Author: Harald Martens
Publisher: John Wiley & Sons
Release Date: 2001-02-08
Data analysis is a vital part of science today, and in assessing quality, multivariate analysis is often necessary in order to avoid loss of essential information. Martens provides a powerful and versatile methodology that enables researchers to design their investigations and analyse data effectively and safely, without the need for formal statistical training. * Offers an introductory explanation of multivariate analysis by graphical 'soft modelling' * Minimises mathematics, providing all technical details in the appendix * Presents itself in an accessible style with cartoons, self-assessment questions and a wide range of practical examples * Demonstrates the methodology for various types of quality assessment, ranging from human quality perception via industrial quality monitoring to environmental quality and its molecular basis All data sets available FREE online on "Chemometrics World" (http://www.wiley.co.uk/wileychi/chemometrics)
Author: Harald Martens
Publisher: John Wiley & Sons
Release Date: 1992-08-07
Multivariate Calibration Harald Martens, Chemist, Norwegian Food Research Institute, Aas, Norway and Norwegian Computing Center, Oslo, Norway Tormod Naes, Statistician, Norwegian Food Research Institute, Aas, Norway The aim of this inter-disciplinary book is to present an up-to-date view of multivariate calibration of analytical instruments, for use in research, development and routine laboratory and process operation. The book is intended to show practitioners in chemistry and technology how to extract the quantitative and understandable information embedded in non-selective, overwhelming and apparently useless measurements by multivariate data analysis. Multivariate calibration is the process of learning how to combine data from several channels, in order to overcome selectivity problems, gain new insight and allow automatic outlier detection. Multivariate calibration is the basis for the present success of high-speed Near-Infrared (NIR) diffuse spectroscopy of intact samples. But the technique is very general: it has shown similar advantages in, for instance, UV, Vis, and IR spectrophotometry, (transmittance, reflectance and fluorescence), for x-ray diffraction, NMR, MS, thermal analysis, chromatography (GC, HPLC) and for electrophoresis and image analysis (tomography, microscopy), as well as other techniques. The book is written at two levels: the main level is structured as a tutorial on the practical use of multivariate calibration techniques. It is intended for university courses and self-study for chemists and technologists, giving one complete and versatile approach, based mainly on data compression methodology in self-modelling PLS regression, with considerations of experimental design, data pre-processing and model validation. A second, more methodological, level is intended for statisticians and specialists in chemometrics. It compares several alternative calibration methods, validation approaches and ways to optimize the models. The book also outlines some cognitive changes needed in analytical chemistry, and suggests ways to overcome some communication problems between statistics and chemistry and technology.
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra. The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.
Author: Kai Yang
Publisher: McGraw Hill Professional
Release Date: 2004-03-17
Genre: Technology & Engineering
Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. * Graphical multivariate data display * Multivariate regression and path analysis * Multivariate process control charts * Six sigma and multivariate statistical methods
Environmental Monitoring theme is a component of Encyclopedia of Environmental and Ecological Sciences, Engineering and Technology Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. The Theme on Environmental Monitoring is largely concerned with strategies in the preparation of environmental impact assessments, as well as in many circumstances in which human activities carry a risk of harmful effects on the natural environment.. All monitoring strategies and programmes on environment have reasons and justifications which are often designed to establish the current status of an environment or to establish trends in environmental parameters. The content of the Theme provides the essential aspects and a myriad of issues that are great relevance to our world with respect to environmental monitoring. These two volumes are aimed at the following five major target audiences: University and College Students Educators, Professional Practitioners, Research Personnel and Policy Analysts, Managers, and Decision Makers and NGOs
Author: Fidel Toldr?
Publisher: John Wiley & Sons
Release Date: 2014-10-27
Genre: Technology & Engineering
Fermented meat products have been consumed for centuries in many different parts of the world and constitute one of the most important groups of food. Bacterial cultures are used in their manufacture to preserve the meat and confer particular textures and sensory attributes. Examples of fermented meats include salami, chorizo, pepperoni and saucisson. This fully revised and expanded reference book on meat fermentation presents all the principle fermented meat products and the processing technologies currently used in their manufacture. The 54 chapters of this substantial book are grouped into the following sections: Meat fermentation worldwide: overview, production and principles Raw materials Microbiology and starter cultures for meat fermentation Sensory attributes Product categories: general considerations Semidry-fermented sausages Dry-fermented sausages Other fermented meats and poultry Ripened meat products Biological and chemical safety of fermented meat products Processing sanitation and quality assurance There are five new chapters in the second edition that address the following topics: Smoking and new smoke flavourings; Probiotics; Methodologies for the study of the microbial ecology in fermented sausages; Low sodium in meat products; and Asian sausages. Handbook of Fermented Meat and Poultry, Second Edition provides readers with a full overview of meat fermentation, the role of microorganisms naturally present and/or added as starter cultures, safety aspects and an account of the main chemical, biochemical, physical and microbiological changes that occur in processing and how they affect final quality. Finally, readers will find the main types of worldwide fermented meat products, typically produced in different areas, with the description of their main characteristics.
