</td><td>2,5-Dichloroaniline</td><td>–</td><td>00095-82-9</td><td>162</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M27</td><td>4,4'-Methylene dianiline</td><td>–</td><td>00101-77-9</td><td>198.3</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M28</td><td>4,4'-Methylene di-o-toluidine</td><td>–</td><td>00838-88-0</td><td>226.3</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M29</td><td>3,3'-Dimethoxy benzidine</td><td>–</td><td>00119-90-4</td><td>244.3</td><td>F</td><td>α,γ,ε</td></tr><tr><td>M30</td><td>Pentadecafluorooctanoic acid</td><td>PFOA</td><td>00335-67-1</td><td>414.1</td><td>G</td><td>ε</td></tr><tr><td>M31</td><td>Hydroquinone</td><td>–</td><td>00123-31-9</td><td>110</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M32</td><td>Acetophenone</td><td>AP</td><td>00098-86-2</td><td>120.1</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M33 *</td><td>Benzophenone</td><td>BP</td><td>00119-61-9</td><td>182.1</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M34</td><td>Ethyl-4-(dimethylamino) benzoate</td><td>EDAB</td><td>10287-53-3</td><td>193.1</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M35</td><td>4-Methylbenzophenone</td><td>4-MBP</td><td>00134-84-9</td><td>196.1</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M36</td><td>4-Hydroxybenzophenone</td><td>4-HBP</td><td>01137-42-4</td><td>198.1</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M37 *</td><td>2,6-Diisopropylnaphtalene</td><td>DIPN</td><td>24157-81-1</td><td>212.3</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M38</td><td>Isopropyl-9H-thioxanthen-9-one</td><td>ITX</td><td>75081-21-9</td><td>254.1</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M39</td><td>4,4'-Bis(dimethylamino) benzophenone</td><td>4,4'-BDAP</td><td>00090-94-8</td><td>268.2</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M40</td><td>2-Ethylhexyl-4-(dimethylamino) benzoate</td><td>EHDAB</td><td>21245-02-3</td><td>277.2</td><td>H</td><td>α,γ,δ</td></tr><tr><td>M41</td><td>Diethyl phthalate</td><td>DEP</td><td>00084-66-2</td><td>222.3</td><td>I</td><td>α,β</td></tr><tr><td>M42 *</td><td>Diisobutyl phthalate</td><td>DIBP</td><td>00084-69-5</td><td>278.1</td><td>I</td><td>α,β</td></tr><tr><td>M43 *</td><td>Dibutyl phthalate</td><td>DBP</td><td>00084-74-2</td><td>278.1</td><td>I</td><td>α,β</td></tr><tr><td>M44</td><td>Benzylbutyl phthalate</td><td>BBP</td><td>00085-68-7</td><td>312.1</td><td>I</td><td>α,β</td></tr><tr><td>M45</td><td>Bis(2-ethylhexyl) terephthalate</td><td>DEHTP</td><td>06422-86-2</td><td>390.3</td><td>I</td><td>α,β</td></tr><tr><td>M46 *</td><td>Di-2-ethylhexyl phthalate</td><td>DEHP</td><td>00117-81-7</td><td>390.3</td><td>I</td><td>α,β</td></tr><tr><td>M47</td><td>Diisobutyl phthalate</td><td>DINP</td><td>28553-12-0</td><td>418.3</td><td>I</td><td>α,β</td></tr><tr><td>M48</td><td>Diisodecyl phthalate</td><td>DIDP</td><td>26761-40-0</td><td>446.3</td><td>I</td><td>α,β</td></tr><tr><td>M49</td><td>2-ethylhexyl acrylate</td><td>2-EHA</td><td>00103-11-7</td><td>184.1</td><td>J</td><td>ε</td></tr><tr><td>M50</td><td>Bis(2-ethylhexyl) maleate</td><td>BEHM</td><td>00142-16-5</td><td>340.2</td><td>J</td><td>ε</td></tr><tr><td>M51 *</td><td>Bis(2-ethylhexyl) fumarate</td><td>BEHF</td><td>00141-02-6</td><td>340.5</td><td>J</td><td>ε</td></tr></tbody></table>

* Substances analyzed during the second step of chromatographic analysis

A = antioxidants/B = bisphenols/C = ink solvent/D = NIAS/E = plasticizers/F = PAAs/G = PFOA/H = photoinitiators/I = phthalates/J = varnish monomers

α = recycled fibers/β = plastic layer/γ = printing inks and pigments/δ = glue/ε = coatings and varnishes

chemicals using this protocol has been estimated to be approximately 80% for P&B and around 70% for polymers (Gratia 2014).

The extracts were analyzed using a gas chromatograph (Varian 431-GC) coupled to a simple quadrupole mass spectrometer (MS) operated in electron ionization mode at 70 eV. The system was equipped with a Varian FactorFour VF-5MS column (30 m × 0.25 mm × 0.25 µm). Helium was

used as the carrier gas at a flow rate of 1.1 mL/min. The temperature program was as follows; an initial hold at 50 °C for 4 min, followed by a heating ramp of 15 °C/min up to 320 °C, where the temperature was held for 18 min. The mass spectrometer was operated at 250 °C, with a scan range of 40 and 650 m/z and a scan time of 0.15 s.

