| Response surface methodology(RSM)combined of statistics and mathematics,which is suitable for the target optimization affected by multiple variables.It is widely used in the optimization of chemical processes and product design.RSM can analyze the main effects of variables by fitting a first or second-order polynomial to a small area response surface model and drawing the contour plot and response surface.Genetic algorithm(GA)is an evolutionary algorithm simulating evolution in nature,which is the most popular algorithm to solve multi-objective optimization problems.Chlorinated paraffins(CPs)are persistent organic pollutants which was found in air,soil and sediment.But it is difficult to analyze them due to the complexity structure of CPs.In this study,RSM was used to optimize the parameters of large volume injection,and the response model was established.The second-order fitting equation for total response of the analytes and injection parameters was obtained,then GA solved the equation to get the optimal parameter combination.The optimized parameters were applied to the analysis of CPs in air samples using programmed temperature vaporization-large volume injection-gas chromatographic-negative chemical ionization-time of flight high-resolution mass spectrometry(PTV-LVI-GC-NCI-TOF-MS).The accuracy and precision of method had been validated.The method increased the sensitivity and decrease of the limit of detection,which was applied to determination of trace CPs in environmental samples.(1)In this article,fractional factorial design and central composite design were used to optimize large volume injection parameters.The five parameters of PTV-LVI(vent time,vent pressure,purge time,vent flow,and initial temperature of injection port)and the total response of six CPs standards were collected.We established the corresponding second-order response surface model based on the obtained data.The experiment designed by Minitab 18software analyzes the data through variance analysis and significance test,checks the model’s accuracy,and explores the influence between the injection parameters and the target response.(2)In the next part,the second-order model of response surface fitting was solved by the Geatpy evolutionary algorithm toolkit,and the parameters of the genetic algorithm were evaluated by the function value and time used of the algorithm.Finally,the tournament selection,unimodal normal distribution crossover,two-point exchange mutation operator of the chromosome were used to obtain the value of the optimal injection parameters.Moreover,the response values corresponding to the injection parameters were verified,and the optimal parameters were obtained as follows,vent time:0.108 min;vent pressure:4.34 psi;purge time:4.24 min;vent flow rate:80 m L/min;the initial temperature of the injection port:60℃.(3)In the last part,the optimal injection parameters were applied to analysis of short-chain chlorinated paraffins(SCCPs)and medium-chain chlorinated paraffins(MCCPs)in air samples.The detection limits with LVI of the six CPs standard were in the range of 2-3 ng/m L,which were significantly lower than the detection limits without LVI(10-28 ng/m L).The spike recoveries for different concentrations of CPs to blank and actual samples were in the range of 87%-136%.The method detection limits were in the range of 2-6 ng/m L,and the linear performance of this method was relatively good(R~2>0.98).The sampling at the Beijing Urban Ecosystem Research Station,China.The concentrations of total SCCPs and total MCCPs in the particle phase samples were 3.11-5.81 ng/m~3and 4.71-14.2 ng/m~3,respectively.The concentrations of total SCCPs and total MCCPs in the gas phase samples were between 6.08-14.0 ng/m~3and 0.88-6.30 ng/m~3,respectively. |
