Quantitative structure-retention relationships for mycotoxins and fungal metabolites in LC-MS/MS

Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling

A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.

Multivariate image analysis-quantitative structure-retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities.

Suspect screening of plastic-related chemicals in northern pike (Esox lucius) from the St. Lawrence River, Canada

Environmental contaminant monitoring traditionally relies on targeted analysis, and very few tools are currently available to monitor "unexpected" or "unknown" compounds. In the present study, a non-targeted workflow (suspect screening) was developed to investigate plastic-related chemicals and other environmental contaminants in a top predator freshwater fish species, the northern pike, from the St. Lawrence River, Canada. Samples were extracted using sonication-assisted liquid extraction and analyzed by high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (HPLC-QTOF-MS). Ten bisphenol compounds were used to test the analytical performances of the method, and satisfactory results were obtained in terms of instrumental linearity (r² > 0.97), recoveries, (86.53-119.32%), inter-day precision and method detection limits. The non-targeted workflow data processing parameters were studied, and the peak height filters (peak filtering step) were found to influence significantly the capacity to detect and identify trace chemicals in pike muscle extracts. None of the ten bisphenol analogues were detected in pike extracts suggesting the absence of accumulation for these chemicals in pike muscle. However, the non-targeted workflow enabled the identification of diethyl phthalate (DEP) and perfluorooctanesulfonic acid (PFOS) in pike extracts. This approach thus can be also applied to various contaminants in other biological matrices and environmental samples.

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (tR) and mass spectral comparisons. Chromatographic tR prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for tR prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound tR dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85% of all compounds to within 2min of their measured tR for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95% prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.