Quantification was performed using external calibration prepared from 36 external standards,
with concentrations ranging from 0.6 to 1500 mg/L. The limits of detection (LOD) and quantification (LOQ) of the 36 substances were determined using external calibration curves constructed with 5-8 eight concentration points, as detailed in Table S2 of the supporting information. For most analytes, the LOQ was 0.6 mg/L except for M04 (30 mg/L), M10 (3 mg/L), M15 (6 mg/L), M32 (1.5 mg/L) and M48 (1.5 mg/L). Additional GC-MS details, including retention times, reference MS ions, and LOQs, are provided in Table S3 of the supporting information.

## Microscopic analysis

### Raman micro-spectroscopy

Contaminants and their distribution in packaging were assessed using a multimodal confocal Raman microscope LabRam Soleil™ from Horiba, France, equipped with two laser sources (532 nm and 785 nm). Objectives 5x (NA 0.15, WD 23.5 mm) and 10x (NA 0.3, WD 15 mm) were used to acquire the images. The 10x objective was used for Raman spectra acquisition, with a laser (785 nm) spot measuring about 32 µm². The confocal hole was opened at 100 µm, which gives an optical plane slice with a thickness of approximately 50 µm by using the equation of optical slice thickness in a geometric-optical confocal microscope (Park et al. 2004). A 600 line/mm grating dispersed the backscattered light. The Raman signal was detected by a Peltier-cooled (−60 °C) electron-multiplying charge-coupled device (EM-CCD Horiba, 1024 pixels × 256 pixels). Spectra of blotting paper (BG) and virgin packaging were acquired with a laser power of 73 mW during 360 s. Spectra of recycled packaging were acquired with a laser power of 2.9 mW during 360 s. A polynomial smoothing with a window of five points was applied to the acquired spectra. Their baseline was corrected using the acquisition software LabSpec6®. The signal-to-noise ratio (SNR) of each spectrum was calculated as the ratio between the intensity of the highest peak and the mean intensity between 1650 cm⁻¹ and 1850 cm⁻¹, where no peaks are expected. The signal quality of spectra where SNR is below 10 was considered poor because peaks cannot be clearly differentiated from the

noise. The Raman signal of these spectra was minimized by dividing their overall intensity by two times the SNR. All spectra were exported as XML files and plotted using a Matlab® code (Matlab R2022b).

### Confocal laser scanning microscopy

Measurements were performed using a Leica TCS SP8 confocal laser scanning microscope (CLSM) coupled to a Leica DMi8 inverted microscope equipped with a laser source at 488 nm and a hybrid detector. The laser power was set to 0.7%. The detector collected the fluorescence emission between 495 nm and 545 nm at a gain of 100%. The resolution of obtained images was 2048 pixels × 2048 pixels, corresponding to a size of 1162.5 µm × 1162.5 µm. The images displayed the fluorescent parts of the material in red, a false color chosen by the operator. The image processing was done with Matlab®, using the following procedure. The initial red image (Figure S3a) was converted into a grayscale image (Figure S3b). The intensity of pixels was then converted into a logarithmic scale, and a threshold was applied to the lowest values. A 2D-Gaussian smoothing kernel filter with a standard deviation of 0.8 was applied to the image. A morphological opening was then applied to each pixel, using a disk-shaped structuring element of radius 5 pixels. The image was then binarized using 80% of the global threshold of the grayscale image (Figure S3c). The connected components (objects) with less than 50 pixels were removed, and the image was horizontally and vertically dilated using a line structuring element of length 5. Finally, the different objects are counted and displayed in different false colors (Figure S3d).

## Data treatment for statistical analysis

### Chemical contamination interpretation

A data treatment involved contamination values obtained with GC-MS. For every sample, each concentration value of the substances was stored in a table. The disparities of contamination were evaluated by computing the pairwise distance between samples' contamination. The distance between two samples, A and B, corresponding to
the information that samples do not share, is calculated as the complementary Pearson's correlation coefficient using Equation 1.

$$d_{AB}^2 = 1 - \left| \frac{\sum_{i=1}^{n} (A_i - \bar{A})(B_i - \bar{B})}{\sqrt{\sum_{i=1}^{n} (A_i - \bar{A})^2} \sqrt{\sum_{i=1}^{n} (B_i - \bar{B})^2}} \right| \quad (1)$$

Where $A_i$ and $B_i$ are the concentrations of substance $i$ in the samples A and B respectively. $n$ is the total number of considered substances (here, 36 or 10 substances). $\bar{X}$ indicates the average concentration of all substances in sample X. A zero concentration is assigned to the substance $i$ when it is not detected. These methods were implemented to create detailed maps and cluster analyses using Matlab® to visualize contamination patterns and their variabilities at different scales. Pairwise distances were stored in a tree containing hierarchical clusters using unweighted average distances and a city block metric. The distance between samples is presented on a two-dimensional map, using the two dimensions that maximize distances (here, denoted dim 1 and dim 2).

