Rome Chemometry, Pr. G. Visco; what lies behind the Chemometrics or Chemometry name?

Rome University, La Sapienza Chemistry Department Rome, Italy, Europe	Dr. Giovanni Visco, Ott 2007 Chemiometria, analisi multivariata, clustering, pattern recognition, exploratory data analysis ...	Corso di Laurea in: Scienze Applicate ai Beni Culturali ed alla Diagnostica per la loro Conservazione Corso di laurea in: Chimica Ambientale
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Cosa si intende per Chemiometria?

what lies behind the Chemometrics or Chemometry name?

All chemometricians reply in different ways to this query. My purpose is not to write a new ones but I want to mention the "others". Here you can see three lists.

Taking back the preceding slide, I don't believe there is, all over the world, a degree course that can cover the contents of this slide, needs a definition of precedence keeping in mind of the necessary specialisation to the CC.HH. or Environment's students.

the indexes: from Brereton, from Hardy, from Massart

Prof. James K. Hardy From Hardy Research Group, Department of Chemistry, The University of Akron, Ohio, USA. A long list of slides (including those on Chemometry) is written in a simple language so that students can easily understand them. These slides, characterised by a correct language and few mistakes, soon began famous. The list is the first answer to our question.	Prof. D.L. Massart Taken by the last version of his book (composed by two volumes) and considered the Chemometry's bible. D.L. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics, Elsevier, 1997-1998
http://ull.chemistry.uakron.edu/chemometrics/ Introduction What is Chemometrics Complex samples Chemometric methods What are data Types of data Obtaining meaningful data Basic statistics Error, uncertainty and probability Normal distribution Large data sets Smaller data sets Univariate tools Pooled statistics Simple ANOVA Simple analysis of variance Confidence intervals The t test Do two means differ? The F test Rejection of data Outliers Huge errors Dixon test Grubbs test Experimental design Experimental Design Simple analysis of variance Two way analysis of variance Randomized blocks and ANOVA Latin squares Factorial design Simple modeling Types of models Linear regression Goodness of fit Correlation coefficient Data transformations Multiple linear regression Non-linear regression Signal detection Signals Limit of detection Precision Optimization Averaging Integration Filtering Multiplex spectroscopy Post collection Calibration Constructiong a calibration curve Linear modeling Linear models and uncertainty Detection limit, sensitivity and linear range Using the residuals Standard addition Exploration Complex samples Leverage Pattern recognition Pre-processing Translation & scaling Autoscaling Feature weighting Eigenvectors Hierarchical Cluster Analysis Distance and similarity Clustering Methods Dendrograms Examples So what's it good for? Principal Component Analysis PCA NIPALS Varimax rotation PCA of artifacts Classification of whiskey GC/MS data Other examples Classification Data sets Similarity classification Linear learning machine K nearest neighbor SIMCA Multivariate calibration Principal component regression Partial least squares regression Regression examples Neural networks Network components Learning in neural networks Backpropagation Dynamic learning vector quantization Self-organizing maps	Part. A: ISBN: 0-444-89724-0 Statistical Description of the Quality of Processes and Measurements Introductory concepts about chemical data Measurement of quality Quality of processes and statistical process control Quality of measurements in relation to quality of processes Precision and bias of measurements Some other types of error Propagation of errors Rounding and rounding errors The Normal Distribution Population parameters and their estimators Moments of a distribution: mean, variance, skewness The normal distribution: description and notation Tables for the standardized normal distribution Standard errors Confidence intervals for the mean Small samples and the t-distribution Normality tests: a graphical procedure How to convert a non-normal distribution into a normal one An Introduction to Hypothesis Testing Comparison of the mean with a given value Null and alternative hypotheses Using confidence intervals Comparing a test value with a critical value Presentation of results of a hypothesis test Level of significance and type I error Power and type II errors Sample size One- and two-sided tests An alternative approach: interval hypotheses Some Important Hypothesis Tests Comparison of two means Multiple comparisons ß error and sample size Comparison of variances Outliers Distribution tests Analysis of Variance One-way analysis of variance Assumptions Fixed effect models: testing differences between means of columns Random effect models: variance components Two-way and multi-way ANOVA Interaction Incorporation of interaction in the residual Experimental design and modelling