Современные методы анализа многомерных данных - PDF

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Самарский государственный технический университет Комиссия по хемометрике Научного Совета по аналитической химии РАН Российское хемометрическое общество Университет Ольборг (ACABS group) при поддержке

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Самарский государственный технический университет Комиссия по хемометрике Научного Совета по аналитической химии РАН Российское хемометрическое общество Университет Ольборг (ACABS group) при поддержке Российского фонда фундаментальных исследований Пятый международный симпозиум Современные методы анализа многомерных данных Организационный комитет Сопредседатели Член-корр. РАН Грибов Л.А. (ГЕОХИ РАН) Д.т.н., профессор Быков Д.Е. (СГТУ) Проф. Эсбенсен К. (ACABS, Дания) Ученый секретарь Рюмина Н.В. (СГТУ) Члены оргкомитета Д.ф.-м.н. Померанцев А.Л. (ИХФ РАН) К.ф.-м.н. Родионова О.Е. (РХО) Проф. Таулер Р. (CSIC, Испания) К.ф.-м.н. Кучерявский С.В. (АлтГУ) К.х.н. Богомолов А.Ю. (EMBL) Адрес Организационного комитета Россия , г. Самара, ул. Молодогвардейская, 244 Телефон/факс: +7 (846) Thanks The WSC-5 organizers and participants wish express greatest appreciation to the following conference sponsors for their valuable economic and friendly help: Russian Foundation for Basic Research Applied Chemometrics Analytical Chemistry & Sampling Research Group (Denmark) Polycert Company (Moscow, Russia) Institute of Chemical Physics (Moscow, Russia) Chemometrics and Intelligent Laboratory Systems journal (Elsevier Company) Finally, we are grateful to all the WSC-4 attendees, lecturers, accompanying persons and visitors for their interest to the conference. 2 Useful information Location and accommodation The attendees will live in this campus in the comfortable single and double rooms. All campus bungalows have big halls for evening discussions. Russian banya (sauna) and cross-country ski will be available. Conference sessions will be placed at the main building. Meals Breakfast, lunches and dinners as well as conference banquet will take place at the dining room in main building. Scores&Loadings Traditional Scores and Loadings meeting, will again be organized in the oldfashioned, self service Kostroma style and will take place in main building. Communication The main two Russian cellular nets, Beeline and MTS, have excellent coverage in campus and around. The Internet can be accessed from administrative building during coffee-brakes and free time. Money You may exchange Euros and US dollars to Russian rubles (RUR) in banks in Samara train station or airport. The organizing committee also will help to exchange currency during the conference. Excursion On the Wednesday participants will have a chance to visit one of the supersecrets of the World War II Stalin's bunker. It was built from March till December in 1942 and has 37 meters deep underground, the deepest bunker of its time. Miscellaneous The conference official language is English. Everyone is encouraged to have his/her badge attached, both during the symposium sessions and social activities. 3 Timetable Saturday, February 18, :00 18:00 Arrival, registration 18:30 19:30 Dinner 20:00 00:00 Scores & Loadings Scheme of Polytechnic campus 4 Sunday, February 19, :00 09:30 Breakfast 9:30 11:00 Free time (skiing/skating) 11:00 11:30 Coffee break Session 1 Chair: Dmitry Bykov 11:30 12:00 Conference opening 12:00 13:00 L1 Kim Esbensen Representative sampling in PAT and environmental/geological work: Theory of Sampling (TOS) a missing link 13:00 14:00 Lunch Session 2 Chair: Oxana Rodionova 14:30 15:30 L2 Dmitri Bykov Chemometric methods for environmental pollution monitoring 15:30 16:00 T1 Evgeniy Mikhailov Ecological assessment of waste fields with Principal Component Analysis feasibility study 16:00 16:30 Coffee break Session 3 Chair: Alexey Pomerantsev 16:30 17:00 T2 Mikhail Shuvalov Technique for selection of natural and waste water parameters for projecting purifying treatment plants 17:00 17:30 T3 Sergei Zhilin On sequential experimental design for empirical model-building under interval error 17:30 18:00 T4 Sergei Kucheryavski Using black and white models for classification of medical images 18:00 18:30 T5 Andrey Bogomolov Two examples of chemometrics application in protein crystallography 18:30 19:00 Free time 19:00 20:00 Dinner 20:00 00:00 Scors & Loadings 5 Monday, February 20, :00 09:30 Breakfast Session 4 Chair: Roma Tauler 09:30 10:30 L3 Pentti Minkkinen Weighting error - the often neglected component of the sampling errors 10:30 11:00 T6 Semyen Spivak The inverse problems of chemical kinetics 11:00 13:00 Free time (skiing/skating) 13:00 14:00 Lunch Session 5 Chair: Pentti Minkkinen 14:30 15:30 L4 Roma Tauler Investigation of main contamination sources of heavy metal ions in fish, sediments and waters from catalonia rivers using different multiway data analysis