## Interpretation of binary data

### Chemical fingerprint interpretation

A second treatment type was done on packaging contamination data, using the occurrence of substances in several samples. A binary matrix of $m$ lines (here 14 samples from 7 different packaging) and $n$ columns (here 36 substances evaluated) was constructed from contamination values, using one when the substance is present and zero when it is not. To evaluate the pairwise distances based on the presence or absence of these substances, the Jaccard index criterion was used. This method quantifies the similarity between two sets of binary variables. The Jaccard index ($J(A, B)$), using the number of substances present in both spectra ($|A \cap B|$) and the number of substances present in either spectrum ($|A \cup B|$), is defined as:

$$J(A, B) = \frac{|A \cap B|}{|A \cup B|} \quad (2)$$

The Jaccard distance ($D_j(A, B)$), which measures dissimilarity between A and B, is calculated as:

$$D_j(A, B) = 1 - J(A, B) \quad (3)$$

This distance ranges from 0 (identical presence/absence of substances) to 1 (completely dissimilar contamination). It is independent of the absolute number of substances or the detection capability. Even a more sensitive technique would yield a similar distance. Since the Jaccard index relies on set operations (intersection and union), it inherently scales with the size of the sets. Considering a larger number of substances does not affect the ratio of common ones. This focus on relative overlap ensures that the metric remains robust despite changes in the total number of substances being considered. The binary nature of the data (presence or absence) further contributes to the metric's stability. Unlike continuous data, where scale and magnitude can impact the metric, binary data ensures that the Jaccard index reflects purely the presence/absence pattern. The Jaccard distance between samples is presented on a two-dimensional map, using the two dimensions that maximize distances (here, denoted dim 1 and dim 2).

### Raman signal interpretation

This data treatment has been similarly applied to the data of Raman spectroscopy. Peak positions were extracted from the Raman spectra of all samples (Raman shifts in $cm^{-1}$). A binary matrix of $m$ lines (here 38 spectra with peaks) and $n$ columns (here 86 different peak positions) was constructed, using one when the peak is present in the spectrum and zero when it is not. The Jaccard index was used to calculate the pairwise distances between the samples. These distances were then used to generate a dendrogram, which visually represents the clustering of the samples based on their Raman spectra. The dendrogram provides a hierarchical clustering, where samples with similar profiles are grouped together. The clustering was performed using an agglomerative hierarchical clustering method, where each sample starts in its own cluster, and pairs of clusters are merged
as one moves up the hierarchy. The linkage criteria used for merging clusters was based on the average distance between all pairs of samples in the two clusters. The resulting dendrogram allows us to visualize and analyze the similarities and differences in Raman signals among different components of commercial P&B packaging.

## Results and discussion

Several substances used as production aids, refining and coating agents, printing ink and glue components, and contaminants are subjected to restrictions according to the French law (DGCCRF 2019) applicable to cellulosic materials intended to be in direct or indirect contact with food. The study targeted 36 substances (Table 3) reported as contaminants (i.e. not belonging to the fibrous material) regardless of their intentional use in paper or packaging processing.

## Level of contamination in commercial packaging

The presence was analyzed of any of the 36 contaminants and their concentrations in commercial packaging P1-P7 in GC-MS and subjected to external calibration. Figure S4 shows a typical

chromatogram of the solvent extract of the P5 sample. All peaks associated with targeted substances were identified via their mass spectrum and retention times. The raw concentration values are reported in Table 4. The values corresponding to two identical packages purchased in the same supermarket are shown as “PX-1” and “PX-2”, where $X=1...7$ is the packaging code (see Table 1). A zero concentration was assigned to non-detected substances.

The total contamination level of virgin packaging was 1453 mg/kg, 82 mg/kg, and 1125 mg/kg, respectively, in P1, P2, and P3. The contamination value reached 2322 mg/kg in P5, while the other recycled packaging P4, P6, and P7 have overall contaminations of 1164 mg/kg, 1259 mg/kg, and 536 mg/kg, respectively. The high contamination levels in recycled packaging probably do not come only from recycling because virgin packaging is also highly contaminated. Recycled materials present more contaminant substances than virgin ones (twenty-two versus twelve on average), but not necessarily a higher contamination level. The recycling process tends to include molecules from other sources (non-food-grade P&B, plastics) and increase the number and levels of contaminants (Geueke et al. 2018).

