Introduction to Part B. 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. References.
Cluster Analysis. Clusters. Measures of (dis)similarity. Clustering algorithms. References.
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. References.
Analysis of Contingency
Tables. Contingency table. Chi-square statistic. Closure. Weighted metric. Distance of chi-square. Correspondence factor analysis.
Log-linear model. References.
Supervised Pattern Recognition. Supervised and unsupervised pattern recognition. Derivation of classification
rules. Feature of selection and reduction. Validation of classification rules. References.
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. References.
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. References.
Multivariate Calibration. Introduction. Calibration methods. Validation. Other
aspects. New developments. References.
Quantitative Structure-Activity Relationships (QSAR). Extrathermodynamic methods. Principal
components models. Canonical variate models. Partial least squares models. Other approaches. References.
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.
References.
Pharmacokinetic Models. Introduction. Compartmental analysis. Non-compartmental analysis. Compartment models versus
non-compartmental analysis. Linearization of non-linear models. References.
Signal Processing. Signal domains. Types of signal
processing. The Fourier transform. Convolution. Signal processing. Deconvolution by Fourier transform. Other transforms. References.
Kalman Filtering. Introduction. Recursive regression of a straight line. Recursive multicomponent analysis. System equations.
The Kalman filter. Adaptive Kalman filtering. Applications. References.
Applications of Operations Research. An overview. Linear
programming. Queueing problems. Discrete event simulation. A shortest path problem. References.
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. References.
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. References.
Reviews Chenometrics & Intelligent Laboratory Systems, Vol. 49, 1999 "I have caused my university library to purchase a copy of Handbook of Chenometrics and Qualimetrics, and I believe that
all serious groups working in chenometrics will feel the need to own the two volumes...For those long ensconced in chenometrics, browsing
these books will not fail to impress with the breadth and complexity of the subject."
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