Blocking Repeated testing by ANOVA Nested ANOVA Control Charts Quality control Mean and range charts Charts for attributes Moving average and related charts Further developments Straight Line Regression and Calibration Introduction Straight line regression Correlation References Vectors and Matrices The data table as data matrix Vectors Matrices Multiple and Polynomial Regression Introduction Estimation of the regression parameters Validation of the model Confidence intervals Multicollinearity Ridge regression Multicomponent analysis by multiple linear regression Polynomial regression Outliers Non-linear Regression Introduction Mechanistic modelling Empirical modelling Robust Statistics Methods based on the median Biweight and winsorized mean Iteratively reweighted least squares Randomization tests Monte Carlo methods Internal Method Validation Definition and types of method validation The golden rules of method validation Types of internal method validation Precision Accuracy and bias Linearity of calibration lines Detection limit and related quantities Sensitivity Selectivity and interferences Method Validation by Interlaboratory Studies Types of interlaboratory studies Method-performance studies Laboratory-performance studies Other Distributions Introduction-probabilities The binomial distribution The hypergeometric distribution The Poisson distribution The negative exponential distribution and the Weibull distribution Extreme value distributions The 2×2 Contingency Table Statistical descriptors Tests of hypothesis Principal Components Latent variables Score plots Loading plots Biplots Applications in method validation The singular value decompostion The resolution of mixtures by evolving factor analysis and the HELP method Principal component regression and multivariate calibration Other latent variable methods Information Theory Uncertainty and information An application to thin layer chromatography The information content of combined procedures Inductive expert systems Information theory in data analysis Fuzzy Methods Conventional set theory and fuzzy set theory Definitions and operations with fuzzy sets Applications Process Modelling and Sampling Introduction Measurability and controllability Estimators of system states Models for process fluctuations Measurability and measuring system Choice of an optimal measuring system: cost considerations Multivariate statistical process control Sampling for spatial description Sampling for global description Sampling for prediction Acceptance sampling An Introduction to Experimental Design Definition and terminology Aims of experimental design The experimental factors Selection of responses Optimization strategies Response functions: the model An overview of simultanous (factorial) designs Two-level Factorial Designs Terminology: a pharmaceutical technology example Direct estimation of effects Yates' method of estimating effects An example from analytical chemistry Significance of the estimated effects: visual interpretation Significance of the estimated effects: by using the standard deviation of the effects Significance of the estimated effects: by ANOVA Least squares modelling Artefacts Fractional Factorial Designs Need for fractional designs Confounding: example of a half-fraction factorial design Defining contrasts and generators Resolution Embedded full factorials Selection of additional experiments Screening designs Multi-level Designs Linear and quadratic response surfaces Quality criteria Classical symmetrical designs Non-symmetrical designs Response surface methodology Non-linear models Latin square designs Mixture Designs The sum constraint The ternary diagram Introduction to the Simplex design Simplex lattice and centroid designs Upper or lower bounds Upper and lower bounds Combining mixture and process variables Other Optimization Methods Introduction Sequential optimization methods Steepest ascent methods Multicriteria decision making Taguchi methods Genetic Algorithms and Other Global Search Strategies Introduction Application scope Principle of genetic algorithms Configuration of genetic algorithms Search behaviour of genetic algorithms Hybridization of genetic algorithms Example Applications, Simulated annealing Tabu search Part. B: ISBN: 0-444-82853-2 Introduction to Vectors and Matrices Vectors Matrices and Operations on Matrices Vector space Geometrical properties of vectors Matrices Matrix product Dimensions and rank Eigenvectors and eigenvalues Statistical interpretation of matrices Geometrical interpretation of matrix products Cluster Analysis Clusters Measures of (dis)similarity Clustering algorithms Analysis of Measurement Tables Introduction Principal components analysis Geometrical interpretation Preprocessing Algorithms Validation Principal coordinates analysis Non-linear principal components analysis PCA and cluster analysis Analysis of Contingency Tables Contingency table Chi-square statistic Closure Weighted metric Distance of chi-square Correspondence factor analysis Log-linear model