<table><caption>Table 4. Concentration (in mg/kg) of 36 substances in two repetitions of seven packaging samples.</caption><thead><tr><th>Code</th><th>M01</th><th>M02</th><th>M03</th><th>M04</th><th>M05</th><th>M06</th><th>M10</th><th>M11</th><th>M12</th><th>M13</th><th>M14</th><th>M15</th><th>M16</th><th>M18</th><th>M20</th><th>M26</th><th>M31</th><th>M32</th></tr></thead><tbody><tr><td>P1-1</td><td>0</td><td>2.01</td><td>2.00</td><td>176.5</td><td>356.1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P1-2</td><td>0</td><td>1.94</td><td>1.13</td><td>101.5</td><td>280.4</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P2-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3.37</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P2-2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3.11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P3-1</td><td>0</td><td>1.20</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2.61</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P3-2</td><td>0</td><td>1.19</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.96</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>P4-1</td><td>0</td><td>1.26</td><td>0</td><td>0</td><td>0</td><td>1.38</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8.36</td><td>0</td><td>4.91</td><td>1.15</td><td>0</td><td>2.85</td><td>0</td><td>0</td></tr><tr><td>P4-2</td><td>0</td><td>1.30</td><td>0</td><td>0</td><td>0</td><td>1.47</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3.61</td><td>0</td><td>5.02</td><td>1.24</td><td>0</td><td>2.85</td><td>0</td><td>0</td></tr><tr><td>P5-1</td><td>0.44</td><td>1.20</td><td>0</td><td>147.7</td><td>1043.6</td><td>5.15</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2.5</td><td>0.45</td><td>0</td><td>2.45</td><td>0</td><td>0</td></tr><tr><td>P5-2</td><td>0</td><td>1.21</td><td>0</td><td>84.8</td><td>948.8</td><td>5.12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2.22</td><td>0.38</td><td>0</td><td>2.45</td><td>0</td><td>0</td></tr><tr><td>P6-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.43</td><td>0</td><td>0</td><td>0</td><td>4.35</td><td>10.41</td><td>0</td><td>214.8</td><td>70.26</td><td>0</td><td>2.09</td><td>0</td><td>0</td></tr><tr><td>P6-2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.44</td><td>0</td><td>0</td><td>0</td><td>7.02</td><td>12.75</td><td>0</td><td>168.9</td><td>58.14</td><td>0</td><td>2.09</td><td>0</td><td>0</td></tr><tr><td>P7-1</td><td>0</td><td>2.55</td><td>1.65</td><td>0</td><td>0</td><td>1.98</td><td>0</td><td>0</td><td>0</td><td>14.36</td><td>5.93</td><td>0</td><td>5.45</td><td>1.53</td><td>0</td><td>2.46</td><td>0</td><td>0</td></tr><tr><td>P7-2</td><td>0</td><td>2.50</td><td>0</td><td>0</td><td>0</td><td>2.00</td><td>0</td><td>0</td><td>0</td><td>13.91</td><td>4.20</td><td>0</td><td>5.60</td><td>1.54</td><td>0</td><td>2.46</td><td>0</td><td>0</td></tr><tr><td>Code</td><td>M33</td><td>M34</td><td>M35</td><td>M36</td><td>M37</td><td>M38</td><td>M39</td><td>M40</td><td>M42</td><td>M43</td><td>M44</td><td>M45</td><td>M46</td><td>M47</td><td>M48</td><td>M49</td><td>M50</td><td>M51</td></tr><tr><td>P1-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>10.87</td><td>0</td><td>61.01</td></tr><tr><td>P1-2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>15.87</td><td>0</td><td>95.37</td></tr><tr><td>P2-1</td><td>1.67</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4.02</td></tr><tr><td>P2-2</td><td>1.3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4.04</td></tr><tr><td>P3-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>50.64</td><td>22.02</td><td>150.7</td></tr><tr><td>P3-2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>48.35</td><td>21.86</td><td>144.8</td></tr><tr><td>P4-1</td><td>4.69</td><td>0</td><td>0.65</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.34</td><td>2.29</td><td>0</td><td>0</td><td>3.84</td><td>0</td><td>0</td><td>41.53</td><td>18.80</td><td>106.5</td></tr><tr><td>P4-2</td><td>4.73</td><td>0</td><td>0.60</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.24</td><td>2.30</td><td>0</td><td>0</td><td>5.99</td><td>0</td><td>0</td><td>37.27</td><td>15.49</td><td>83.08</td></tr><tr><td>P5-1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.46</td><td>0</td><td>0</td><td>0</td><td>0.74</td><td>1.53</td><td>0</td><td>0</td><td>2.40</td><td>0</td><td>0</td><td>0</td><td>0</td><td>32.29</td></tr><tr><td>P5-2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8.34</td><td>0</td><td>0</td><td>0</td><td>0.81</td><td>1.53</td><td>0</td><td>0</td><td>2.41</td><td>0</td><td>0</td><td>0</td><td>0</td><td>36.43</td></tr><tr><td>P6-1</td><td>7.14</td><td>0</td><td>1.24</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.05</td><td>0</td><td>0</td><td>10.41</td><td>0</td><td>28.57</td></tr><tr><td>P6-2</td><td>7.40</td><td>0</td><td>1.29</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.22</td><td>0</td><td>0</td><td>6.62</td><td>0</td><td>35.31</td></tr><tr><td>P7-1</td><td>161.3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.06</td><td>0.86</td><td>0</td><td>0</td><td>2.93</td><td>0</td><td>0</td><td>38.56</td><td>0</td><td>61.47</td></tr><tr><td>P7-2</td><td>142.3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.87</td><td>0.72</td><td>0</td><td>0</td><td>2.77</td><td>0</td><td>0</td><td>0</td><td>0</td><td>61.71</td></tr></tbody></table>
<figure><img src="image_4.png" /></figure>

Figure 2 graphically analyzes the concentration data of Table 4 using Principal Coordinate Analysis (PCoA). Pairwise distances are calculated from their occurrence *via* their Jaccard Index (Equation 3 characterizing distances between fingerprints) and the complementary Pearson's correlation (Equation 1 characterizing distances between profiles) in Figures 2a and 2b, respectively. The two represented dimensions capture 68% (for Figure 2a) and 91% (for Figure 2b) of the total distance between all samples. The distance associated with other dimensions is shown as a filled gray disk and represents the size of the null space, that is, of a theoretical sample with almost none of the 36 substances. The average mean squared distance between repetitions is either much smaller than the null space (Figure 2a) or proportional to it (Figure 2b).

The Jaccard index produces distances bound to unity, which is materialized by the external blue circle in Figure 2a. Since only the substance M51 (varnish monomer) was found in all samples, the null space is associated with one substance. The Jaccard distance between samples measures the number of mutually exclusive substances. Similar substances were found in all samples except for packaging P7, where two substances (M03 and M49) are found in P7-1 and not in P7-2; they are an antioxidant and a varnish monomer, respectively. Based on the occurrences of the 36 substances, virgin and recycled packaging samples are separated into two clusters along the first principal coordinate axis (Dim 1). On average, recycled materials and virgin ones are located at the same distance from the null space, showing similar numbers of contaminants.

