Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection

Multivariate 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

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

The 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

Use of the orthogonal projection approach (OPA) to monitor batch processes

The 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

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

Generic 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

An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model

A 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

Differential proteomics based on 2D-difference in-gel electrophoresis and tandem mass spectrometry for the elucidation of biological processes in antibiotic-producer bacterial strains

Proteomics based on 2D-Difference In Gel Electrophoresis (2D-DIGE) coupled with mass spectrometry (MS) procedures can be considered a â\u80\u9cgold standardâ\u80\u9d to determine quantitatively and comparatively protein abundances in cell extracts from different biological sources/conditions according to a gel-based approach. In particular, 2D-DIGE is used for protein specie separation, detection, and relative quantification, whenever tandem MS is used to obtain peptide sequence information that is managed according to bioinformatic procedures to identify the differentially represented protein species. The proteomic results consist of a dynamic portray of over- and down-represented protein species that, with the integration of gene ontology resources, allow obtaining a comprehensive understanding of the complex network of molecular signaling, regulatory circuits, and biochemical reactions occurring in cellular contexts. For this reason, proteomics has been widely used for studying molecular physiology of Gram-positive bacterial strains producing bioactive metabolites and belonging to actinomycete family. This highlighted the complex relationships linking overall regulatory processes and metabolic pathways to the biosynthesis of interesting bioactive molecules. In this chapter, we provide a detailed description of the procedures adopted to perform a differential proteomic analysis of the actinomycete Microbispora ATCC-PTA-5024, producing the promising NAI-107 lantibiotic. Although each experimental proteomic procedure has to be optimized to face the specific molecular characteristics of the organism under investigation, the protocols here described have also been used with minor modifications for proteomic studies on other bacterial strains, including the actinomycetes Streptomyces coelicolor, S. ambofaciens, Amycolatopsis balhimycina, and the Gram-negative proteobacteria Klebsiella oxytoca and Pseudoalteromonas haloplanktis

Local factor analysis of rank-deficient reaction systems

The 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

Monitoring batch processes with the STATIS approach

Structuration des Tableaux A Trois Indices de la Statistique (STATIS), a method which can be seen as a 3-way exploratory analysis method, is proposed and investigated for the purpose of batch process monitoring. It is applied to batch process data to monitor the evolution in time of batches, through what are called the compromise plots. Because all batches do not have the same length, a particular procedure has to be used to obtain the compromise for batches. The monitoring is then based on the comparison between reference batches and new batches being processed. Three different real industrial data sets (with both process variables and spectroscopic variables) are studied in this paper and yield good results.</p