Computational protocol: Methodology for non target screening of sewage sludge using comprehensive two dimensional gas chromatography coupled to high resolution mass spectrometry

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Protocol publication

[…] The limit of quantification (LOQ) and LOD were derived from method validation blank values (see section “”) where possible. In all other cases, they were determined using the standard deviation of the triplicate injections of the lowest point of the standard curve. The formulae for the LOD and LOQ are as follows:LOD=3.3×σS LOQ=10×σS where σ is the standard deviation of the response (blank or standard dilution close to the LOQ, respectively) and S being the slope of the standard curve.The peak finding and library search for the non-target application were carried out using the ChromaTOF software (version 1.90.60) from Leco Corporation in connection with the NIST MS library (2011). For a peak to be accepted, the following criteria had to be fulfilled: (i) the area of the peak in the sample had to be at least three times higher than the area of the same peak in the blank and (ii) the peak had to be found in at least two out of three sample replicates. The stepwise procedure of identifying and classifying peaks in sludge chromatograms was as follows:Peaks occurring in the blank (in high enough concentrations) as well as the sample were removed (as defined above).Features that occurred only in one of the triplicates were removed.Peaks were classified into groups according to the rules in Table in combination with the regions defined in Table and Fig. (see “” section). All classification regions followed the upwards trend (increasing second dimension retention time) caused through the isothermal (starting at 41 min) in the end of the oven temperature program.The remaining peaks were identified using the NIST library (similarity and probability), fragmentation patterns, and, where possible, retention indexes. Only hits with a similarity match greater than 500 were displayed. To reduce the amount of peaks to look at, only peaks that had a first hit with either a high similarity (>750) or a high probability (>7000) were considered. For compounds where no retention index was found, a simple linear regression model using retention times of standard analytes and their boiling points was used for giving an approximate retention time. Retention times were used for exclusion purposes rather than confirmation.Chlorine and bromine filters were applied. Firstly, ChromaTOF’s built-in chlorine and bromine filters were used. In addition, our own filter criteria were applied (Table ).The mass defect was used to identify chlorinated and brominated compounds using 81Br–79Br and 37Cl–35Cl (nominal isotope spacing divided by exact isotope spacing), respectively, as reference for normalization. The mass spectrum was summed over a range of 10 min each. Since the raw chromatograms/spectra were used and peaks were identified manually, peaks that were missed in the peak picking process during the data processing could also be identified. Peaks occurring in the blank (in high enough concentrations) as well as the sample were removed (as defined above).Features that occurred only in one of the triplicates were removed.Peaks were classified into groups according to the rules in Table in combination with the regions defined in Table and Fig. (see “” section). All classification regions followed the upwards trend (increasing second dimension retention time) caused through the isothermal (starting at 41 min) in the end of the oven temperature program.The remaining peaks were identified using the NIST library (similarity and probability), fragmentation patterns, and, where possible, retention indexes. Only hits with a similarity match greater than 500 were displayed. To reduce the amount of peaks to look at, only peaks that had a first hit with either a high similarity (>750) or a high probability (>7000) were considered. For compounds where no retention index was found, a simple linear regression model using retention times of standard analytes and their boiling points was used for giving an approximate retention time. Retention times were used for exclusion purposes rather than confirmation.Chlorine and bromine filters were applied. Firstly, ChromaTOF’s built-in chlorine and bromine filters were used. In addition, our own filter criteria were applied (Table ).The mass defect was used to identify chlorinated and brominated compounds using 81Br–79Br and 37Cl–35Cl (nominal isotope spacing divided by exact isotope spacing), respectively, as reference for normalization. The mass spectrum was summed over a range of 10 min each. Since the raw chromatograms/spectra were used and peaks were identified manually, peaks that were missed in the peak picking process during the data processing could also be identified.The in silico fragmentation tool MetFrag [] was used to identify unknown chlorinated compounds (steps 5 and 6 above). MetFrag uses compound structures stored in databases (e.g., PubChem or Chemspider) to predict the fragmentation of small molecules. Those fragmentation patterns are then compared to a spectrum that is inserted by the user. The similarity of the spectrum inserted by the user to the predicted fragmentation is then given. Originally, MetFrag was developed for tandem MS data but, it can also be applied for EI MS data.Here, the internet database Chemspider was used as a source for candidate structures matching the neutral mass of the highest m/z present in the spectrum, with a 5 ppm mass tolerance. The electron ionization spectra for the unknown compounds were exported from ChromaTOF and compared to the fragments generated by MetFrag from [M+] using a 5 ppm or 0.001 mDa tolerance. Only compounds including (at least) carbon, hydrogen, and chlorine were considered. The Chemspider data source count and reference count were taken into account in scoring the results. Hereby, the spectral match was weighted with 100%, while the data source count and reference count were weighted with 50% each. […]

Pipeline specifications

Software tools ChromaTOF, NIST MS Search, MetFrag
Databases ChemSpider
Application MS-based untargeted metabolomics
Chemicals Silica Gel