Open Access Article
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Correction: 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
ab,
Shu
Liu
*b,
Hong
Min
b,
Yarui
An
*a,
Chenglin
Yan
b and
Chen
Li
b
aInstitute of Bismuth Science, University of Shanghai for Science and Technology, Shanghai 200093, China. E-mail: anyarui@usst.edu.cn
bTechnical Center for Industrial Product and Raw Material Inspection and Testing of Shanghai Customs, Shanghai 200135, China. E-mail: liu_shu@customs.gov.cn
First published on 2nd March 2022
Abstract
Correction for ‘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’ by Piao Su et al., Anal. Methods, 2022, 14, 427–437, DOI: 10.1039/D1AY01881G.
The authors would like to call the reader’s attention to the fact that, unfortunately, there were two errors regarding Fig. 9 and the graphical abstract. The colors for DBP-ANN and BP-ANN in Fig. 9 and in the graphical abstract were assigned in reverse and should be exchanged. The corrected versions of Fig. 9 and the graphical abstract are shown below. These corrections have no effect on any other results published in the article and do not affect the discussion or any of the published conclusions.
 | ||
| Fig. 9 Prediction results of TFe by DBP-ANN and BP-ANN models. | ||
The corrected graphical abstract.
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
| This journal is © The Royal Society of Chemistry 2022 |