Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry

Kalivas, John H.; Roberts, Nancy.; Sutter, Jon M.

doi:10.1021/ac00193a006

Cited by 206 publications

(108 citation statements)

References 28 publications

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“…The crucial point for building the best calibration models for the determination of sample components is to select informative NIR regions where one can obtain an optimized calibration model for them. Theoretical 31,32 and experimental 33,34 evidence have indicated that wavelength selection can significantly improve the performance of full-spectrum calibration techniques, such as PLS, and various wavelength or wavenumber selection methods have been proposed and used. [35][36][37][38][39][40][41] We developed several new chemometrics algorithms for wavelength interval selection and sample selection in multicomponent spectral analysis.…”

Section: ·2 Chemometricsmentioning

confidence: 99%

Near-Infrared Spectroscopy—Its Versatility in Analytical Chemistry

2012

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Section: ·2 Chemometricsmentioning

confidence: 99%

Near-Infrared Spectroscopy—Its Versatility in Analytical Chemistry

2012

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“…Typical objective criteria include the spectral signal-to-noise ratio, the condition number or determinant of the calibration matrix, Akaike information criterion (AIC) and Mallows Cp statistics, as well as some estimates of the mean squared error in prediction (MSEP) [2]. The routine search algorithms comprise the stepwise selection [8], simplex optimisation [9], branch and bound combinatorial search [10], simulated annealing [9], genetic algorithms (GAs) [11], successive projections algorithm [12] and moving windows selection strategy [13]. Very recently, we proposed an experimental design-neural network procedure to select the most informative spectral regions [14].…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimisation: a powerful tool for wavelength selection

Shamsipur

Zare-Shahabadi

Hemmateenejad

et al. 2006

Journal of Chemometrics

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Ant colony optimisation (ACO) is a meta-heuristic algorithm, which is derived from the observation of real ants. Real ant colonies are distributed system that, in spite of the simplicity of their individuals, present a highly structured social organisation and can accomplish complex tasks. They always find a short path between the nest and a food source. ACO is based on local message exchange via the deposition of pheromone trails. It is in fact a population-based approach using positive feedback as well as greedy search. Wavelength selection is a strategy used for improving the quality of calibration methods. As a first report, this work indicated that the ACO possesses a great ability to find best subsets of wavelengths, at a short period of time with small PRESS values, via accumulation of information in the form of pheromone trails deposited on each wavelength. Theory of ACO is described and, to carry out the wavelength selection, a fitness function is defined. The ACO parameters are configured with a 3-levels full factorial design. The high ability of ACO in wavelength selection process was demonstrated by examining four different NIR and UV/Vis data sets via various ACO algorithms, including ACO-ILS, ACO-CLS and ACO-PLS. The results showed that, with the same fitness function, ACO-ILS algorithm suffers from some overfitting problem. This problem was overcome by constraining the algorithm to choose limited number of wavelengths, the corresponding algorithm called as ACO-ILS(limited). The results obtained by these algorithms clearly revealed the improved predictive ability of ACO in wavelength selection over the existing full-spectrum models.

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“…Several approaches for overcoming this have been proposed to select optimal sets of variables for multivariate calibration, such as the "branch and bound" algorithm commonly applied in combinatorial optimization, 22 the least condition number of the calibration matrix, 23 generalized simulated annealing, 24 incertitude modeling, 25 genetic algorithms, [26][27][28][29] artificial noise introduction in PLS modeling, 30 analysis of weights resulting from MLR, 29 hybrid linear analysis, 31 hierarchical multiblock PLS models, 32 successive projection algorithm, 33 artificial neural networks, 34,35 wavelet transform, 36,37 iterative predictor weighting PLS, 38 and discriminant partial least squares.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Spectrophotometric Determination of p-Benzoquinone and Chloranil after Microcrystalline Naphthalene Extraction by Using Genetic Algorithm-Based Wavelength Selection-Partial Least Squares Regression

Abdollahi

Bagheri

2004

ANAL. SCI.

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Microcrystalline naphthalene extraction has been used for the preconcentration of p-benzoquinone and tetrachloro-pbenzoquinone (chloranil), after their reaction by aniline, and later simultaneous spectrophotometric analysis by genetic algorithm-partial least squares (GA-PLS) calibration. The chemical variables affecting the analytical performance of the methodology were studied and optimized. Under the optimum conditions i.e., [aniline] = 0.05 M and [naphthalene] = 2.2% (w/v), preconcentration of 25 ml of sample solution permitted the detection of 0.32 and 0.23 µg ml -1 for pbenzoquinone and chloranil, respectively. The predictive abilities of partial least squares regression (PLS) and genetic algorithm-partial least squares regression (GA-PLS) were examined for simultaneous determination of two quinones. The GA-PLS shows superiority over other PLS methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity, provides useful information about the chemical system.

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Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry

Cited by 206 publications

References 28 publications

Near-Infrared Spectroscopy—Its Versatility in Analytical Chemistry

Near-Infrared Spectroscopy—Its Versatility in Analytical Chemistry

Ant colony optimisation: a powerful tool for wavelength selection

Simultaneous Spectrophotometric Determination of p-Benzoquinone and Chloranil after Microcrystalline Naphthalene Extraction by Using Genetic Algorithm-Based Wavelength Selection-Partial Least Squares Regression

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