Rather than overshadowing the concepts with theoretical background, this book uses application-oriented examples to illustrate how chemometrics techniques can be applied to complex scenarios with multiple and dynamic variables. The book presents a diverse selection of topics that include sampling, modeling, experimental design, calibration, pattern recognition, data analysis techniques, algorithms, and error. This second edition has been completely revised to feature new chapters on principal component analysis, self-modeling curve resolution, and multi-way analysis methods. It includes expanded material on normal distributions, sampling theory, signal processing, and digital filtering.
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