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. SmeyersVerbeke, Handbook of Chemometrics and Qualimetrics, Elsevier, 19971998

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
 Nonlinear 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
 Preprocessing
 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
 Selforganizing maps

Part. A: ISBN: 0444897240
 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 tdistribution
 Normality tests: a graphical procedure
 How to convert a nonnormal 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 twosided 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
 Oneway analysis of variance
 Assumptions
 Fixed effect models: testing differences between means of columns
 Random effect models: variance components
 Twoway and multiway 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
 Nonlinear 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
 Methodperformance studies
 Laboratoryperformance studies
 Other Distributions
 Introductionprobabilities
 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 simultaneous (factorial) designs
 Twolevel 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 halffraction factorial design
 Defining contrasts and generators
 Resolution
 Embedded full factorials
 Selection of additional experiments
 Screening designs
 Multilevel Designs
 Linear and quadratic response surfaces
 Quality criteria
 Classical symmetrical designs
 Nonsymmetrical designs
 Response surface methodology
 Nonlinear 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: 0444828532
 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
 Nonlinear principal components analysis
 PCA and cluster analysis
 Analysis of Contingency Tables
 Contingency table
 Chisquare statistic
 Closure
 Weighted metric
 Distance of chisquare
 Correspondence factor analysis
 Loglinear 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
 Fullrank 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 StructureActivity 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
 Noncompartmental analysis
 Compartment models versus noncompartmental analysis
 Linearization of nonlinear 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.
