Genetic algorithms in wavelength selection: a comparative study

Lucasius, C.B.; Beckers, M.L.M.; Kateman, G.

doi:10.1016/0003-2670(94)80155-x

Cited by 170 publications

(89 citation statements)

References 22 publications

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“…This entire procedure was executed independently for PLS model ranks from 1 to 15 factors. Other, more sophisticated, optimization algorithms are available to identify the ideal wavelengths for PLS analysis (Ding et al, 1998;Lucasius et al, 1994;Spiegelman et al, 1998).…”

Section: Pls Calibration Model Developmentmentioning

confidence: 99%

Nondestructive near‐infrared spectroscopic measurement of multiple analytes in undiluted samples of serum‐based cell culture media

Rhiel

Cohen

Murhammer

et al. 2001

Biotech & Bioengineering

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An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800-to 4200-cm )1 combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.

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Section: Pls Calibration Model Developmentmentioning

confidence: 99%

Nondestructive near‐infrared spectroscopic measurement of multiple analytes in undiluted samples of serum‐based cell culture media

Rhiel

Cohen

Murhammer

et al. 2001

Biotech & Bioengineering

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“…The stepwise elimination algorithm 33 was used to optimize the regions used in the models, and a coefficient of determination increase and PRESS (Predictive Residual Error Sum Of Squares) decreases were used as selection criteria.…”

Section: Variables Selectionmentioning

confidence: 99%

Multivariate regression models for the simultaneous quantitative analysis of calcium and magnesium carbonates and magnesium oxide through drifts data

Marder¹,

Tomedi²,

Ferrão³

et al. 2006

J. Braz. Chem. Soc.

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Neste trabalho são apresentados modelos de regressão multivariada, empregando espectros por reflexão difusa no infravermelho com transformada de Fourier (DRIFTS), para análise quantitativa do sistema ternário formado pelos carbonatos de cálcio e magnésio e pelo óxido de magnésio. Através de um diagrama ternário, foram definidas as misturas para aquisição dos espectros por reflexão no infravermelho médio. Empregando-se a regressão por mínimos quadrados parciais (PLS), foram construídos modelos com os dados espectrais centrados na média e/ou escalados pela variância, utilizando o conjunto de espectros de calibração. Para selecionar os melhores modelos, foram comparados os valores de RMSEP (root mean square error of prediction). Os resultados comprovam que bons modelos de calibração multivariada para a determinação de carbonatos de cálcio e magnésio e de óxido de cálcio podem ser obtidos a partir dos espectros por reflexão no infravermelho. Estas determinações são particularmente úteis no estudo da decomposição térmica de rochas dolomíticas.In the present work multivariate regression models were developed for the quantitative analysis of ternary systems using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) to determine the concentration in weight of calcium carbonate, magnesium carbonate and magnesium oxide. Nineteen spectra of standard samples previously defined in ternary diagram by mixture design were prepared and mid-infrared diffuse reflectance spectra were recorded. The partial least squares (PLS) regression method was applied to the model. The spectra set was preprocessed by either mean-centered and variance-scaled (model 2) or mean-centered only (model 1). The results based on the prediction performance of the external validation set expressed by RMSEP (root mean square error of prediction) demonstrated that it is possible to develop good models to simultaneously determine calcium carbonate, magnesium carbonate and magnesium oxide content in powdered samples that can be used in the study of the thermal decomposition of dolomite rocks.Keywords: dolomite, DRIFTS, partial least squares, multivariate regression, ternary mixture design IntroductionThe reserves of carbonate rocks in the state of Rio Grande do Sul -Brazil, are mainly constituted by dolomitelike rocks (MgO content ≅ 18%), while calcite-like rocks (MgO content < 4%) occur in a smaller proportion. It is assumed that, in volume, only 18% of the calcareous rocks are calcite-like (used mainly in cement production) and 82% are dolomite-like and are used mainly in the correction of soil acidity and in the production of lime.The search for a solution to the suppressed demand of raw materials by turning dolomite into calcium-rich carbonate and magnesium oxide, which are thoroughly used in industrial processes impelled the development of a physical process of dissociation of dolomite-like rocks, in order to obtain these two different products. The basic idea of the process is a fractionated calcination of a dolomite-like rock, composed m...

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“…The benefit gained from wavelength selection is not only the stability of the model to the colinearity in multivariate spectra but also the interpretability of the relationship between the model and the sample compositions. Meanwhile, the complexity of wavelength selection problem is discussed in many papers such as Reference [8] , where it has been shown both qualitatively and quantitatively that the wavelength selection problem is a NP-hard problem. Nevertheless, a number of procedures have already been proposed for wavelength selection in multicomponent spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

“…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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Genetic algorithms in wavelength selection: a comparative study

Cited by 170 publications

References 22 publications

Nondestructive near‐infrared spectroscopic measurement of multiple analytes in undiluted samples of serum‐based cell culture media

Nondestructive near‐infrared spectroscopic measurement of multiple analytes in undiluted samples of serum‐based cell culture media

Multivariate regression models for the simultaneous quantitative analysis of calcium and magnesium carbonates and magnesium oxide through drifts data

Ant colony optimisation: a powerful tool for wavelength selection

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