Accuracy improvement on quantitative analysis of the total iron content in branded iron ores by laser-induced breakdown spectroscopy combined with the double back propagation artificial neural network
Piao Su, Shu Liu, Hong Ki Min, Yarui An, Chenglin Yan, Chen Li
Abstract
) were reduced from 0.456 wt% and 0.584% to 0.177 wt% and 0.228% respectively. Moreover, the prediction error based on the DBP-ANN model was within the error range (<0.275 wt%) accepted by the traditional chemical analysis method GB/T 6730.5-2009. Meanwhile, the established DBP-ANN method was also compared with the common multivariate method, and it showed better analytical performance. The results showed that LIBS combined with DBP-ANN has the potential to achieve rapid and accurate analysis of the TFe content of branded iron ores.
Topics & Concepts
Laser-induced breakdown spectroscopyUnivariateIron oreArtificial neural networkCorrelation coefficientMultivariate statisticsMean squared errorBackpropagationContent (measure theory)Partial least squares regressionChemistryCast ironSpectroscopyAnalytical Chemistry (journal)MetallurgyMathematicsMaterials scienceArtificial intelligenceStatisticsChromatographyComputer scienceMathematical analysisPhysicsQuantum mechanicsLaser-induced spectroscopy and plasmaCultural Heritage Materials AnalysisAnalytical chemistry methods development