methods 15:30 16:00 T7 Gennadii Rozenberg The principle of «ecological matreshka» (a set of nesting doll) in the system of the analysis of multivariate ecological data 16:00 16:30 Coffee break Session 6 Chair: Satu-Pia Reinikainen 16:30 17:00 T8 Taghi Khayamian The investigation of hetrosecdastic noise in multiway methods T9 Alica Rudnitskaya Analysis of port wines using the 17:00 17:30 electronic tongue. Assessment of port wine age and comparison with chemical analysis data T10 Artem Sidelnikov The classification of aqueous solutions 17:30 18:00 with the use of voltammetric system of divided cells and principal component analysis 18:00 18:30 T11 Eugene Karpushkin Structurally aware approach to the interpretation of vibrational spectra 18:30 19:00 Free time 19:00 20:00 Dinner 20:00 00:00 Scores & Loadings 6 Tuesday, February 21, :00 09:00 Breakfast Session 7 Chair: Paul Geladi 09:30 10:30 L5 Oxana Rodionova Analytical process control and optimization 10:30 11:00 T12 Leon Rusionov Real time diagnostics of technological processes and field equipment 11:00 13:00 Free time (skiing/skating) 13:00 14:00 Lunch Session 8 Chair: Leon Rusinov 14:30 15:30 L6 Paul Geladi Is hypserspectral imaging an analytical instrument? 15:30 16:00 T13 Pavel Luzanov NIR analyzers standardization 16:00 16:30 Coffee break Session 9 Chair: Gennadii Rozenberg 16:30 17:00 T14 Federico Marini Multilayer feed-forward artificial neural networks for class-modeling 17:00 17:30 T15 Nikolay Zemtsov Analysis of short-term process dynamics 17:30 19:00 Poster Session 19:00 20:00 Free time 20:00 00:00 Banquet 7 Wednesday, February 22, :00 09:30 Breakfast 09:30 13:00 Excursion to Stalin s bunker 13:00 14:00 Lunch Session 10 Chair: Sergei Kucheryavski 14:30 15:30 L7 Yuri Kalambet Implementation of chemometric techniques in chromatographic data station software 15:30 16:00 T16 Alexey Pomerantsev Hard and soft modeling. A case study 16:00 16:30 Coffee break Session 11 Chair: Kim Esbensen 16:30 17:30 L8 Christopher Marks A retrospective of the previous WSC from a personal perspective. 17:30 18:30 Discussion: Drushbametric program results and prospects Conference closing 18:30 19:00 Free time 19:00 20:00 Dinner 20:00 00:00 Scores & Loadings 8 Abstracts Lectures L1. Representative sampling in PAT and environmental/geological work: Theory of Sampling (TOS) a missing link Kim H. Esbensen, Aalborg University, Esbjerg Institute of Technology, Denmark Representative sampling is a critical success factor for PAT, in the geosciences and for environmental characterisation. Many instrumental analytical methods are based on analysis by proxy , for which the representativity of the reference data is critical w.r.t. the underlying multivariate calibrations. Both X-data as well as Y-date need to be fully representative, in themselves but also w.r.t. intercalibrations. Although TOS has been known for 25 years, it is still only very little known or implemented in today's analytical chemistry, even though sampling errors form the by far most dominant part of what is all too loosely termed measurement errors . This presentation presents a theoretical as well as practical framework of seven sampling unit operations (TOS) with which to approach all types of sampling issues in the field or plant, in industry; the laboratory as well as for PAT purposes. L2. Chemometric methods for environmental pollution monitoring D.E Bykov 1,, K.L. Chertes 1, A.L. Pomerantsev 2, O.Ye. Rodionova 2 1 Samara State Technical University 2 Semenov Institute of Chemical Physics of Russian Academy of Sciences At present time in Russian Federation and abroad there are a lot of methods and technologies for environmental pollution monitoring. In the majority they are based on both the conventional methods of chemical and physical analyses in laboratory and in-process management, as well as modern methods of chemometric analyses. Chemometric methods are widely used for solution of highly specialised tasks of control of waste condition (humidity, ash level, etc.), as well as control of the process of waste processing. It is suggested to use the integrated system of methods for on-line monitoring of waste field at the stages of processing and reclamation. The characteristic feature of this approach is the considering of composition of mancaused fields typical to Russia. It is based upon the well known chemometric technique as Principal Component Analysis, which aims to reveal the hidden structural interrelations in data set. This method is applied to evaluate the current state of a waste field as well as to predict its evolution in future. Mathematical model of conversion of substance inside the field forecasts its properties at geological environment assimilation. 