Data chemometrics

Chemometrics is the discipline which deals wdth the application of statistical and, in a more general sense, of mathematical methods to chemical data. Chemometric methods are used for the extraction of chemical information from chemical data. 

Yonng SS, Gombar VK, Emptage MR, Cariello NE, Lambert CG. Mixtnre de-convolntion and analysis of Ames mntagenicity data. Chemometrics Intell Lab Syst 2002 60 5-11. 

C.A.A. Duineveld, A.K. Smilde and D.A. Doombos, Comparison of experimental designs combining process and mixture variables. Part 2 Design evaluation on measured data, Chemometrics and Intelligent Laboratory Systems, 19 (1993) 309-318. 

His interests have included such areas of analytical chemistry as separation science, supercritical fluid extraction, chromatography, thermal analysis, and mathematical analysis of the chemical data (chemometrics). 

Thousands of chemical compounds have been identified in oils and fats, although only a few hundred are used in authentication. This means that each object (food sample) may have a unique position in an abstract n-dimensional hyperspace. A concept that is difficult to interpret by analysts as a data matrix exceeding three features already poses a problem. The art of extracting chemically relevant information from data produced in chemical experiments by means of statistical and mathematical tools is called chemometrics. It is an indirect approach to the study of the effects of multivariate factors (or variables) and hidden patterns in complex sets of data. Chemometrics is routinely used for (a) exploring patterns of association in data, and (b) preparing and using multivariate classification models. The arrival of chemometrics techniques has allowed the quantitative as well as qualitative analysis of multivariate data and, in consequence, it has allowed the analysis and modelling of many different types of experiments. 

Helland IS, Naes T, Isaksson T, Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data, Chemometrics and Intelligent Laboratory Systems, 1995, 29, 233-241. 

W. Wu, D.L. Massart and S. de Jong, The Kernel PCA Algorithms for Wide Data. Part II Fast Cross-Validation and Application in Classification of NIR Data, Chemometrics and Intelligent Laboratory Systems. 37 (1997) 271-280. 

Recently developed chemometrics algorithms are particularly suited to the rapid analysis and depiction of this data. Chemometrics is one approach that may offer novel insights into... 

Rinnan, A. Andersen, C. M. (2005). Handling of first-order Rayleigh scatter in PARAFAC modelling of fluorescence excitation-emission data. Chemometrics and Intelligent Laboratory Systems, Vol. 76, No. 1, pp. 91-99... 

Chemometrics has been defined as "a chemical discipline that uses statistical and mathematical methods to design or select optimum procedures and experiments, and to provide maximum chemical information by analyzing chemical data". Chemometrics can be considered as a part of the wider field chemoinformatics, and has close relationships to bioinformatics. 

Tauler, R. (1995). Multivariate curve resolution applied to second order data Chemometrics and Intelligent Laboratory Systems Vol.30, p>p. 133-146, ISSN 0169-7439... 

P. Geladi. 1989. Analysis of multi-way (multi-mode) data. Chemometrics and Intelligent Laboratory Systems 7 11-30. 

Tauler R. Multivariate curve resolution applied to second order data. Chemometr Intell Lab... 

Bro R, Vidal M. EEMizer automated modeling of fluorescence EEM data. Chemometr Intell Lab Syst 2011 106 86-92. 

De Maesschalck R, Candolfi A, Massart DL, Heuerding S. Decisitm criteria for soft independent modelling of class analogy applied to near infiared data. Chemometr Intell Lab Syst 1999 47 65 77. 

Daszykowski M, Orzel J, Wrobel MS, Czamik-Matusewicz H, Walczak B. Improvement of classification nsing robust soft classificatitm rules for near-infrared reflectance spectral data. Chemometr Intell Lab Syst 2011 109 86 93. 

---