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All calibration, and test samples data were obtained using UV\u2010Vis spectrophotometer. In this way, a modified PC\u2010ANN consisting of three layers of nodes was trained by combination of Bayesian\u2010Levenberg\u2010Marquardt as training rule. Sigmoid and liner transfer functions were used in the hidden and output layers respectively to facilitate nonlinear calibration. The model could accurately estimate the concentration of components with acceptable precision and accuracy, for mixtures. The PC\u2010ANN model exhibits a good ability for the simultaneous determination of the thiocyanate and salycilate in concentration range 0.5 \u00d7 10<jats:sup>\u22124<\/jats:sup>mol.l<jats:sup>\u22121<\/jats:sup>up to 5.0 \u00d7 10<jats:sup>\u22124<\/jats:sup>mol.l<jats:sup>\u22121<\/jats:sup>with Root Mean square error (2.22% and 2.20%, for thiocyanate and salycilate, respectively) and high correlation coefficients (R<jats:sup>2<\/jats:sup>= 0.998 or greater). Results obtained with modified trained PC\u2010ANN were compared with stepwise linear regression (SMLR) model. Validation of the two models shows a better ability in estimation of the modified PC\u2010ANN as compared with the SMLR model (MSRE given are 3.12%, 6.31%.).<\/jats:p>","DOI":"10.1002\/adic.200690068","type":"journal-article","created":{"date-parts":[[2006,11,6]],"date-time":"2006-11-06T11:31:44Z","timestamp":1162812704000},"page":"657-667","source":"Crossref","is-referenced-by-count":2,"title":["Simultaneous Determination of Thiocyanate and Salycilate by a Combined UV\u2010Spectrophotometric Detection Principal Component Artificial Neural Network"],"prefix":"10.1002","volume":"96","author":[{"given":"Hajir","family":"Karimi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehrorang","family":"Ghaedi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2006,11,6]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/0223-5234(96)83973-0"},{"key":"e_1_2_1_3_2","doi-asserted-by":"publisher","DOI":"10.1021\/ac980208r"},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(01)00131-9"},{"key":"e_1_2_1_5_2","doi-asserted-by":"publisher","DOI":"10.1021\/jm000226c"},{"key":"e_1_2_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0003-2670(02)00437-3"},{"key":"e_1_2_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2003.08.005"},{"key":"e_1_2_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0039-9140(02)00103-0"},{"key":"e_1_2_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2004.12.015"},{"key":"e_1_2_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2004.03.034"},{"key":"e_1_2_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2004.02.003"},{"key":"e_1_2_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2004.03.010"},{"key":"e_1_2_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chroma.2005.04.038"},{"key":"e_1_2_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2004.12.015"},{"key":"e_1_2_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0003-2670(00)01012-6"},{"volume-title":"Fundamentals of Neural Networks: Architectures, Algorithms and Applications","year":"1994","author":"Fausett L.","key":"e_1_2_1_16_2"},{"key":"e_1_2_1_17_2","doi-asserted-by":"publisher","DOI":"10.1039\/a805562i"},{"key":"e_1_2_1_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"e_1_2_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.329697"},{"key":"e_1_2_1_20_2","unstructured":"A. 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