In an article published on July 20, 2022, in the journal Food Chemistry, Janira Jaén from the University of Zaragoza, Spain, and co-authors present a simple, rapid, and reproducible analytical methodology to determine markers for mineral oil aromatic hydrocarbons (MOAH) contamination in food. Since MOAH identification is analytically challenging due to a lack of analysis protocols, mineral oil standards and validated samples, the scientists aimed to develop an improved method for MOAH analysis.
To meet their objective, Jaén and co-authors optimized and validated a solid-phase extraction (SPE) method and a gas chromatography-mass spectrometry (GC-MS) method for the separation and analysis of 16 chemical markers of MOAH, respectively. Subsequently, they applied the method to identify and quantify the markers in two mineral oil-based offset printing inks and to 19 cardboard food packaging samples. Cardboard samples were cut into small pieces and chemicals were extracted with an n-hexane/ethanol (1:1) mixture before being applied to chemical analysis.
Jaén et al. reported that their developed SPE-GC-MS method to determine MOAH contamination markers “is fast, has good sensitivity, precision, and linearity within the range of the studied concentrations with recovery values of the target analytes above 80%.” Applying the methodology to the inks demonstrated the presence of ten MOAH markers. All but one of the ten, were also detected in the cardboard food packaging samples. MOAH concentrations ranged between 2.28 and 8.59 µg/g in offset printing inks and between 0.10 and 0.33 mg/g in cardboards. The scientists assumed that the MOAH presence in carboards “correlates with the MOAH of mineral oil-based offset printing inks.”
Mineral oil hydrocarbons are complex chemical mixtures generally derived from crude oil. They can be present in food contact materials (FCMs) either intentionally as additives or unintentionally as contaminants from processing equipment or recycled paper fibers (FPF dossier). In March 2022, the European Commission recommended limits of MOAH contamination in food, which are e.g., 0.5 mg/kg for dry foods with a low fat/oil content (≤ 4% fat/oil) (FPF reported). In the EU, mineral oil hydrocarbons have been monitored in food packaging since 2017 (FPF reported).
In contrast to Jaén et al. who analyzed volatile compounds, Xue-Chao Song also from the University of Zaragoza, Spain, and co-authors developed a workflow to identify non-volatile compounds migrating from food packaging. In an article published on July 20, 2022, in the Journal of Agricultural and Food Chemistry, they present the workflow which is based on liquid chromatography−ion mobility−high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. Earlier in 2022, Song and colleagues published a CCS prediction tool to identify non-intentionally added substances (NIAS) in FCMs (FPF reported) and a database for leachable and extractable food contact chemicals (FCCs) to help in chemical identification during targeted and untargeted analysis (FPF reported).
In their current study, the scientists used machine learning approaches and experimental values to develop the prediction models. Generally, machine-learning-based prediction tools can help to identify compounds in complex matrices for which commercial standards are missing, as is the case for FCCs. After the model development, the researchers applied them to predict RT and CSS values of the FCCs listed in the Food Packaging Forum`s (FPF’s) Plastic Packaging Database (CPPdb) and the Food Contact Chemicals Database (FCCdb) (FPF reported and here) and transformed the two databases into screening libraries. These were integrated into their workflow to elucidate FCC structures. To test the developed workflow, Song et al. applied it to migrates from polyamide (PA) spatulas into 95% ethanol for untargeted identification of FCCs.
The researchers tentatively identified 44 compounds using the CPPdb and FCCdb libraries enhanced with the predicted RT and CSS values. In contrast, when screening against an in-house additive library, they identified 51 compounds, including additives such as antioxidants, slip agents, and plasticizers, as well as NIAS. PA6 and PA66 oligomers were most abundant in the migrates. Song et al. concluded that “using plastic-related or FCM-related databases [such as the CPPdb and FCCdb] in the identification process of FCCs can significantly reduce the number of false positives and improve the confidence of identifications” compared to databases such as PubChem. However, with new FCCs continuously emerging, the CPPdb and FCCdb libraries did not include all the substances detected in the PA migrate.
When using GC-MS, compounds are often identified by comparing measured mass spectra (i.e. unknown spectra) with commercially available mass spectral libraries (i.e., known spectra) such as NIST. One value that is applied to rank the quality of the hit is the matching factor which can be between 0 and 999 indicating how close a peak of a measured mass spectrum and a peak of a library spectra are matching. However, there is no generally accepted guideline stating which matching factor is considered high enough to designate a peak (i.e., a compound) as identified. Therefore, in an article published on July 15, 2022, in Microchemical Journal, Csaba Kirchkeszner and co-authors from Eötvös Loránd University, Budapest, Hungary, aimed “to evaluate the efficiency and the reliability of identifications based solely on GC-EI-QMS [gas chromatograph-electron impact-quadrupole mass spectrometry] measurements followed by NIST mass spectral library searches.”
Kirchkeszner and co-authors performed migration tests with 22 single-use and 32 reusable polypropylene (PP) food containers using isooctane as a food simulant and applying temperatures of 60 °C for 10 days. Using the GC-EU-QMS and NIST comparison, they tentatively identified 70 compounds including 24 n-alkanes, as well as NIAS. Using analytical standards they confirmed 31 non-alkanes but for four chemicals “the tentative identification proved to be wrong.” Six compounds were identified although they had a relatively low matching factor of below 700. The authors concluded that the results of GC-EI-QMS followed by evaluation with NIST “is a powerful tool in the nontarget analysis of compounds migrating from plastic food contact materials.” However, the results would also show that “both reliability and productivity can be increased with GC-EI-TOFMS measurements and consideration of linear retention indices.”
References
Jaén, J. et al. (2022). “Development of an analytical method for the determination of mineral oil aromatic hydrocarbons (MOAH) from printing inks in food packaging.” Food Chemistry. DOI: 10.1016/j.foodchem.2022.133745
Kirchkeszner, C. et al. (2022). “Role of gas chromatography–single quadrupole mass spectrometry in the identification of compounds migrating from polypropylene-based food contact plastics.” Microchemical Journal. DOI: 10.1016/j.microc.2022.107772
Song, X.-C. et al. (2022). “Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility−High-Resolution Mass Spectrometry and in Silico Prediction Tools.” Journal of Agricultural and Food Chemistry. DOI: 10.1021/acs.jafc.2c03615
