PFAS are synthetic chemicals, part of a large family of fluorinated organic compounds which can exert a range of toxic effects, including a behaviour as 'endocrine disruptor chemicals' (EDCs) that can alter the endocrine system. PFAS can be divided into 'legacy PFAS' and 'emerging PFAS'. The first are fully fluorinated, have a polar terminal group and are subdivided into perfluorocarboxylic acids (PFCA), such as perfluorooctanoic acid (PFOA), and perfluorosulphonic acids (PFSA), such as perfluorooctanesulphonic acid (PFOS). Despite the ban under the Stockholm Convention, considering the wide use of these compounds in various fields of application and their high persistence in the environment, detectable concentrations can be found in matrices like water, food etc. In addition, industries continuously synthesise new fluorinated molecules. In particular, to replace long-chain perfluoroalkyl acids, ether organofluorinated substances (ether-PFAS) have been synthesised, including GEN-X (C6H4F11NO3) and ADONA (C7H2F12O4), with the aim of increasing the molecule's solubility and thus degradability by introducing the ether functional group. Indeed, with the development of toxicological studies, it has been shown that these synthesised 'emerging PFAS' also have a non-negligible toxicity towards humans and animals, so their detection, quantification and regulation will be necessary in the future. To develop innovative and improved methods aimed at the detection and quantification of per- and polyfluoroalkyl substances (PFAS) in water and food matrices mass spectrometry and liquid chromatography parameters were optimized by employing Design of Experiments (DoE). The selected responses to be optimized were chromatographic peak area (related to sensitivity) and resolution (chromatographic separation), while variables were the following: on the chromatographic side, flow, gradient ramp and column temperature; on the mass spectrometry side, capillary voltage, sheath gas flow and fragmentor voltage. Considering the number of variables, the Placket-Burman (PB) design was performed to identify the more influent variables, with the minimum number of experiments (16, including replicates). In order to reduce the computational effort, a Principal Component Analysis (PCA) was performed on the experimental matrix containing the peak area response. A high percentage of variance (96.95%) was explained by the first three principal components, with PC1 accounting for 65.32%. The loading plot highlighted correlations among the responses and thus, given the high variance explained by PC1, the scores on the first principal component were used as a unique response and one model was computed (instead of 20 models for the single analytes). From the model obtained, (with a R2ADJ =95.14%), it was possible to derive the significance of the studied variables. Flow and capillary voltage were significant with a negative sign, while sheath gas flow and fragmentor voltage showed a significant positive sign. The negative effect of the flow variable on the peak area response is plausibly justified considering the operating principle of ESI. On the other hand, the reason for the negative effect of capillary voltage could be attributed to a possible excessive fragmentation of the precursor ions prior to their entry into the mass spectrometer, thus reducing the final peak area. The positive effect of the sheath gas flow on the response may be due to either the easier generation of smaller droplets in the ionization process, or by improving the focalization of the ions into the spectrometer inlet. Finally, a higher fragmentor voltage positively influence peak area, thanks to a more efficient acceleration of the ions toward the analyzer. In the near future, a response surface DoE will set, considering the significant variables from PB results in order to optimize sensitivity and resolution. By working in this way, it will be possible to apply the method optimised for analysis to both water and food samples, with the appropriate pre-treatment technique.
Optimization of LC-Q-TOF Mass Spectrometry and Chromatographic Parameters for the development of an innovative method for the determination of PFAS in different matrices
Bona Daniel;Di Carro Marina;Benedetti Barbara;Magi Emanuele
2025-01-01
Abstract
PFAS are synthetic chemicals, part of a large family of fluorinated organic compounds which can exert a range of toxic effects, including a behaviour as 'endocrine disruptor chemicals' (EDCs) that can alter the endocrine system. PFAS can be divided into 'legacy PFAS' and 'emerging PFAS'. The first are fully fluorinated, have a polar terminal group and are subdivided into perfluorocarboxylic acids (PFCA), such as perfluorooctanoic acid (PFOA), and perfluorosulphonic acids (PFSA), such as perfluorooctanesulphonic acid (PFOS). Despite the ban under the Stockholm Convention, considering the wide use of these compounds in various fields of application and their high persistence in the environment, detectable concentrations can be found in matrices like water, food etc. In addition, industries continuously synthesise new fluorinated molecules. In particular, to replace long-chain perfluoroalkyl acids, ether organofluorinated substances (ether-PFAS) have been synthesised, including GEN-X (C6H4F11NO3) and ADONA (C7H2F12O4), with the aim of increasing the molecule's solubility and thus degradability by introducing the ether functional group. Indeed, with the development of toxicological studies, it has been shown that these synthesised 'emerging PFAS' also have a non-negligible toxicity towards humans and animals, so their detection, quantification and regulation will be necessary in the future. To develop innovative and improved methods aimed at the detection and quantification of per- and polyfluoroalkyl substances (PFAS) in water and food matrices mass spectrometry and liquid chromatography parameters were optimized by employing Design of Experiments (DoE). The selected responses to be optimized were chromatographic peak area (related to sensitivity) and resolution (chromatographic separation), while variables were the following: on the chromatographic side, flow, gradient ramp and column temperature; on the mass spectrometry side, capillary voltage, sheath gas flow and fragmentor voltage. Considering the number of variables, the Placket-Burman (PB) design was performed to identify the more influent variables, with the minimum number of experiments (16, including replicates). In order to reduce the computational effort, a Principal Component Analysis (PCA) was performed on the experimental matrix containing the peak area response. A high percentage of variance (96.95%) was explained by the first three principal components, with PC1 accounting for 65.32%. The loading plot highlighted correlations among the responses and thus, given the high variance explained by PC1, the scores on the first principal component were used as a unique response and one model was computed (instead of 20 models for the single analytes). From the model obtained, (with a R2ADJ =95.14%), it was possible to derive the significance of the studied variables. Flow and capillary voltage were significant with a negative sign, while sheath gas flow and fragmentor voltage showed a significant positive sign. The negative effect of the flow variable on the peak area response is plausibly justified considering the operating principle of ESI. On the other hand, the reason for the negative effect of capillary voltage could be attributed to a possible excessive fragmentation of the precursor ions prior to their entry into the mass spectrometer, thus reducing the final peak area. The positive effect of the sheath gas flow on the response may be due to either the easier generation of smaller droplets in the ionization process, or by improving the focalization of the ions into the spectrometer inlet. Finally, a higher fragmentor voltage positively influence peak area, thanks to a more efficient acceleration of the ions toward the analyzer. In the near future, a response surface DoE will set, considering the significant variables from PB results in order to optimize sensitivity and resolution. By working in this way, it will be possible to apply the method optimised for analysis to both water and food samples, with the appropriate pre-treatment technique.
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