RESUMO
In order to protect human health and the environment, highly efficient, low-cost, labor-saving, and green analysis of toxic chemicals are urgently required. To achieve this objective, we have developed a novel database-based automated identification and quantification system (AIQS) using LC-QTOF-MS. Since the AIQS uses retention times (RTs), exact MS and MS-MS spectra, and calibration curves of 484 chemicals registered in the database instead of the use of standards, the targets can be determined with low-cost in a short time. The AIQS uses Sequential Window Acquisition of All Theoretical Fragment-ion Spectra as an acquisition method by which we can obtain accurate MS and MS-MS spectra of all detectable substances in a sample with minimal interference from co-eluted peaks. Identification is certainly done using RTs, mass error, ion ratios (a precursor to two product ions), and accurate MS and MS-MS spectra. Consequently, the chance of misidentification is very low even in dirty samples. To examine the accuracy of the AIQS, two collaborative tests were conducted. The first test used 208 pesticide standards at two concentrations (10 and 100 ng mL-1) using 7 instruments, and showed that average trueness was 106 and 95.2%, respectively, with relative standard deviations of 90% of the test compounds below 30%. The second collaborative study involved 5 laboratories carrying out recovery tests on 200 pesticides using 10 river waters. The average recovery was 71.6%; this was 15% lower than that using purified water probably due to the matrix effects. The average relative standard deviation was 30% worse than that of measurement of the standards. Both the recovery and reproducibility, however, satisfied the criteria of Analytical Method Validity Guidelines, Ministry of Health, Labour and Welfare, Japan. Instrument detection limits of 96% of the registered compounds are below 10 pg. The AIQS allows for easy addition of new substances and retrospective analysis after their addition. The results applied to actual samples showed that the AIQS has sufficient identification and quantification performance as a target screening method for a large number of substances in environmental samples.
