Principal Component Analysis-Ranking as a Variable Selection Method for the Simultaneous Spectrophotometric Determination of Phenol, Resorcinol and Catechol in Real Samples
نویسندگان
چکیده
Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCrankingANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples. Keywords—Phenol, Resorcinol, Catechol, Principal componentranking Artificial Neural Network, Chemometrics.
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