Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Application of Doolittle Algorithm as a Multivariate Calibration Method for Infrared Spectrometric Determination of Some Ingredients in Detergent Washing Powder

Application of Doolittle Algorithm as a Multivariate Calibration Method for Infrared... Abstract The basic principle of Doolittle Multivariate Calibration Algorithm (DMCA) was investigated, and used for simultaneous quantitative determination of STPP, SS and SC in commercial washing powders with serious overlapping. The root mean square error of prediction (RMSEP) for DMCA is 0.084 which confnms the improvement in accuracy, in comparison with K-Matrix (RMSEP = 0.118). It was demonstrated that DDA can avoid matrix inverting, reduce the orders of matrices and needs a little time for analysis. So it has bright prospects in chemometrics and it is feasible that the Doolittle Algorithm could be applied to the practical determinations in real samples with spectral overlapping. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Analytical Chemistry Letters Taylor & Francis

Application of Doolittle Algorithm as a Multivariate Calibration Method for Infrared Spectrometric Determination of Some Ingredients in Detergent Washing Powder

Download PDF
5 pages

Application of Doolittle Algorithm as a Multivariate Calibration Method for Infrared Spectrometric Determination of Some Ingredients in Detergent Washing Powder

Abstract

Abstract The basic principle of Doolittle Multivariate Calibration Algorithm (DMCA) was investigated, and used for simultaneous quantitative determination of STPP, SS and SC in commercial washing powders with serious overlapping. The root mean square error of prediction (RMSEP) for DMCA is 0.084 which confnms the improvement in accuracy, in comparison with K-Matrix (RMSEP = 0.118). It was demonstrated that DDA can avoid matrix inverting, reduce the orders of matrices and needs...
Loading next page...
 
/lp/taylor-francis/application-of-doolittle-algorithm-as-a-multivariate-calibration-Pvkj6Dg0Bq
Publisher
Taylor & Francis
Copyright
Copyright Har Krishan Bhalla & Sons
ISSN
2230-7532
eISSN
2229-7928
DOI
10.1080/22297928.2011.10648221
Publisher site
See Article on Publisher Site

Abstract

Abstract The basic principle of Doolittle Multivariate Calibration Algorithm (DMCA) was investigated, and used for simultaneous quantitative determination of STPP, SS and SC in commercial washing powders with serious overlapping. The root mean square error of prediction (RMSEP) for DMCA is 0.084 which confnms the improvement in accuracy, in comparison with K-Matrix (RMSEP = 0.118). It was demonstrated that DDA can avoid matrix inverting, reduce the orders of matrices and needs a little time for analysis. So it has bright prospects in chemometrics and it is feasible that the Doolittle Algorithm could be applied to the practical determinations in real samples with spectral overlapping.

Journal

Analytical Chemistry Letters – Taylor & Francis

Published: Jan 1, 2011

Keywords: detergent powder; infrared spectroscopy; chemometrics; Doolittle algorithm

There are no references for this article.