Sample-independent approach to normalize two-dimensional data for orthogonality evaluation using whole separation space scaling

Systematic approach to online comprehensive 2D-LC method development for organic micropollutant profiling in wastewater

The increasing global contamination of freshwater systems with organic micropollutants (OMPs) is an important environmental problem. OMPs are frequently detected in the effluents of wastewater treatment plants (WWTPs), due to the inability of WWTPs to effectively remove them, and are subsequently discharged into surface waters, severely reducing water quality. The characterization of these complex wastewater samples is challenging, due to the large variety in physicochemical properties of OMPs and the presence of various matrix compounds, such as inorganic salts, humic acids and microorganisms. An emerging and promising technology to tackle this challenge is comprehensive two-dimensional liquid chromatography (LC x LC), combining two orthogonal separation modes to drastically enhance the separation power. However, the method development of LC x LC is complicated, currently confining its application mainly to academic research. It is difficult to predict which combinations will result in an increased peak capacity for a specific sample, and there is no consensus on how to best describe orthogonality. Furthermore, no single metric can fully assess all aspects of the quality of a 2D-LC separation. This study presents a systematic approach to evaluating the orthogonality of different separation modes for a given sample, more specifically for OMP profiling in wastewater, with less bias related to the sample and the user. To achieve this, an orthogonality score is defined, based on several orthogonality metrics commonly applied in 2D-LC studies. To automate the calculation of the orthogonality score, the mathematical algorithms of each metric as well as all other calculations are incorporated in a Python-based tool. Based on their orthogonality score and predicted peak capacity, LC x LC conditions are selected and then further optimized and applied to the analysis of real WWTP effluent samples. It is demonstrated that the optimized sub-hour RPLC x RPLCHRMS method achieves a peak capacity of 1887, emphasizing its potential for practical applications in environmental analysis.

On-line two-dimensional liquid chromatography hyphenated to mass spectrometry and ion mobility-mass spectrometry for the separation of carbohydrates from lignocellulosic biomass

Lignocellulosic biomass is a promising resource of renewable energy. Its transformation to ethanol requires efficient pretreatment leading to complex liquid mixtures made of hundreds of oxygenated analytes. A large part of the released compounds belong to the carbohydrates family. To overcome the complexity of such samples, a comprehensive on-line two-dimensional reversed-phase liquid chromatography hyphenated to high-resolution mass spectrometry (RPLC × RPLC-HRMS) was dedicated to the separation of carbohydrates and more specifically oligomers coming from pretreated lignocellulosic biomass. The first part of this study consisted in the optimization of such hyphenation (i.e. selection of stationary phases, mobile phases, sampling time, etc.). Then, the analytical method was applied to an industrial aqueous biomass product coming from the sulfuric acid-based pretreatment of a wheat straw. Around 70 well-resolved chromatographic peaks corresponding to oligomers were obtained. Occupation of the separation space between each chromatographic dimension was estimated to 75%. In the last part of this study, the interest of ion mobility-mass spectrometry in addition to RPLC × RPLC was discussed. Some examples highlighted the additional separation that can bring ion mobility to RPLC × RPLC-IMS-HRMS method. Using this four-dimensional hyphenation method, each analyte was described by two retention times, the collisional cross section and the molecular formula allowing to reach a level of detail never seen for biomass sample compositions.