title: [Determination of five component infrared spectra system with artificial neural network].
abstract: This article demonstrates the application of artificial neural network in multi-component analysis. Parameters were obtained after the BP network was trained with large amount of simulated data. Five organic toxins whose FTIR spectra are strongly overlapped were used to make the multi-component system. The relative standard deviation(RSD%), the percent standard error of prediction samples(SEP%) and the percent standard error of calibration samples(SEC%) were used for evaluating the ability of the neural network.
authors.affiliation: Laboratory of Advanced Spectroscopy, Nanjing University of Science and Technology, 210014 Nanjing.
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.forename: X
authors.initials: X
authors.lastname: Sun
authors.forename: J
authors.initials: J
authors.lastname: Wang
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D001554:Benzene / Q000032:analysis; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000032:analysis; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods; D020058:Styrene / Q000032:analysis*; D014992:Xylenes / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D009930:Organic Chemicals; D014992:Xylenes; D020058:Styrene; D001554:Benzene; C026114:2-xylene