Statistical Design - Chemometrics


  • Roy Bruns, Instituto de Quimica, Universidade Estadual de Campinas, Campinas, SP, Brazil
  • Ieda Scarminio, Departamento de Quimica, Universidade Estadual de Londrina, Londrina, Brazil
  • Benicio de Barros Neto, Departamento de Quimica Fundamental, Universidade Federal de Pernambuco, Recife, Brazil

Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study of theoretical principles. Forty two cases studies with real data are presented showing designs and the complete statistical analyses for problems in the areas chromatography, electroanalytical and electrochemistry, calibration, polymers, gas adsorption, semiconductors, food technology, biotechnology, photochemistry, catalysis, detergents and ceramics. These studies serve as a guide that the reader can use to perform correct data analyses.
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-Researchers in the chemical, pharmaceutical, food and related industries -3rd & 4th year undergraduate students and graduate university students in physical science, biological science, chemical and mechanical engineers, food technology, and environmental science


Book information

  • Published: January 2006
  • Imprint: ELSEVIER
  • ISBN: 978-0-444-52181-1


"The authors are academic chemists from various universities in Brazil, and indeed the book is a translation of an original text in Portuguese. The quality of the translation is excellent; very clear and readable expositions are provided for all concepts. The pain of learning the statistics is ameliorated by the authors' almost whimsical style. All in all, a very competent and thorough introduction to this important area."
Derek Robinson, Pontypool, UK, Vol. 10, No. 5, 2006, ORGANIC PROCESS RESEARCH AND DEVELOPMENT

Table of Contents

1. How statistics can help
2. When the situation is normal
3. Changing everything at the same time
4. When there are many variables
5. Empirical model-building
6. Exploring the response surface
7. Mixtures modeling
8. Simplex optimization