{'fraction_non_alphanumeric': 0.027736131934032984, 'fraction_numerical': 0.011244377811094454, 'mean_word_length': 5.608910891089109, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimization of the separation of a group of triazine herbicides by micellar capillary electrophoresis using experimental design and artificial neural networks

The micellar electrokinetic chromatography separation of a group of triazine compounds was optimized using a combination of experimental design (ED) and artificial neural network (ANN). Different variables affecting separation were selected and used as input in the ANN. A chromatographic exponential function (CEF) combining resolution and separation time was used as output to obtain optimal separation conditions. An optimized buffer (19.3 mM sodium borate, 15.4 mM disodium hydrogen phosphate, 28.4 mM SDS, pH 9.45, and 7.5% 1‐propanol) provides the best separation with regard to resolution and separation time. Besides, an analysis of variance (ANOVA) approach of the MEKC separation, using the same variables, was developed, and the best capability of the combination of ED‐ANN for the optimization of the analytical methodology was demonstrated by comparing the results obtained from both approaches. In order to validate the proposed method, the different analytical parameters as repeatability and day‐to‐day precision were calculated. Finally, the optimized method was applied to the determination of these compounds in spiked and nonspiked ground water samples.