This comprehensive text introduces readers to the most commonly used multivariate techniques at an introductory, non-technical level. By focusing on the fundamentals, readers are better prepared for more advanced applied pursuits, particularly on topics that are most critical to the behavioral, social, and educational sciences. Analogies between the already familiar univariate statistics and multivariate statistics are emphasized throughout. The authors examine in detail how each multivariate technique can be implemented using SPSS and SAS and Mplus in the book’s later chapters. Important assumptions are discussed along the way along with tips for how to deal with pitfalls the reader may encounter. Mathematical formulas are used only in their definitional meaning rather than as elements of formal proofs. A book specific website - www.psypress.com/applied-multivariate-analysis - provides files with all of the data used in the text so readers can replicate the results. The Appendix explains the data files and its variables. The software code (for SAS and Mplus) and the menu option selections for SPSS are also discussed in the book. The book is distinguished by its use of latent variable modeling to address multivariate questions specific to behavioral and social scientists including missing data analysis and longitudinal data modeling. Ideal for graduate and advanced undergraduate students in the behavioral, social, and educational sciences, this book will also appeal to researchers in these disciplines who have limited familiarity with multivariate statistics. Recommended prerequisites include an introductory statistics course with exposure to regression analysis and some familiarity with SPSS and SAS.
This book provides an introduction to the analysis of multivariate data. It should be suitable for statisticians and other research workers who are familiar with basic probability theory and elementary inference, and also have a basic grounding in matrix algebra. The book should also be suitable as a text for undergraduate and postgraduate statistics courses on multivariate analysis. The book covers a wider range oftopics than some other books in this area. It deals with preliminary data analysis, principal component and factor analysis and traditional normal-theory material. It also covers cluster analysis and scaling techniques. In writing the book, we have tried to provide a reasonable blend of theory and practice, in contrast to much of the existing literature which concentrates on one or other aspect. Enough theory is given to introduce the concepts and to make the topics mathematically interesting. But we also discuss the use (and misuse) of the techniques in practice and present appropriate real-life examples. Although the book is primarily an introductory text, we have nevertheless added appropriate references for further reading, so that the reader may extend his studies if he wishes.
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.
This volume presents an exposition of topics in industrial statistics. It serves as a reference for researchers in industrial statistics/industrial engineering and a source of information for practicing statisticians/industrial engineers. A variety of topics in the areas of industrial process monitoring, industrial experimentation, industrial modelling and data analysis are covered and are authored by leading researchers or practitioners in the particular specialized topic. Targeting the audiences of researchers in academia as well as practitioners and consultants in industry, the book provides comprehensive accounts of the relevant topics. In addition, whenever applicable ample data analytic illustrations are provided with the help of real world data.
Author: Hans-Hermann Bock
Publisher: Springer Science & Business Media
Release Date: 2004-04-20
Genre: Business & Economics
This volume contains a selection of papers presented during the biennial meeting of the CLAssification and Data Analysis Group (CLADAG) of the Societa Italiana di Statistica which was orga nized by the Istituto di Statistica of the Universita degli Studi di Palermo and held in the Palazzo Steri in Palermo on July 5-6, 2001. For this conference, and after checking the submitted 4 page abstracts, 54 papers were admitted for presentation. They covered a large range of topics from multivariate data analysis, with special emphasis on classification and clustering, computa tional statistics, time series analysis, and applications in various classical or recent domains. A two-fold careful reviewing process led to the selection of 22 papers which are presented in this vol ume. They convey either a new idea or methodology, present a new algorithm, or concern an interesting application. We have clustered these papers into five groups as follows: 1. Classification Methods with Applications 2. Time Series Analysis and Related Methods 3. Computer Intensive Techniques and Algorithms 4. Classification and Data Analysis in Economics 5. Multivariate Analysis in Applied Sciences. In each section the papers are arranged in alphabetical order. The editors - two of them the organizers of the CLADAG confer ence - would like to express their gratitude to the authors whose enthusiastic participation made the meeting possible and very successful.