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Data analysis is a vital part of science today, and in assessingquality, multivariate analysis is often necessary in order to avoidloss of essential information. Martens provides a powerful andversatile methodology that enables researchers to design theirinvestigations and analyse data effectively and safely, without theneed for formal statistical training. * Offers an introductory explanation of multivariate analysis bygraphical 'soft modelling' * Minimises mathematics, providing all technical details in theappendix * Presents itself in an accessible style with cartoons,self-assessment questions and a wide range of practicalexamples * Demonstrates the methodology for various types of qualityassessment, ranging from human quality perception via industrialquality monitoring to environmental quality and its molecularbasis All data sets available FREE online on "Chemometrics World"(http://www.wiley.co.uk/wileychi/chemometrics)
Multivariate Calibration Harald Martens, Chemist, Norwegian Food Research Institute, Aas, Norway and Norwegian Computing Center, Oslo, Norway Tormod Næs, 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.
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 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.
Master Statistical Quality Control using JMP ! Using examples from the popular textbook by Douglas Montgomery, Introduction to Statistical Quality Control: A JMP Companion demonstrates the powerful Statistical Quality Control (SQC) tools found in JMP. Geared toward students and practitioners of SQC who are using these techniques to monitor and improve products and processes, this companion provides step-by-step instructions on how to use JMP to generate the output and solutions found in Montgomery’s book. The authors combine their many years of experience as passionate practitioners of SQC and their expertise using JMP to highlight the recent advances in JMP’s Analyze menu, and in particular, Quality and Process. Key JMP platforms include: Control Chart Builder CUSUM Control Chart Control Chart (XBar, IR, P, NP, C, U, UWMA, EWMA, CUSUM) Process Screening Process Capability Measurement System Analysis Time Series Multivariate Control Chart Multivariate and Principal Components Distribution For anyone who wants to learn how to use JMP to more easily explore data using tools associated with Statistical Process Control, Process Capability Analysis, Measurement System Analysis, Advanced Statistical Process Control, and Process Health Assessment, this book is a must!
According to European legislation, extra virgin is the top gradeof olive oils. It has a superior level of health properties andflavour compared to virgin and refined olive oils. Mediterraneancountries still produce more than 85% of olive oil globally, butthe constant increase of demand for extra virgin olive oil has ledto new cultivation and production in other areas of the world,including California, Australia, China, South Africa and SouthAmerica. At the same time, olive oil’s sensory properties andhealth benefits are increasingly attracting the attention andinterest of nutritionists, food processors, manufacturers and foodservices. Progress and innovation in olive cultivation, harvestingand milling technologies as well as in oil handling, storage andselling conditions make it possible to achieve even higher qualitylevels than those stipulated for extra virgin oils. As aconsequence, a new segment – excellent extra virgin oliveoils – is increasingly attracting the attention of the marketand earning consumers’ preference. The Extra-Virgin Olive Oil Handbook provides a completeaccount of olive oil’s composition, health properties,quality, and the legal standards surrounding its production. Thebook is divided into convenient sections focusing on extra virginolive oil as a product, the process by which it is made, and theprocess control system through which its quality is assured. Anappendix presents a series of tables and graphs with useful data,including conversion factors, and the chemical and physicalcharacteristics of olive oil. This book is aimed at people involved in the industrial productionas well as in the marketing and use of extra virgin olive oil whoare looking for practical information, which avoids overly academiclanguage, but which is still scientifically and technically sound.The main purpose of the handbook is to guide operators involved inthe extra virgin olive oil chain in making the most appropriatedecisions about product quality and operating conditions in theproduction and distribution processes. To these groups, the mostimportant questions are practical ones of why, how, how often, howmuch will it cost, and so on. The Extra-Virgin Olive OilHandbook will provide the right answers to these key practicalconsiderations, in a simple, clear yet precise and up-to-dateway.

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