Replacing contamination fingerprints (presence/absence of substances) with profiles (concentration ranges) leads to a different map (Figure 2b) without a clear separation of virgin and recycled materials. The distances are unbound and related to the mean square concentration ranges, with the most polluted samples (P5 and P6) located farther from the null space. The first principal coordinate axis (Dim 1) essentially supports the magnitude of the contamination for the major contaminants and leads to a distribution similar to the one observed along Dim 2 in Figure 2a. The second axis (Dim 2) provides

additional differentiation for the minor components. The differences between samples tended to appear larger when concentrations were used rather than occurrences. The distances between two repetitions were commensurable to the diameter of the null space (i.e. the central circle). The largest discrepancy between samples of the same packaging was identified for the French fries packaging (e.g. P1-1 and P1-2 for the fries) but remains smaller than the distances between different packaging systems and very similar (i.e. a short distance between them) for each food product. Recycled P&B packaging was distributed similarly but farther from the center, suggesting higher contamination levels, in particular for P5 and P6.

## Heterogeneous contamination within packaging

The heterogeneity of contamination within the same packaging was further analyzed for virgin (P1) and recycled (P5) packaging systems, which exhibited the highest overall contamination levels. Various parts of each packaging material (see Table 2) were analyzed separately. The analysis focused on ten common substances present at the highest contamination levels (excluding MOSH and MOAH) to facilitate the comparison between parts and packaging systems. These substances are labeled with an asterisk (*) in Table 3; they correspond to M02 (Irganox 1076), M04 (Irganox PS 800), M05 (bisphenol A), M06 (bisphenol F), M33 (4-methylbenzophenone), M37 (2-ethylhexyl 4-(dimethylamino)benzoate), M42 (diisobutyl phthalate), M46 (bis(2-ethylhexyl) phthalate), and M51 (triphenylphosphine oxide). Their concentration levels in the different parts of P1 and P5 are presented in the supporting information, Table S4.

Figure 3 illustrates the total concentration for the ten substances in each packaging part (Figure 3a), their concentration distribution between parts (Figure 3b), and classes of substances (Figure 3c).

In detail, the bar graph of Figure 3a shows that the overall contamination levels vary between the different parts of P1 and P5. Recycled packaging P5 generally exhibits higher contamination levels compared to virgin packaging P1. The printed areas (including the external layer) and
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Projection of pairwise distances between P&B samples, based on (a) the occurrences of 36 substances in each sample (Jaccard distance from Equation 2) and (b) the concentration of 36 substances in each sample (Pearson distance from Equation 1). Symbols represent different sample pairs: triangles for Sample 1 and circles for Sample 2. The colors indicate the type of packaging: yellow for virgin packaging and blue for recycled packaging. The gray circles represent the average mean-square distance on all other dimensions and the unexplained intra-packaging mean-square distance. The dashed lines illustrate key percentiles (10th, 25th, 50th, 75th, and 90th), providing a visual guide to the distribution of contamination fingerprints. The solid gray circle in the center denotes the unexplained intra-packaging mean-square distance, while the outermost blue circle marks the boundary for completely different contamination fingerprints.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 3.** (a) Total concentration (in mg/kg) of contaminant classes in different parts of packaging P1 and P5. (b) The concentration of 10 contaminants in packaging P1 and P5. (c) Concentration of substances from 5 different classes in different parts of packaging P1 and P5.</figcaption></figure>

glued parts tend to have higher contamination levels, indicating that these areas may accumulate more contaminants during packaging manufacturing and recycling.

The box plot of Figure 3b compares the concentrations of specific substances between virgin packaging P1 and recycled packaging P5. The concentrations of substances are shown on a

logarithmic scale to accommodate the wide range of values from 0.3 to 500 mg/kg. The distribution highlights that certain substances are more prevalent in recycled packaging P5 than in virgin packaging P1. Substances such as Irganox PS 800, bisphenol A, 4-methylbenzophenone, and diisobutyl phthalate exhibit particularly high concentrations in recycled packaging, suggesting these
contaminants are more likely introduced or con-
centrated during the recycling process.

Figure 3c depicts the distribution of different classes of substances (antioxidants, bisphenol, photoinitiators, phthalates, and varnish monomers) within various parts of the packaging. Each plot compares the concentration of a specific class of substances in different parts of P1 and P5, including printed (print), glued (glu), non-glued (no-glu), internal (int), and external (ext) parts. The radar plots reveal very similar contamination patterns P1 and P5 for each substance class. Differences appear mainly in concentrations, higher in P5 (recycled) than in P1. For example, photoinitiators and phthalates show higher concentrations in the printed and glued parts of P5 compared to P1. Varnish monomer contamination is significantly higher in recycled packaging P5. Antioxidants are mainly found in glued parts of P1 and P5. Bisphenol is an exception as it is not present in P1 but appears in all parts of P5.

