Analytical chemistry of the recent years is strongly influenced by automation. Data acquisition from analytica instruments - and some times also controlling of instruments - by a computer are principally solved since many years. Availability of microcomputers made these tasks also feasible from the economic point of view. Besides these basic applications of computers in chemical measurements scientists developed computer programs for solving more sophisticated problems for which some kind of "intelligence" is usually supposed to be necessary. Harm less numerical experiments on this topic led to passionate discussions about the theme "which jobs cannot be done by a computer but only by human brain ? . If this question is useful at all it should not be ans wered a priori. Application of computers in chemistry is a matter of utility, sometimes it is a social problem, but it is never a question of piety for the human brain. Automated instruments and the necessity to work on complex pro blems enhanced the development of automatic methods for the reduction and interpretation of large data sets. Numerous methods from mathematics, statistics, information theory, and computer science have been exten sively investigated for the elucidation of chemical information; a new discipline "chemometrics" has been established. Three different approaches have been used for computer-assisted interpretations of chemical data. 1. Heuristic methods try to formu late computer programs working in a similar way as a chemist would solve the problem. 2."

Using formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects the practical approach of chemometrics and the more formally oriented one of statistics. To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry. They also examine results of the different methods, comparing traditional approaches with their robust counterparts. In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems. Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts. It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data.

1907-66: 3'791'519 1967-71: 1'314'655 (8. Sammelregister) 6'418'796 1972-76: 1'772'194 (9. Sammelregister) { online 1977 -81: 2'201'680 (10. Sammelregister) 1982-: 1'130'267 (bis Bd. 100; 30. 6. 84) Seit 1965 hat CAS 6'699'392 verschiedene chemische Verbindungen registriert (Stand: 30. 6. 84); dazu kommen noch ca. 1,5 Mill. Verbindungen, die vor 1965 in der Literatur erschienen sind und z. Z. maschinell erfasst werden [6]. Wie kann man nun diese Informationsmengen noch mit vertretbarem Auf- wand bewaltigen? Als etwa Mitte des letzten Jahrhunderts die Zahl der Zeit- schriften so gross wurde, dass sie vom einzelnen Wissenschaftler nicht mehr uberschaubar war, entstanden die ersten Referatezeitschriften [1 a, 7]. Heute reicht dieses Instrument der Information in seiner gedruckten, manuell zu be- nutzenden Form nicht mehr aus. Die Verarbeitung und Speicherung von gro- ssen Datenmengen mit Computern liegt auf der Hand [8]. Die heute wichtigste Einsatzform des Computers zur gezielten (Wieder)gewinnung von Information (, Retrieval') durch den (End)benutzer ist die, Online-Recherche'.