title: Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy.
abstract: The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.
authors.affiliation: College of Chemistry, Jilin University, 2519 Jiefang Avenue, Changchun 130021, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Dou
authors.forename: Hong
authors.initials: H
authors.lastname: Mi
authors.forename: Lingzhi
authors.initials: L
authors.lastname: Zhao
authors.forename: Yuqiu
authors.initials: Y
authors.lastname: Ren
authors.forename: Yulin
authors.initials: Y
authors.lastname: Ren
mesh_terms: D001241:Aspirin / Q000737:chemistry*; D002138:Calibration; D003198:Computer Simulation; D016571:Neural Networks, Computer; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013607:Tablets / Q000737:chemistry
chemical_list: D013607:Tablets; D001241:Aspirin