Partial least-squares regression for multicomponent kinetic determinations in linear and non-linear systems

Blanco, M.; Coello, J.; Iturriaga, H.; Maspoch, S.; Redón, M.

doi:10.1016/0003-2670(94)00494-7

Cited by 33 publications

(18 citation statements)

References 26 publications

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“…El número máximo de factores o variables latentes que puede ser calculado es el resultado de min(N, K), donde N es el número de observaciones y K es el número de variables independientes X (Li et al, 2002 (Blanco, et al, 1995 El Algoritmo NIPALS estándar es el siguiente (Baffi et al, 1999): 0. Centrar en media y escalar X e Y.…”

Section: Plsrunclassified

“…La experiencia disponible en el uso del método PLSR con varias técnicas analíticas ha demostrado que permite algunos efectos no lineales, entregando buenas predicciones al respecto (Blanco et al, 1995) Otro planteamiento para manejar la no linealidad de los datos es buscar subconjuntos lineales en los grandes conjuntos de datos (Figura 11) (Geladi, 2002). Un planteamiento simple para extender la matriz de entrada es incluir combinaciones no lineales de las variables originales X (como son logaritmos, valores cuadráticos, productos cruzados, etc.)…”

Section: A H+1 = C H a Hunclassified

“…La selección del número de componentes principales (vectores latentes) no es fácil y se basa a menudo en el procedimiento por estimación del error de predicción de la CV, (Blanco et al, 1995).…”

Section: Figura 13unclassified

“…FIGURA 14 Error promedio de validación para modelos con distinto número de factores (Fuente: Ferré, 2006) Una forma de cuantificar la magnitud del fallo del modelo PLSR es por medio del Error cuadrático medio de predicción (MSEC), el cual se obtiene con la siguiente expresión (Blanco, et al, 1995):…”

Section: Figura 13unclassified

“…No requieren de conocimiento previo de los valores de las constantes o coeficientes involucrados en el fenómeno en estudio. Lo que se busca generalmente con estas técnicas es la simplificación (Blanco et al, 1995).…”

Section: Introductionunclassified

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Modelación unidimensional de la calidad del agua en embalses. Análisis comparativo de modelos y multivariantes.

Gutiérrez¹

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Section: Plsrunclassified

Section: A H+1 = C H a Hunclassified

Section: Figura 13unclassified

Section: Introductionunclassified

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Modelación unidimensional de la calidad del agua en embalses. Análisis comparativo de modelos y multivariantes.

Gutiérrez¹

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A combined artificial neural network/residual bilinearization approach for obtaining the second‐order advantage from three‐way non‐linear data

Olivieri¹

2005

Journal of Chemometrics

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Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available multivariate methodologies for obtaining this advantage are all based on linear models, and hence they are not applicable to spectral information behaving in a non-linear manner with respect to target analyte concentrations. This work describes the combination of a backpropagation artificial neural network model with a technique known as residual bilinearization, applicable to second-order spectral information. The joint model allows one to efficiently extract analyte concentrations from intrinsically non-linear data, even in the presence of unsuspected constituents. Simulations have been performed by mimicking deviations from linearity brought about by: (1) exponential relationship between fluorescence and concentration, (2) kinetic evolution of responsive reaction products and (3) analytes acting as reaction catalysts. In all of these cases, successful prediction of the analyte concentrations was achieved on large test sample sets, which included the presence of overlapping components not included in the training step. The new method not only obtains the second-order advantage, but also correctly retrieves the contribution of the unsuspected components to the total test sample signals. The comparison with a multivariate methodology based on partial least-squares regression with second-order advantage shows that the presently described method displays better predictive ability.

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In‐line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics models

Bhatia

Mehdizadeh

Drapeau

et al. 2017

Engineering in Life Sciences

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In-line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics modelsThe application of PAT for in-line monitoring of biopharmaceutical manufacturing operations has a central role in developing more robust and consistent processes. Various spectroscopic techniques have been applied for collecting real-time data from cell culture processes. Among these, Raman spectroscopy has been shown to have advantages over other spectroscopic techniques, especially in aqueous culture solutions. Measurements of several process parameters such as glucose, lactate, glutamine, glutamate, ammonium, osmolality and VCD using Ramanbased chemometrics models have been reported in literature. The application of Raman spectroscopy, coupled with calibration models for amino acid measurement in cell cultures, has been assessed. The developed models cover four amino acids important for cell growth and production: tyrosine, tryptophan, phenylalanine and methionine. The chemometrics models based on Raman spectroscopy data demonstrate the significant potential for the quantification of tyrosine, tryptophan and phenylalanine. The model for methionine would have to be further refined to improve quantification.

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Partial least-squares regression for multicomponent kinetic determinations in linear and non-linear systems

Cited by 33 publications

References 26 publications

Modelación unidimensional de la calidad del agua en embalses. Análisis comparativo de modelos y multivariantes.

Modelación unidimensional de la calidad del agua en embalses. Análisis comparativo de modelos y multivariantes.

A combined artificial neural network/residual bilinearization approach for obtaining the second‐order advantage from three‐way non‐linear data

In‐line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics models

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