English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ternary mixtures of nitroaniline isomers have been simultaneously determined in synthetic and real matrix by application of genetic algorithm principal component artificial neural network model. All effective factors on the sensitivity were optimized. Also, the linear dynamic range for determination of nitroaniline isomers was found. The simultaneous determination of nitroaniline mixtures by using spectrophotometric methods due to spectral interferences is a difficult problem. A genetic algorithm is a suitable method for selecting wavelength for principal component-artificial neural network (PC-ANN) calibration of mixtures with almost identical spectra without loss prediction capacity. The experimental calibration matrix was designed by measuring the absorbance over the range of 200 to 500 nm for 21 samples of 1.0 to 17.0, 1.0 to 15.0 and 1.0 to 18.0 μg/ml of m-nitroaniline, o-nitroaniline and p-nitroaniline, respectively. The root mean square error of prediction for m-nitroaniline, o-nitroaniline and p-nitroaniline were 0.7848, 0.2864 and 0.1851, respectively. The proposed method was successfully applied for the determination of m-nitroaniline, o-nitroaniline and p-nitroaniline in synthetic and water samples.       Key words: Nitroaniline isomers, genetic algorithm, principal component, artificial neural network.

african journal of pharmacy and pharmacology

Simultaneous spectrophotometric determination of nitroanilines using genetic-algorithm-based wavelength selection in principal component-artificial neural network