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Bogomolov A.,J and M Analytik AG
Chemometrics and Intelligent Laboratory Systems | Year: 2011

Modern process analytical chemistry and technology applications are reviewed from the point of view of acquisition, resolution and analysis of trajectories in a designed analytical space. © 2011 Elsevier B.V.

Ermakov V.V.,Samara State Technical University | Bogomolov A.,J and M Analytik AG | Bykov D.E.,Samara State Technical University
Journal of Environmental Management | Year: 2012

Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into account.Representative sample sets were collected from fifty four depositories of different age, origin, and location in Samara region and analyzed using multivariate data analysis: Principal Component Analysis (PCA) and Partial Least-Squares (PLS) regression. PCA results provide a better understanding of the internal data structure, i.e. variable correlations and groupings. Based on the PCA results, a new approach to the classification of oil sludge depositories has been suggested. Another practically important task of site assessment has been solved by PLS regression modeling. The method has been successfully applied to the accurate estimation of the depository processing profitability for a specific site. © 2012 Elsevier Ltd.

Bogomolov A.,J and M Analytik AG | Bogomolov A.,Samara State Technical University | Melenteva A.,Samara State Technical University
Chemometrics and Intelligent Laboratory Systems | Year: 2013

Accurate prediction models for fat and total protein content in raw milk have been built on visible and short-wave near infrared spectra (400-1100. nm) of a designed sample set with systematically varied nutrient composition and homogenization degree. Unlike the conventional approach to the spectroscopic milk analysis, exploiting the components' absorbance, the present method basically relies on the phenomenon of light scatter by fat globules and protein micelles.It has been shown that partial least-squares (PLS) regression on raw spectral data results in higher prediction accuracies of fat and total protein content compared to scatter-corrected spectra. Interpretation given to individual PLS factors confirms a dominating role of scatter in the modeling and aids in general understanding of quantitative multivariate analysis based on complex optical responses resulting from multiple scattering by different particles at significantly varying size distributions. This new approach can be used in the raw milk laboratory or field analysis as well as for in-line monitoring of various milk transfer and production stages.Practical modeling issues, i.e. experimental design of the sample set, model validation and refinement, have been also elaborated in this study. Variable selection by interval PLS (iPLS) regression essentially improved the model performance. Selected intervals can be utilized for technical simplification of suggested method of milk analysis. © 2013 Elsevier B.V.

Kucheryavski S.,University of Aalborg | Esbensen K.H.,University of Aalborg | Bogomolov A.,J and M Analytik AG
Journal of Chemometrics | Year: 2010

Image analysis is an efficient technique used in many areas of science and industry. However, in process analytical applications it tends to be an ancillary tool, used mainly for visual monitoring or measuring some geometrical properties. At the same time, there are many other important aspects of the process samples appearance, besides measurable distances, that may be connected to the information of interest. In the present paper, the methods of image analysis were applied to at-line monitoring of fluid bed pellet coating process. The quantitative description of images of pellet samples, taken from different process stages, has been obtained using two different approaches: wavelet decomposition and angle measure technique (AMT). Both methods revealed a strong correlation between image features and process parameters. However, the AMT results turned out to be more accurate and stable. It has been shown that pellet images, taken with a conventional digital camera, can be used for at-line monitoring of the process course, specifically, the growth of pellets due to the coating. An algorithm for precise counting of pellets has been developed. Combined with the sample weighing, it enables an accurate determination of the mean added pellets' weight. The method can be used for the determination of the mean layer thickness, either by itself for at-line analysis or as a reference technique, when modeling the process from in-line spectroscopy data. Copyright © 2010 John Wiley & Sons, Ltd.

Bogomolov A.,J and M Analytik AG | Bogomolov A.,Samara State Technical University | Melenteva A.,Samara State Technical University | Dahm D.J.,Rowan University
Journal of Near Infrared Spectroscopy | Year: 2013

Step-wise homogenisation has been applied to raw milk samples of different composition to investigate the effect of fat globule size distribution on diffuse transmission spectra in the region 400-1100 nm. Homogenisation results in significant spectral changes with two distinct phases. Initial even growth of spectral intensity across the whole spectral range, observed at lower degrees of homogenisation, was followed by a drastic fall in absorbance at the long-wave end of spectrum as the fat globules reached some critical size. Fat and protein content in the sample significantly affected the observed dependences of spectra on the applied homogenisation time. These observations have been explained as a superposition of two effects: growing fat globule density and changes in scatter nature as the particle sizes approach the light wavelengths in a corresponding spectral range. The representative layer theory has been used to illustrate the nature of the spectral effects.

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