Introduction. To assess the impact of the toxicants vinyl chloride (VC) and 1,2-dichloroethane (DCE) on humans, personalized biomonitoring of thiodiacetic acid (TDAA) is of most significant importance. Determination of TDAA in urine was carried out using the method of gas chromatography-mass spectrometry GC-MS. Materials and methods. Sample preparation consists of the analyte’s esterification in a biological matrix with methyl alcohol (with 10% boron trifluoride), extraction of the derivative by liquid extraction with ethyl acetate. We used an Agilent 7890A gas chromatograph with an HP-5MS capillary column and a mass-selective detector. TDAA was identified in the form of a dimethyl ester on a mass chromatogram according to the retention time and the ratio of the intensities of the registered ions. Results. To determine the rational parameters of the sample preparation process for the determination of TDAA in urine studies were carried out under the method of planning the experiment, which makes it possible to obtain the most accurate mathematical description of the processes. Optimization of the conditions for esterification of TDAA was carried out using mathematical planning, varying the temperature, process time, and the nature of the catalyst (BF3 or H2SO4). The scheduling matrix included eight experiments; the degree of conversion of TDAA served as an optimization parameter. Interpretation of the model showed that temperature makes more contribution to the formation of the degree of conversion than the processing time. The nature of the catalyst does not affect the degree of conversion. Conclusion. A mathematical model developed for optimizing the conditions for sample preparation of the biomarker of exposure to VC (TDAA) in urine, showed the contribution of three factors (reaction temperature, reaction time, catalyst nature) to the conversion rate, of which the reaction temperature makes the greatest contribution to the choice of optimal esterification conditions. The nature of the catalyst (BF3 or H2SO4) does not affect the conversion rate.
Automating LC–MS/MS mass chromatogram quantification: Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods.