Sequential number-theoretic optimization (SNTO) method applied to chemical quantitative analysis

A sequential number‐theoretic optimization (SNTO) method recently developed in statistics was introduced as a global optimization procedure in constrained background bilinearization (CBBL) for the quantification of real two‐way bilinear data. SNTO searches for the global optimum among points uniform...

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Vydané v:Journal of chemometrics Ročník 11; číslo 3; s. 267 - 281
Hlavní autori: Zhang, Lin, Liang, Yi-Zeng, Yu, Ru-Qin, Fang, Kai-Tai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 01.05.1997
Wiley
Predmet:
Analytical chemistry
Chemistry
constrained background bilinearization
Exact sciences and technology
General, instrumentation
global optimization
sequential number-theoretic optimization method
variable step size simulated annealing
Background concentration
Statistical method
Chemical analysis
Sequential method
Optimization method
Simulated annealing
Bilinear transform
Algorithm
Global solution
Chemometrics
Quantitative analysis
ISSN:0886-9383, 1099-128X
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Abstract A sequential number‐theoretic optimization (SNTO) method recently developed in statistics was introduced as a global optimization procedure in constrained background bilinearization (CBBL) for the quantification of real two‐way bilinear data. SNTO searches for the global optimum among points uniformly scattered in the search space and convergence of the algorithm is quickened through sequential contraction of that space. Since the global optimization performance of SNTO is closely related to the number of points scattered, a new practical approach for selection of the number of points scattered in the original search space by trial tests is proposed in this paper in order to increase the possibility of locating the global optimum. The performance of SNTO has also been tested with mathematical models with multiple local optima. In comparison with another global optimization method, variable step size simulated annealing (VSGSA), SNTO achieved satisfactory results for both mathematical models and a real analytical system. The clarity and simplicity of the idea of SNTO together with its convenience for implementation make SNTO a promising tool in chemometrics. © 1997 John Wiley & Sons, Ltd.
AbstractList A sequential number‐theoretic optimization (SNTO) method recently developed in statistics was introduced as a global optimization procedure in constrained background bilinearization (CBBL) for the quantification of real two‐way bilinear data. SNTO searches for the global optimum among points uniformly scattered in the search space and convergence of the algorithm is quickened through sequential contraction of that space. Since the global optimization performance of SNTO is closely related to the number of points scattered, a new practical approach for selection of the number of points scattered in the original search space by trial tests is proposed in this paper in order to increase the possibility of locating the global optimum. The performance of SNTO has also been tested with mathematical models with multiple local optima. In comparison with another global optimization method, variable step size simulated annealing (VSGSA), SNTO achieved satisfactory results for both mathematical models and a real analytical system. The clarity and simplicity of the idea of SNTO together with its convenience for implementation make SNTO a promising tool in chemometrics. © 1997 John Wiley & Sons, Ltd.
Author Yu, Ru-Qin
Liang, Yi-Zeng
Zhang, Lin
Fang, Kai-Tai
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  organization: Department of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, People's Republic of China
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  fullname: Fang, Kai-Tai
  organization: Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong
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Issue 3
Keywords Background concentration
Statistical method
Chemical analysis
Sequential method
Optimization method
Simulated annealing
Bilinear transform
Algorithm
Global solution
Chemometrics
Quantitative analysis
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PublicationTitle Journal of chemometrics
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Snippet A sequential number‐theoretic optimization (SNTO) method recently developed in statistics was introduced as a global optimization procedure in constrained...
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SubjectTerms Analytical chemistry
Chemistry
constrained background bilinearization
Exact sciences and technology
General, instrumentation
global optimization
sequential number-theoretic optimization method
variable step size simulated annealing
Title Sequential number-theoretic optimization (SNTO) method applied to chemical quantitative analysis
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