Based on Pearson distance criteria, Figure 4a
shows chemical shifts from the origin (theoretical
clean sample without any of the substances) as
well as total concentration on the second vertical
axis. A two-dimensional map associating samples
into five clusters using the K-means method is
shown in Figure 4b. This representation high-
lights no major distinction between P1 and P5
samples, as they appear mixed in the Pareto dia-
grams (Figure 4a) and clusters (Figure 4b). Glued
and internal parts seem to be the most contami-
nated, except for P1-int-2, which, surprisingly,
was the least contaminated.

Figure 4a shows the average mean-square dis-
tance from the origin of each packaging compo-
nent sample. This analysis demonstrates that the
distances vary between parts of P1 and P5, with
recycled packaging P5 generally showing higher
average distances. The total concentration levels
(black line) also vary across different parts, with
glued and printed areas exhibiting higher levels.

Figure 4b presents a two-dimensional map
using the K-means clustering method, associating
samples with five distinct clusters. This analysis
indicates no clear separation between P1 and P5
samples, as they are mixed within the clusters.
The glued and internal parts are generally more

contaminated, except P1-int-2, which was the
least contaminated. The centroids of the P1 and
P5 component samples are shown, along with the
distance of each cluster to the center. The filled
gray disk represents the unexplained
intra-packaging distance, illustrating the variabil-
ity within packaging components.

In summary, the analyses presented in Figures 3 and 4 highlight the complexity and variability of contamination patterns within different parts of packaging materials. The findings suggest recycled packaging generally exhibits higher contamination levels, particularly in printed and glued areas. However, the clustering analysis shows no significant distinction between virgin and recycled packaging samples, indicating that multiple factors, including manufacturing and recycling processes influence contamination patterns. Indeed, the same hierarchy appears in both packaging types, with the lowest contamination for the internal layer and the highest for the external part (four times higher in P5 and five times higher in P1).

All these observations highlight the very het-
erogeneous distribution of contaminants within a
package. The random sampling process used in
the first part of the study led to an average con-
centration value, which is not necessarily repre-
sentative of all parts of the packaging. The results
confirm the necessity for representative sampling
during contamination assessment and for a har-
monized method of evaluation, as requested by
Koster et al. (2020) and Kourkopoulos et al.
(2022). In terms of safety, the low level of con-
tamination in internal parts is encouraging
because it lowers the risk of contamination of the
food in contact with the packaging. The external
parts present higher concentrations of chemical
substances from coating and printing. According
to this, the packaging design is important to
avoid migration from external layers to food via
leaks around the closures, as described in
Biedermann et al. (2021).

Microscopic distribution and quantification of contamination spots

The French DGCCRF (2019) recommends using
microscope images to detect contaminant
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 4.** (a) Pareto chart of total distances (sum of distances on every dimension) between P1 and P5 samples based on their contamination by 10 substances. (b) Projection of pairwise distances and clustering of P1 and P5 samples based on their contamination by 10 substances.</figcaption></figure>
particles indicative of recycled fibers. This approach is supported by the Technical Guide of the Resolution CM/Res (2020) 9 of the European Council (EDQM CoE 2021), which evidences the presence of recycled material under ultraviolet transmission light microscopy. Typical microscopic cross-sectional observations of recycled P&B (P5) are shown in Figure 5.

Figure 5a illustrates the internal layers of P5 observed at 300x magnification using a ZS200 objective of a Keyence VHX-7000 numerical microscope. The presence of color spots in the fibrous network is typical of recycled materials. These spots and colored fibers likely originate from the previous use of these fibers before recycling. Despite undergoing deinking steps during recycling, a significant proportion of ink products remains in the recycled materials (Jamnicki et al. 2015). Newspapers and other printed media entering the recycling process are primary sources of mineral oils in recycled P&B packaging (Biedermann et al. 2011).

Figure 5b, obtained using a laser source at 488 nm, reveals fluorescent spots corresponding to

substances in these colored spots. Such contamination spots were not observed in the virgin packaging P1 under the same acquisition conditions. Figure 5c shows the distribution of contaminants throughout the thickness of the material, not just near the external printed surface.

The same analysis was conducted on different parts of P5 (refer to Table 2) to quantify contamination and investigate its heterogeneity. Image processing was applied to the images obtained when a 488 nm laser source illuminated samples. Examples of ink contamination spots on the internal surface (Figure 6a) and the external printed surface (Figure 6b) are presented. The pattern of contamination on the external printed surface is typical of the application of colored ink on printed materials, with spots being equidistant and relatively similar in size. Conversely, the pattern of contamination in other parts (internal surface, internal layer, external layer, glued part) does not follow a discernible pattern.

The number of contamination spots per area varies across different parts (Table 5). The

<figure><img src="image_9.png" /><figcaption>**Figure 5.** Cross-sectional analysis of packaging P5. (a) Material axes. (b) Numerical microscope image of the packaging slice in xz plane. (c) Confocal laser scanning microscope image of the packaging slice. (d) Number of contaminant spots through different layers of the thickness of packaging P5. The bar graph on the right provides a count of contaminant spots at various positions along the z-axis, from the external surface to the internal surface. The blue bars represent the counts from this image, while the orange bars show the average counts from three images with 95% confidence intervals.</figcaption></figure>
<table><caption>**Table 5.** Statistical analysis of contamination spots detected in packaging P5 using CLSM.</caption><thead><tr><th>Sample part</th><th>Surface observed (mm²)</th><th>Number of spots</th><th>Area of spots (µm²)</th><th>Covering (µm² spots/µm² image)</th></tr></thead><tbody><tr><td>External surface</td><td>1.35</td><td>117±1</td><td>465±131</td><td>0.0298±0.0092</td></tr><tr><td>Internal surface</td><td>1.35</td><td>247±71</td><td>114±20</td><td>0.0125±0.0035</td></tr><tr><td>Internal layer</td><td>1.35</td><td>111±18</td><td>113±10</td><td>0.0056±0.0010</td></tr><tr><td>External layer</td><td>1.35</td><td>100±36</td><td>117±16</td><td>0.0054±0.0025</td></tr><tr><td>Glued part</td><td>1.35</td><td>64±10</td><td>113±10</td><td>0.0072±0.0035</td></tr><tr><td>Side cross-section</td><td>0.68</td><td>141±65</td><td>110±6</td><td>0.0061±0.0006</td></tr></tbody></table>

