Resampling for estimation of parameters uncertainty in genetic algorithm based model fitting

[Display omitted] •A resampling method was used for the calculation of parameters uncertainty in genetic algorithm based hard model fitting.•Number of eliminated samples affect the uncertainty of parameters from model fitting in complexometric spectrophotometric titration data.•In case of real and simulated 1H NMR, uncertainty in the estimated parameters from resampling of fitting is not proportional to the number of eliminated variables.•A novel and simple resampling method is proposed for non-parametric estimation of parameter uncertainty in model fitting of experimental and simulated dataset. Resampling methods draw samples from the observed data to draw certain conclusions about the population of interest. The study is the employment of resampling to the calculation of parameters uncertainty in hard model fitting of chemical and spectrochemical data, assisted by genetic algorithm. Four data sets are investigated including: a simulated two steps of complexometric spectrophotometric titration data, a real data from IgG purification by ion exchange chromatography, one 1H NMR spectrum from a real metabolomics data set, and a simulated metabolomics 1H NMR spectrum. In the first data, the logarithm of formation constants in the first and second steps were 4.500 and 7.200, respectively. The mean of estimated parameters and standard deviations using the proposed method were obtained as 4.504 ± 0.005 and 7.205 ± 0.017 by removing around half of the samples while for NGLM algorithm and Hessian matrix were 4.503 ± 0.005 and 7.203 ± 0.018. In the second data, desorption coefficient (kdes,0), equilibrium coefficient (Keq,0), and a constant parameter that describes ion exchange characteristics (β) were well estimated with root mean square error (RMSE) of 0.031 mg mL−1. In the third and last data sets, parameters including chemical shifts and intensity of each metabolite were well calculated with low relative error of 4.23 × 10−4. The estimated uncertainty of parameters were different. In some cases, the means of estimated parameters were dependent of the number of eliminated samples in resampling. Effect of number of variables in data and model deficiency on the results from resampling was investigated. Due to the complexity of the mentioned datasets, use of resampling and GA is useful and important strategy for acquiring parameter mean and uncertainty, when we are not able to determine the Hessian matrix.