9 One more ecological problem, that is sorting of plastics in waste, is being solved using chemometrics. Environmental and economic reasons make recycling of mixed industrial and household waste more attractive. Usually low amount of recycled waste can significantly be increased by sorting as the purer fractions of different plastics can easier be reused. There are the pilot experiments on in-line sorting of the multi-component waste flows based on the near infrared spectroscopy (NIR). NIR measurements are rapid, simple and need no special sampling preparation. The apparatus is available as a portable unit that gives results in a matter of minutes with the help of computerized controls. However, successful problem solving depends on extracting the needed information. A factor limiting significant advancement in these areas is that data obtained from such instruments are typically highly correlated and corrupted with noise, making it difficult to obtain necessary information. To extract it, a special mathematical data processing is applied. Another application of the method of multivariate analysis is the selection of the ways of processing of large-capacity heterophasis industrial and household waste with their following reuse as a reclaiming material; the utilisation of highly-polluted sewage with the isolation of valuable components; the creation of new technological processes with industrial waste as raw material. Thus, the use of mathematical apparatus of multivariate analysis allows to optimise the solution of the widest range of environmental tasks. 1. Bykov D.E., Smirnov B.Yu.: Chemical and chemical technology, spec, 2004; (in Russian). 2. Chertes K.L., Bykov D.E.: Ecology and Industry of Russia, 2003, 2, 4-8, (in Russian). 3. Rodionova O. Ye., Pomerantsev A.L.: Uspekhi Khimii, 2006, in print (in Russian). 4. Pomerantsev A. L., Rodionova O. Ye., Höskuldsson A.: Chemom. Intell. Lab.Syst, 2006, in print. L3. Weighting Error the Often Neglected Component of the Sampling Errors Pentti Minkkinen, Lappeenranta University of Technology, Finland Pierre Gy has developed a complete sampling theory [1-3]. He divides the sampling errors into two main classes: 1) Errors arising from incorrect sampling equipment and procedures and 2) Statistical sampling errors. To class 1) belong sample delimitation, sample extraction and preparations errors and into class 2 ) fundamental sampling error, grouping and segregation error, long range point selection error and periodic point selection error. There is also an eighth error component, called weighting error. Weighting error is made if a simple average is calculated from samples taken from a continuous object, e.g. from a process stream, where the flow-rate varies, or samples of equal sizes are cut from a continuous object, 10 where the density varies along the object. In sampling process steams this error is eliminated, if proportional cross-stream sample cutters are used, the weights of the samples are recorded and the mean of the lot, a L, is estimated as the weighted mean: (1) Here M i is the weight, a i the analytical result of sample i and the mean sample weight. Weighting error is also eliminated in this case if all the samples are combined into one homogenized composite sample, which is then analyzed. The uncertainty (relative standard deviation) of the mean, a L, can be estimated from the variogram of the experimental heterogeneity, h i, of the process by using the technique Gy has developed. (2) When high-volume gas or liquid streams are sampled it usually not possible to use cross-stream sampling. In this case the sample masses in Eq. 1 can be replaced by flow-rates at the sampling time, if reliable simultaneous flow-rate measurements are available. To avoid sampling in composite sample in this case, the sampling time must be related to the low-rate measurement; either sample increments of equal size are drawn when a fixed volume has passed the sampling point, ore time intervals during which the samples are drawn are proportional to the flow-rates at sampling time. Depending on case the weighting error can be really significant. Examples, both simulated and real cases, are shown. 1. Gy P.M., Sampling of Particulate Materials, Theory and Practice, Elsevier, Amsterdam, Gy P.M., Sampling of Heterogeneous and Dynamic Material Systems, Elsevier, Amsterdam, Gy P.M., Sampling for Analytical Purposes, John Wiley & Sons Ltd, Chichester, L4. Investigation of main contamination sources of heavy metal ions in fish, sediments and waters from catalonia rivers using different multiway data analysis methods Emma Peré-Trepat 1 and Roma Tauler 2 1 Dept. of Analytical Chemistry, Universitat de Barcelona, Barcelona, Spain 2 IIQAB-CSIC, Barcelona, Spain Comparison of different multiway data analysis methods including Principal Component Analysis and Multivariate Curve Resolution Matrix Augmentation bilinear model based methods, and PARAFAC and TUCKER3 trilinear model based methods is performed in the analysis of a three-way data set formed by the analysis of 11 11 metal ions in 17 river samples of fish, sediment and water at the