Supervised Pattern Recognition Supervised and unsupervised pattern recognition Derivation of classification rules Feature of selection and reduction Validation of classification rules Curve and Mixture Resolution by Factor Analysis and Related Techniques Abstract and true factors Full-rank methods Evolutionary and local rank methods Pure column (or row) techniques Quantitative methods for factor analysis Application of factor analysis for peak purity check in HPLC Guidance for the selection of a factor analysis method Relations between Measurement Tables Introduction Procrustes analysis Canonical correlation analysis Multivariate least squares regression Reduced rank regression Partial least squares regression Continuum regression methods Concluding remarks Multivariate Calibration Introduction Calibration methods Validation Other aspects New developments Quantitative Structure-Activity Relationships (QSAR) Extrathermodynamic methods Principal components models Canonical variate models Partial least squares models Other approaches Analysis of Sensory Data Introduction Difference tests Multidimensional scaling The analysis of Quantitative Descriptive Analysis profile data Comparison of two or more sensory data sets Linking sensory data to instrumental data Temporal aspects of perception Production formulation Pharmacokinetic Models Introduction Compartmental analysis Non-compartmental analysis Compartment models versus non-compartmental analysis Linearization of non-linear models Signal Processing Signal domains Types of signal processing The Fourier transform Convolution Signal processing Deconvolution by Fourier transform Other transforms Kalman Filtering Introduction Recursive regression of a straight line Recursive multicomponent analysis System equations The Kalman filter Adaptive Kalman filtering Applications Applications of Operations Research An overview Linear programming Queueing problems Discrete event simulation A shortest path problem Artificial Intelligence: Expert and Knowledge Based Systems Artificial intelligence and expert systems Expert systems Structure of expert systems Knowledge representation The interference engine The interaction module Tools Developments of an expert system Conclusion Artificial Neural Networks Introduction Historical overview The basic unit - the neuron The linear learning machine and the perception network Multilayer feed forward (MLF) networks Radial basis function networks Kohonen networks Adaptive resonance theory networks
Prof. Richard Brereton From Bristol Chemometrics, Bristol University, U.K. The new book Applied Chemometrics for Scientists, J. Wiley & Sons (2007), ISBN: 0470016868. From one of the fathers of chemometrics the update of previous, 2003, book with more and more applications.
ISBN: 0-470-01686-8 Introduction Development of Chemometrics - Early Developments - 1980s and the Borderlines between Other Disciplines - 1990s and Problems of Intermediate Complexity - Current Developments in Complex Problem Solving Application Areas How to Use this Book Literature and Other Sources of Information Experimental Design Why Design Experiments in Chemistry? Degrees of Freedom and Sources of Error Analysis of Variance and Interpretation of Errors Matrices, Vectors and the Pseudoinverse Design Matrices Factorial Designs - Extending the Number of Factors - Extending the Number of Levels An Example of a Factorial Design Fractional Factorial Designs Plackett-Burman and Taguchi Designs The Application of a Plackett-Burman Design to the Screening of Factors Influencing a Chemical Reaction Central Composite Designs Mixture Designs - Simplex Centroid Designs - Simplex Lattice Designs - Constrained Mixture Designs A Four Component Mixture Design Used to Study Blending of Olive Oils Simplex Optimization Leverage and Confidence in Models Designs for Multivariate Calibration Statistical Concepts Statistics for Chemists Errors - Sampling Errors - Sample Preparation Errors - Instrumental Noise - Sources of Error Describing Data - Descriptive Statistics - Graphical Presentation - Covariance and Correlation Coefficient The Normal Distribution - Error Distributions - Normal Distribution Functions and Tables - Applications Is a Distribution Normal? - Cumulative Frequency - Kolmogorov-Smirnov Test - Consequences Hypothesis Tests Comparison of Means: the t-Test F-Test for Comparison of Variances Confidence in Linear Regression Linear Calibration - Example - Confidence of Prediction of Parameters More about Confidence - Confidence in the Mean - Confidence in the Standard Deviation Consequences of Outliers and How to Deal with Them Detection of Outliers - Normal Distributions - Linear Regression - Multivariate Calibration Shewhart Charts More about Control Charts - Cusum Chart - Range Chart - Multivariate Statistical Process Control Sequential Methods Sequential Data Correlograms - Auto-correlograms - Cross-correlograms - Multivariate Correlograms Linear Smoothing Functions and Filters Fourier Transforms Maximum Entropy and Bayesian Methods - Bayes' Theorem - Maximum Entropy - Maximum Entropy and Modelling Fourier Filters Peakshapes in Chromatography and Spectroscopy - Principal Features - Gaussians - Lorentzians - Asymmetric Peak Shapes - Use of Peak Shape Information Derivatives in Spectroscopy and Chromatography Wavelets Pattern Recognition Introduction - Exploratory Data Analysis - Unsupervised Pattern Recognition - Supervised Pattern Recognition Principal Components Analysis - Basic Ideas - Method Graphical Representation of Scores and Loadings - Case Study 1 - Case Study 2 - Scores Plots - Loadings Plots - Extensions Comparing Multivariate Patterns Preprocessing Unsupervised Pattern Recognition: Cluster Analysis Supervised Pattern Recognition - Modelling the Training Set - Test Sets, Cross-validation and the Bootstrap - Applying the Model Statistical Classification Techniques - Univariate Classification - Bivariate and Multivariate Discriminant Models - SIMCA - Statistical Output K Nearest Neighbour Method How Many Components Characterize a Dataset? Multiway Pattern Recognition - Tucker3 Models - PARAFAC - Unfolding Calibration Univariate Calibration - Classical Calibration - Inverse Calibration - Calibration Equations - Including Extra Terms - Graphs Multivariate Calibration and the Spectroscopy of Mixtures Multiple Linear Regression Principal Components Regression Partial Least Squares How Good is the Calibration and What is the Most Appropriate Model? - Autoprediction - Cross-validation - Test Sets - Bootstrap Multiway Calibration - Unfolding - Trilinear PLS1 - N-PLSM Coupled Chromatography Preparing the Data - Preprocessing - Variable Selection - Chemical Composition of Sequential Data Univariate Purity Curves Similarity Based Methods - Similarity - Correlation Coefficients - Distance Measures - OPA and SIMPLISMA Evolving and Window Factor Analysis - Expanding Windows - Fixed Sized Windowsv - Variations Derivative Based Methods Deconvolution of Evolutionary Signals Noniterative Methods for Resolution - Selectivity: Finding Pure Variables - Multiple Linear Regression - Principal Components Regression - Partial Selectivity Iterative Methods for Resolution Equilibria, Reactions and Process Analytics The Study of Equilibria using Spectroscopy Spectroscopic Monitoring of Reactions - Mid Infrared Spectroscopy - Near Infrared Spectroscopy - UV/visible Spectroscopy - Raman Spectroscopy - Summary of Main Data Analysis Techniques Kinetics and Multivariate Models for The Quantitative Study of Reactions Developments in The Analysis of Reactions using On-Line Spectroscopy - Constraints and Combining Information - Data Merging - Three-Way Analysis he Process Analytical Technology Initiative - Multivariate Tools for Design, Data Acquisition and Analysis - Process Analysers - Process Control Tools - Continuous Improvement and Knowledge Management Tools Improving Yields and Processes Using Experimental Designs Use of Statistical Designs for Improving the Performance of Synthetic Reactions Screening for Factors that Influence the Performance of a Reaction Optimizing the Process Variables Handling Mixture Variables using Simplex Designs - Simplex Centroid and Lattice Designs - Constraints More about Mixture Variables - Ratios - Minor Constituents - Combining Mixture and Process Variables - Models Biological and Medical Applications of Chemometrics Introduction - Genomics, Proteomics and Metabolomics - Disease Diagnosis - Chemical Taxonomy Taxonomy Discrimination - Discriminant Function - Combining Parameters - Several Classes - Limitations Mahalanobis Distance Bayesian Methods and Contingency Tables Support Vector Machines Discriminant Partial Least Squares Micro-organisms - Mid Infrared Spectroscopy - Growth Curves - Further Measurements - Pyrolysis Mass Spectrometry Medical Diagnosis using Spectroscopy Metabolomics using Coupled Chromatography and Nuclear Magnetic Resonance - Coupled Chromatography - Nuclear Magnetic Resonance Biological Macromolecules Sequence Alignment and Scoring Matches Sequence Similarity Tree Diagrams - Diagrammatic Representations - Dendrograms - Evolutionary Theory and Cladistics - Phylograms Phylogenetic Trees Multivariate Image Analysis Scaling image - Scaling Spectral Variables - Scaling Spatial Variables - Multiway Image Preprocessing Filtering and Smoothing the Image Principal Components for the Enhancement of image Regression of image Alternating Least Squares as Employed in Image Analysis Multiway Methods In Image Analysis Food Introduction - Adulteration - Ingredients - Sensory Studies - Product Quality - Image Analysis How to Determine the Origin of a Food Product using Chromatography Near Infrared Spectroscopy - Calibration - Classification - Exploratory Methods Other Information - Spectroscopies - Chemical Composition - Mass Spectrometry and Pyrolysis Sensory Analysis: Linking Composition to Properties - Sensory Panels - Principal Components Analysis - Advantages Varimax Rotation Calibrating Sensory Descriptors to Composition

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