Areas of expertise

Experimental planning and optimization using design of experiments approach (DoE)

  • Experimental planning

  • Optimization of experiments

  • Quality by design for industrial purposes

Signals' processing

  • Alignment of instrumental signals including chromatographic one- and two-dimensional signals

  • Processing of two-dimensional instrumental signals (e.g. GC-MS, CE-MS, LC-MS, hyperspectral images, etc.)

  • Comparative analysis of instrumental signals

  • Selection of relevant regions explaining underlying differences among groups of interest described by collection of instrumental signals

Selected publications:

  1. M. Daszykowski, Y. Vander Heyden, C. Boucon, B. Walczak, Automated alignment of one-dimensional chromatographic fingerprints, Journal of Chromatography A, 1217 (2010) 6127-6133

  2. A.M. van Nederkassel, M. Daszykowski, P.H.C. Eilers, Y. Vander Heyden, A comparison of three algorithms for chromatograms alignment, Journal of Chromatography A, 1118 (2006) 199-210

  3. W. Wu, M. Daszykowski, B. Walczak, B.C. Sweatman, S.C. Connor, J.N. Haselden, D.J. Crowther, R.W. Gill, M.W. Lutz, Peak alignment of urine NMR spectra using fuzzy warping, Journal of Chemical Information and Modeling, 46 (2006) 863-875

  4. M. Daszykowski, R. Danielsson, B. Walczak, No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry, Journal of Chromatography A, 1192 (2008) 157-165

Data mining

  • Hierarchical clustering (single, average, complete linkage)

  • Non-hierarchical clustering (K-means, Neural Gas, Growing Neural Gas)

  • Density-based clustering (DBSCAN, OPTICS)

  • Projection Pursuit (PP)

  • Principal Component Analysis (PCA)

  • Multiple Factor Analysis

  • Nonlinear Principal Component Analysis (nPCA)

  • Generative Topographic Mapping (GTM)

  • Self-Organizing Kohonen Maps (SOM)

  • TUCKER, PARAFAC, STATIS, etc.

  • Data fusion (processing data containing different blocks of variables)

Selected publications:

  1. I. Stanimirova, B. Walczak, D.L. Massart, V. Simeonov, C.A. Saby, E. Di Crescenzo, STATIS, a 3-way method for data analysis. Application to environmental data, Chemometrics and Intelligent Laboratory Systems, 73 (2004) 219-233

  2. I. Stanimirova, K. Zehl, D.L. Massart, Y. Vander Heyden, J.W. Einax, Chemometric analysis of soil pollution data applying the Tucker N-way method, Analytical and Bioanalytical Chemistry, 385 (2006) 771-773

  3. M. Daszykowski, B. Walczak, D.L. Massart, Projection methods in chemistry, Chemometrics and Intelligent Laboratory Systems, 65 (2003) 97-112

  4. M. Daszykowski, B. Walczak, D.L. Massart, On the optimal partitioning of data with K-means, Growing K-means, Neural Gas and Growing Neural Gas, Journal of Chemical Information and Computer Sciences, 42 (2002) 1378-1389

  5. M. Daszykowski, B. Walczak, D.L. Massart, Looking for Natural Patterns in Data. Part 1: Density Based Approach, Chemometrics and Intelligent Laboratory Systems, 56 (2001) 83-92

  6. M. Daszykowski, B. Walczak, D.L. Massart, Looking for Natural Patterns in Analytical Data. 2. Tracing Local Density with OPTICS, Journal of Chemical Information and Computer Sciences, 42 (2002) 500-507

  7. I. Stanimirova, B. Walczak, D.L. Massart, Multiple Factor Analysis in environmental chemistry, Analytica Chimica Acta, 545 (2005) 1-12

Data modelling

  • Multivariate Linear Regression (MLR)

  • Partial Least Squares (PLS)

  • Principal Component Regression (PCR)

  • Neural Networks (NN)

  • Radial Basis Function-Partial Least Squares (RBFN-PLS)

  • Support Vector Machines (SVM)

  • Classification and Regression Trees (CART)

  • Linear Discriminant Analysis (LDA)

  • Quadratic Discriminant Analysis (QDA)

  • Regularized Discriminant Analysis (RDA)

Robust methods

  • robust Principal Component Analysis (rPCA)

  • robust SIMCA

  • robust Partial Least Squares (rPLS)

Selected publications:

  1. M. Daszykowski, K. Kaczmarek, Y. Vander Heyden, B. Walczak, Robust statistics in data analysis - a review. Basic concepts, Chemometrics and Intelligent Laboratory Systems, 85 (2007) 203-219

  2. M. Daszykowski, K. Kaczmarek, I. Stanimirova, Y. Vander Heyden, B. Walczak, Robust SIMCA - bounding influence of outliers, Chemometrics and Intelligent Laboratory Systems, 87 (2007) 121-129

  3. I. Stanimirova, B. Walczak, D.L. Massart, V. Simeonov, A comparison between two robust PCA algorithms, Chemometrics and Intelligent Laboratory Systems, 71 (2004) 83-95

  4. I. Stanimirova, M. Daszykowski, B. Walczak, Dealing with missing values and outliers in principal component analysis, Talanta, 72 (2007) 172-178

  5. I. Stanimirova, B. Walczak, Classification of data with missing elements and outliers, Talanta, 76 (2008) 602-609

Missing and censored data

  • Expectation Maximization (EM)

  • multiple regression model for data with missing elements and outliers

Selected publications:

  1. I. Stanimirova, M. Daszykowski, B. Walczak, Dealing with missing values and outliers in principal component analysis, Talanta, 72 (2007) 172-178

  2. I. Stanimirova, B. Walczak, Classification of data with missing elements and outliers, Talanta, 76 (2008) 602-609

  3. I. Stanimirova, S. Serneels, P.J. Van Espen, B. Walczak, How to construct a multiple regression model for data with missing elements and outlying objects, Analytica Chimica Acta, 581 (2007) 324-332