Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium, copper, and lead using an ISE array. (6th February 2014)
- Record Type:
- Journal Article
- Title:
- Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium, copper, and lead using an ISE array. (6th February 2014)
- Main Title:
- Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium, copper, and lead using an ISE array
- Authors:
- Wang, Liang
Yang, Die
Chen, Zuliang
Lesniewski, Peter J.
Naidu, Ravi - Abstract:
- <abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>The paper introduces a novel chemometric strategy based on independent component analysis (ICA) coupled with a back‐propagation neural network. In this approach, one of the most popular ICA methods, the fast fixed‐point algorithm for ICA (<italic>fast</italic>ICA), was implemented by the genetic algorithm (<italic>genetic</italic>ICA) to avoid the local maxima problem commonly observed with <italic>fast</italic>ICA. As a case study, an ion‐selective electrode (ISE) array, consisting of three working electrodes and one reference electrode, was used for the simultaneous determination of three heavy metals (cadmium, copper, and lead) in aqueous solutions, which are normally prone to severe interferences. The robustness and appropriateness of the approach were assessed using the average mean of relative error (MRE) of triplicated external validation. After configuration and optimization, the average MRE for Cu was <5%. For the determination of Cd and Pb, whose ISEs normally cannot tolerate Cu ions even at the microgram per liter levels, the MREs were 8%. This article demonstrated that this approach can be applied to the detection of heavy metal contamination in industrial wastewater with prediction accuracies comparable with other popular quantitative chemometric neural network methods. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>
- Is Part Of:
- Journal of chemometrics. Volume 28:Number 6(2014:Jun.)
- Journal:
- Journal of chemometrics
- Issue:
- Volume 28:Number 6(2014:Jun.)
- Issue Display:
- Volume 28, Issue 6 (2014)
- Year:
- 2014
- Volume:
- 28
- Issue:
- 6
- Issue Sort Value:
- 2014-0028-0006-0000
- Page Start:
- 491
- Page End:
- 498
- Publication Date:
- 2014-02-06
- Subjects:
- Chemistry -- Mathematics -- Periodicals
Chemistry -- Statistical methods -- Periodicals
542.85 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/cem.2599 ↗
- Languages:
- English
- ISSNs:
- 0886-9383
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4957.380000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4269.xml