Enhancing targeted LC-Q-TOF analysis of emerging contaminants through multivariate mass spectrometry optimization

Emerging contaminants (ECs) occur at ultra-trace levels in complex environmental matrices, requiring analytical methods with high sensitivity and selectivity. Although LC-Q-TOF MS is widely applied for suspect and non-target screening, thanks to its high-resolution capabilities, its use for targeted quantitative analysis is often considered limited, and systematic optimization strategies aimed at improving its performance remain poorly explored. In this study, a multivariate experimental design approach was applied to optimize the ion-source and instrumental parameters of an LC-Q-TOF platform for the targeted analysis of 43 chemically diverse ECs. Sequential screening and response-surface experimental designs were employed to maximize ionization efficiency and analytical sensitivity. The optimized method was validated in terms of linearity, precision, limits of detection and quantification, and subsequently applied to wastewater samples. The optimized Q-TOF workflow showed satisfactory analytical performance, with good linearity (R² ≥ 0.995 for most analytes), high precision, and enhanced selectivity through accurate-mass acquisition. Application to real wastewater samples confirmed its suitability for the quantification of ECs in complex matrices. The analytical performance achieved after optimization was finally compared with that of an independently optimized LC-QqQ method. The optimized Q-TOF workflow showed lower RSD values for approximately half of the investigated compounds, together with sensitivity comparable to or better than the QqQ for approximately 15 analytes. This study thus demonstrated that, through careful optimization, LC-Q-TOF represents a powerful platform to develop methods for combined targeted quantification and non-target screening for environmental applications.