same site locations of Catalonia (NE, Spain). Adaptation of Multivariate Curve Resolution for the fulfillment of PARAFAC and TUCKER3 trilinear models is shown the flexibility of this method to handle data of different structures and fulfilling different type of constraints. Although the way how the results are obtained using these different chemometric methods is different, it is shown that the same main interpretation and conclusions may be derived independently of the chemometric method used for the analysis although a more simplified interpretation is obtained in some cases using multilinear models specially if reduction of the number of components in one of the modes is possible L5. Analytical Process Control and Optimization Oxana Rodionova, Alexey Pomerantsev, Institute of Chemical Physics, Moscow, Russia The main concept of multivariate statistical process control (MSPC) is application of historical instrumental X-data for construction of a linear model, which explains how the final results (i.e. quality, y) depend on the X-variables. Apparently, studding this model, it is possible to work out a program of actions that could improve the process performance in general. However, this is a post factum optimization, while the most important issue in production is an in situ optimization, which prescribes immediate actions in the course of production in order to correct its current state and to improve the future. The optimization methods are based on the PLS block modeling as well as on the Simple Interval Calculation methods of interval prediction and object status classification. It is proposed to employ the series of expanding PLS/SIC models in order to support the on-line process improvements. This method helps to predict the effect of planned actions on the product quality, and thus enables passive quality control. We have also considered an optimization approach that proposes the correcting actions for the quality improvement in the course of production. The latter is an active quality optimization, which takes into account the actual history of the process. The advocate approach is allied to the conventional method of multivariate statistical process control (MSPC) as it also employs the historical process data as a basis for modeling. On the other hand, the presented concept aims more at the process optimization than at the process control. Therefore, it is proposed to call such an approach as multivariate statistical process optimization (MSPO). Methods of process control and optimization are illustrated with a real world example. 1. Pomerantsev, O. Rodionova, A. Höskuldsson Process Control and Optimization with Simple Interval Calculation Method , Chemom. Intell. Lab.Syst., in print (2006) 12 L6. Is Hypserspectral Imaging an Analytical Instrument? Paul Geladi, SLU, Umeå, Sweden Hyperspectral imaging in the near infrared in the laboratory is now a possible but expensive and slow reality. Images of reasonable sizes (256x320 pixels) can be made where every pixel is a spectrum of 120 wavelengths in the region nm. The images have the properties of visual interpretation, but do they also have the same properties as an analytical instrument giving a bulk spectrum between 400 and 2500 nm? What are the advantages and disadvantages of images compared to reflectance spectra? What are the issues of calibration and instrument setup? How does sampling come in? The role of different mathematical and statstical modeling techniques will be highlighted. Chemometrics has a major role to play in getting things right when hyperspectral imaging is concerned. Some examples are given of how imaging models can be made and how they can be interpreted. 1. Geladi P & Grahn H, Multivariate Image Analysis, Wiley, Chichester, 1996, ISBN Geladi P, Burger J & Lestander T, Hyperspectral imaging: calibration problems and solutions, Chemometrics and Intelligent Laboratory Systems, 72, , Burger J & Geladi P, Hyperspectral NIR Image Regression Part I : Calibration and CorrectionJournal of Chemometrics, 19, , L7. Implementation of chemometric techniques in chromatographic data station software Yuri Kalambet, Ampersand Ltd., Moscow, Russia A wide variety of chemometric techniques is implemented in Chrom&Spec (russian name Multikhrom ) chromatography data station. They include factor analysis of multichannel chromatograms, deconvolution of overlapping peaks into a set of EMG peaks, Fourier filtration of periodic noise and accessing peak parameters by adaptive approximation. Some achievements in chemometrics theory helps in solving these tasks. L8. Drushbametrics My Russian Adventures Christopher A Marks, Richmond, USA A retrospective of the previous WSC from a personal perspective. 13 Talks T1. Ecological assessment of waste fields with Principal Component Analysis feasibility study Evgeniy Michailov 1, O.V. Tupicina 1, O.Ye. Rodionova 2
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