average size of spots, four times smaller than the original size of prints (on the external surface), remains consistent across all packaging parts. In contrast, the number of contaminant spots varies. This suggests that the size of ink spots is reduced during recycling due to deinking, but the resulting number of contamination spots in contact with food is not diminished (Figure 6c,d).

<figure><img src="image_10.png" /><figcaption>**Figure 6.** Detailed analysis of contamination spots on both the internal and external surfaces of the P5 packaging using confocal laser scanning microscopy. (a) Contamination spots on the internal surface of P5 were detected by confocal laser scanning microscopy. (b) Contamination spots on the external surface of P5 were detected by confocal laser scanning microscopy. (c) Size distribution of contamination spots on the internal surface of P5. (d) Size distribution of contamination spots on the external surface of P5.</figcaption></figure>
Quantification of spots highlighted the homogenization of contamination by inks after recycling. Despite differences in contaminant concentrations across packaging parts, contamination at the fiber scale remains homogeneous. Considering the results of contamination at macroscopic and microscopic scales, the recommended sampling for safety assessment of P&B materials is to mix all parts of the packaging and assess a representative quantity of this homogenized mixture.

## Identification of contaminants by Raman micro-spectroscopy

Figure 7 displays the normalized Raman spectra of the different samples acquired with the 785 nm laser in the range of 250–1850 cm⁻¹. Identification of peaks and associated possible molecular structures and usages are summarized in Table 6.

Figure 7a shows the spectrum of blotting paper, chosen as a reference. The spectrum corresponds to that of cellulose, as described in chapter 8 of the seminal book *IR and Raman Spectroscopy* (Larkin 2011). The Raman spectra of different parts of P1 (virgin) closely resemble the spectrum of blotting paper (Figure 7b). Almost all peaks of cellulose can be identified in the spectra of P1 samples. The labelled peaks that differ from those of cellulose include three peaks at 281 cm⁻¹, 711 cm⁻¹, and 1085 cm⁻¹, characteristic of glued samples. These peaks were observed in the Raman spectrum of calcium carbonate (CaCO₃) (Yamauchi et al. 2020; Macchia et al. 2022) typically used as a filler in polyvinyl acetate (PVA)-based adhesive (Gadhave 2023) and starch-based adhesive (Gadhave and Gadhave 2022) for the paper and board industry. The presence of CaCO₃ in recycled paperboard is also explained by its use in PVA nanocomposites intended to remove dyes from wastewater (Jahani et al. 2020), which could be used during the froth flotation process of deinking applied to recycled paper.

Unlike those of P1, the Raman spectra of P5 (Figure 7c) are very different from the reference blotting paper. Indeed, no peaks of cellulose can be identified in those spectra, corresponding to colored fibers and visible spots of contamination. The reason for the complete loss of the Raman signal of cellulose in recycled packaging is yet to

be determined. Characteristic peaks of CaCO₃ are still present in glued samples of P5. Several peaks appear to be characteristic of the contamination by inks, with typical peaks of azo dyes (Bouchard et al. 2009) and phthalocyanine dyes (Kunicki et al. 2013) observed at 679, 745, 1181, 1230, 1264, 1528, and 1596 cm⁻¹. The peak at 1605 cm⁻¹, typical from C=O or aromatic C=C (Smith and Dent 2005), could also be characteristic of quinacridone dyes (Bouchard et al. 2009).

Combining the microscopic observations from CLSM with Raman spectra information, contamination of recycled fibers appears mainly as spots distributed throughout all parts of the packaging. Raman spectroscopy enables the detection and identification of ink pigments in recycled packaging.

## Discrimination of packaging samples by statistical analysis

Statistical analysis was conducted on all samples of P1 and P5 and tested on random samples of P2, P3, P4, P6, and P7. The pairwise similarities between samples can be displayed on a dendrogram (Figure 8), where similar samples are grouped. Virgin samples (P1, P2, and P3) are more similar to cellulose (BG) than recycled ones (P4, P5, P6, and P7). The focus on typical spots of contamination in recycled materials partly explains this difference. The non-glued part of P1 is different from other P1 parts because it corresponds to the internal plastic liner. The glued parts of P1 packaging are distinct from the internal and external layers due to glue components contaminating the signal.

