Universal Route to Polycyclic Aromatic Hydrocarbon Analysis in Foodstuff: Two-Dimensional Heart-Cut Liquid Chromatography–Gas Chromatography–Mass Spectrometry

Analysis of polycyclic aromatic hydrocarbons (PAHs) in complex foodstuff is associated with complicated and work-intensive sample preparation. Chromatographic interference has to be faced in many situations. The scope of the current work was the development of a highly efficient two-dimensional heart-cut LC-LC-GC-MS method. Detection was performed with a time-of-flight mass spectrometer (TOF-MS) to allow for a comprehensive evaluation of the obtained data in terms of cleanup efficiency. Additionally, routine detection was performed with single quadrupole MS. An easy and quick generic sample preparation protocol was realized as a first step. During method development, focus was given to optimizing HPLC cleanup for complex foodstuff. Silica-, polymeric-, and carbon-based HPLC phases were tested. Coupling of silica gel to π-electron acceptor modified silica gel showed the best cleanup properties. A four rotary valve configuration allowed the usage of a single binary HPLC pump. Screening of several fatty and nonfatty food matrices showed the absence of unwanted matrix compounds in the cleaned-up PAH fraction down to the low picogram range using TOF-MS. Limits of quantitation (LOQ) were below 0.1 μg/kg for all EU priority PAHs. Recovery rates ranged from 82 to 111%. Validation data fully complied with EU Regulation 836/2011. Sample preparation was possible in 20 min. Interlacing of HPLC and GC allowed an average method runtime of 40 min per sample

Parameterization Models for Pesticide Exposure via Crop Consumption

An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop harvest, degradation half-lives in crops and on crop surfaces, overall residence times in soil, and substance molecular weight. Partition coefficients also play an important role for fruit trees and tomato (Kow), potato (Koc), and lettuce (Kaw, Kow). Focusing on these parameters, we develop crop-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework’s physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results correspond well with results from the complex framework for 1540 substance-crop combinations with total deviations between a factor 4 (potato) and a factor 66 (lettuce). Predicted residues also correspond well with experimental data previously used to evaluate the complex framework. Pesticide mass in harvest can finally be combined with reduction factors accounting for food processing to estimate human exposure from crop consumption. All parametric models can be easily implemented into existing assessment frameworks