1,721,016 research outputs found
Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection
No full textMultivariate curve resolution (MCR) and especially the orthogonal projection approach (OPA) can be applied to spectroscopic data and were proved to be suitable for process monitoring. To improve the quality of the on-line monitoring of batch processes, it is interesting to get as many as possible spectra in a given period of time. Nevertheless, hardware limitations could lead to the fact that it is not possible to acquire more than a certain number of spectra in this given period of time. Wavelength selection could be a good way to limit this problem since it decreases size, and consequently the acquisition time, of each recorded spectrum. This paper details an industrial application of genetic algorithms (GA) coupled with a curve resolution method (OPA) for such purpose.</p
Use of the orthogonal projection approach (OPA) to monitor batch processes
No full textThe orthogonal projection approach (OPA) and multivariate curve resolution (MCR) are presented as a way to monitor batch processes using spectroscopic data. Curve resolution allows one to look within a batch and predict on-line real concentration profiles of the different species appearing during reactions. Taking into account the variations of the process by using an augmented matrix of complete batches, the procedure explained here calculates some prediction coefficients that can afterwards be applied for a new batch.</p
Determination of the number of components during mixture analysis using the Durbin-Watson criterion in the Orthogonal Projection Approach and in the SIMPLe-to-use Interactive Self-modelling Mixture Analysis approach
No full textThe Orthogonal Projection Approach (OPA) and the SIMPLe-to-use Interactive Self-modelling Mixture Analysis approach (SIMPLISMA) are widely employed during process monitoring to obtain concentration profiles and/or pure spectra of a mixture. In the first step of these methods, it is extremely important to select the right number of components present in the mixture. This selection is not always obvious, and in this paper, the Durbin-Watson criterion was applied to dissimilarity values in OPA and to purity values in SIMPLISMA as a tool for the decision of the number of components. It is shown that this yields more objective results than visual interpretation.</p
Optimization of signal denoising in discrete wavelet transform
No full textA method to optimize the parameters used in signal denoising in the wavelet domain is presented. The method, which is based on cross-validation CV.procedure, permits to select the best decomposition level and the best wavelet filter function to denoise a signal in the discrete wavelet domain. The procedure was validated by using computer generated signals to which white noise was added. Signals having different features and a range of signal to noise ratios were explored. The method was shown to give reliable results for all cases studied. The proposed method was applied to experimental gravitation field flow fractionation records, and the results were compared with classical low pass filtering in the Fourier domain
An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model
No full textA crucial point of the PLS algorithm is the selection of the right number of factors or components (i.e., the determination of the optimal complexity of the system to avoid overfitting). The leave-one-out cross-validation is usually used to determine the optimal complexity of a PLS model, but in practice, it is found that often too many components are retained with this method. In this study, the Monte Carlo Cross-Validation (MCCV) and the PoLiSh smoothed regression are used and compared with the better known adjusted Wold's R criterion.</p
Local factor analysis of rank-deficient reaction systems
No full textThe analysis of spectral measurement data sets using local factor analysis (LFA) requires the rank of the sub-matrix under study to be equal to the number of absorbing species present in the associated sub-system. However, because of mass balance or kinetic constraints, LFA will fail if local rank deficiency occurs. A local rank deficiency sub-system may be present in a global full-rank reaction system or a rank-deficient one. In this paper, the problems occurring when using window target-testing factor analysis (WTTFA), one type of the LFA methods, in a local rank-deficient situation are shown. A new augmented WTTFA (AWTTFA) is then proposed for the correct use of WTTFA when rank deficiency occurs. Principles of this new method have been demonstrated by a simulated kinetic system and an industrial batch data set.</p
Using orthogonal projection approach (OPA) for rank-deficient reaction processes
No full textOrthogonal projection approach (OPA) has been used for monitoring batch processes. However, because of the mass balance constraints between the reactants and products in a chemical reaction, standard OPA will fail if rank-deficiency occurs. The effect of the rank-deficiency on using OPA for process reaction data sets is discussed, and a new automatic approach for resolution of rank-deficiency data sets by OPA is proposed for batch control. The method is demonstrated for an industrial batch data set.</p
Robust regression and outlier detection for non-linear models using genetic algorithms
No full textExperimental data such as calibration and pharmacokinetic data can be contaminated with outliers. Robust regression based on the calculation of the least median of squared residuals (LMS) is robust to the presence of outliers and is used for outlier detection. The original LMS program only handles models which are linear in the parameters. A genetic algorithm can be used to obtain the LMS parameters for models that are non-linear in the parameters. In this work the feasibility of using genetic algorithms for LMS is demonstrated by means of curved analytical calibration and pharmacokinetic data contaminated with outliers. © 1995
Determining orthogonal and similar chromatographic systems from the injection of mixtures in liquid chromatography-diode array detection and the interpretation of correlation coefficients color maps
No full textGeneric orthogonal chromatographic systems might be helpful tools as potential starting points in the development of methods to separate impurities and the active substance in drugs with unknown impurity profiles. The orthogonality of 38 chromatographic systems was evaluated from weighted-average-linkage dendrograms and color maps, both based on the correlation coefficients between the retention factors on the different systems. On each chromatographic system, 68 drug substances were injected as mixtures of three or four components to increase the throughput. The (overlapping) peaks were identified and resolved with a peak purity algorithm, orthogonal projection approach (OPA). The visualization techniques applied allowed a simple evaluation of orthogonal and (groups of) similar systems.</p
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