Extracting groups of samples from the P5 packaging using the distance criterion was challenging. The correlation between signals does not follow the different parts of the packaging because spots of contamination by ink are distributed in every packaging part. Moreover, adding other packaging samples to the statistical analysis demonstrates the method's ability to quickly discriminate recycled (P4, P6, and P7) from virgin (P2 and P3) packaging. The two samples of P2 and P3 can be differentiated from other samples of virgin packaging. In contrast, the contamination fingerprint of recycled packaging samples P4, P6, and P7 is not easily distinguishable.
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 7.** (a) Typical Raman spectra of blotting paper. (b) Raman spectra of samples from virgin packaging P1 compared to blotting paper. (c) Raman spectra of samples from recycled packaging P5 compared to blotting paper.</figcaption></figure>
<table><caption>**Table 6.** Identification and assignment of Raman peaks in commercial packaging.</caption><thead><tr><th>Raman peak (cm<sup>-1</sup>)</th><th>Associated vibration</th><th>Possible molecule</th><th>Usage in P&B packaging</th><th>References</th></tr></thead><tbody><tr><td>281</td><td>Lattice vibration of crystal structure</td><td>CaCO<sub>3</sub></td><td>Filler or adhesive</td><td>1, 2</td></tr><tr><td>711</td><td>Bending mode</td><td>CO<sup>2</sup><sub>3</sub></td><td>Filler or adhesive</td><td>1, 2</td></tr><tr><td>1085</td><td>Symmetric vibration</td><td>CO<sup>2</sup><sub>3</sub></td><td>Filler or adhesive</td><td>1, 2</td></tr><tr><td>679 & 745</td><td>Ring deformation & bending mode</td><td>Aromatic pigment</td><td>Ink component</td><td>3, 4</td></tr><tr><td>1181 & 1230 & 1264</td><td>Aromatic C-H bending</td><td>Aromatic pigment</td><td>Ink component</td><td>3, 4</td></tr><tr><td>1528 & 1596</td><td>Aromatic C=C or C-N stretching modes</td><td>Aromatic pigment</td><td>Ink component</td><td>3, 4</td></tr><tr><td>1605</td><td>Aromatic C=C or C=O stretching modes</td><td>Aromatic or conjugated pigment</td><td>Ink component</td><td>3, 4</td></tr></tbody></table>

1: Macchia et al. (2022), 2: Yamauchi et al. (2020), 3: Bouchard et al. (2009), 4: Kunicki et al. (2013)

Conclusions and perspectives

Assessing the contamination of paper and board (P&B) packaging materials has long been fraught with challenges, from the low sensitivity of chemical analytical methods to the low precision of sampling processes. These limitations become particularly critical when dealing with recycled materials, where contamination is inherently heterogeneous and the number of potential substances is extensive. For example, under French regulations, more than 50 substances must be quantified in P&B packaging, significantly exceeding the regulatory requirements for plastics. The absence of prior compliance declarations and the variability of contamination across packaging parts further complicate this assessment.

Our study demonstrates that intra-packaging variability can lead to concentration differences of up to a factor of seven, emphasizing the need for comprehensive sampling to ensure representative

<figure><img src="image_14.png" /><figcaption>**Figure 8.** Dendrograms showing the Jaccard distance between samples of each part of P&B packaging, based on the occurrence of peaks in Raman spectra. The peaks considered are shown in Figure 7.</figcaption></figure>
contamination assessments. While practical con-
straints often limit the feasibility of grinding
entire packaging materials, this approach remains
the most reliable means of capturing the total
contamination burden. A clear understanding of
these heterogeneities supports the identification
of substances most likely to migrate into food,
whether due to their proximity to food contact
surfaces or their toxicological relevance. These
insights are critical for performing worst-case risk
evaluations and guiding the design of more pre-
cise and targeted analytical experiments. While
these findings highlight the need for comprehen-
sive sampling strategies to evaluate contamination
accurately, further studies with larger sample sizes
are necessary to confirm these observations and
to strengthen comparisons between virgin and
recycled fibers.

The findings presented here also highlight the complementary role of macroscopic and microscopic techniques in contamination analysis. Combining solvent extraction with advanced imaging methods such as confocal laser scanning microscopy (CLSM) and Raman microscopy enabled us to characterize contamination profiles at multiple scales. Raman microscopy, in particular, allowed for the rapid differentiation between recycled and virgin materials, regardless of the specific packaging part, demonstrating its potential for quality control applications. Moreover, extending CLSM with UV lasers opens new opportunities for detecting contaminants beyond ink-related substances, significantly broadening the analytical scope. The complementarity of mass spectrometry and chemical imaging tools offers a powerful tool for mapping contamination pathways, correlating the occurrence of substances, and streamlining hazard analyses by integrating chemical imaging with conventional analytical techniques.

In conclusion, this study provides a comprehen-
sive framework for addressing the challenges of
contamination assessment in recycled P&B materi-
als. Combining macroscopic and microscopic ana-
lytical approaches advances our understanding of
contamination variability, spatial distribution, and
potential risks. As recycled materials play an
increasingly central role in the circular economy,
this research lays the groundwork for future efforts

to develop safer, more sustainable food packaging
solutions while enhancing the accuracy of risk
assessments and hazard evaluations.

Acknowledgments

The chromatographic analysis was conducted in the French Pulp and Paper Research & Technical Center (CTP) laboratories and the French Laboratory of Metrology and Testing (LNE). We gratefully acknowledge their technical contributions and support.

Disclosure statement

The authors report that there are no competing interests to declare.

Funding

This project was supported by the French National Research Agency (ANR) under Grant ANR-20-CE21-0009.

ORCID

Lucas Biant http://orcid.org/0009-0001-4337-6310

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