Multivariate chemometric techniques neural networks

Acoustic emission power spectra are similar in many respects to optical spectra and are amenable to chemometric processing (multivariate analysis). Principal component analysis, partial least squares (PLS), neural networks, and qualitative techniques such as SIMCA (soft independent modeling of class analogy a pattern recognition technique) have been employed... 

In chemometrics, multivariate calibration methods provide a convenient way to determine several components in a mixture within one experimental step, without the tedious operation of separation of these components. The method of calculation usually used is PLS method. Artificial neural network is also often used especially when the data set exhibits obvious nonlinearity. But it is prone to overfitting. Therefore, several types of techniques have been developed to prevent overfitting. At the same time, support vector regression, as a method suitable to treat nonlinear data without serious overfitting, can be used as a new method of computation in multivariate calibration. An example of using SVR in multivariate calibration will be described as follows. 

Multivariate calibration has the largest number of applications of chemometric methods in routine analysis for instance it became a widely used technique in quantitative analy.sis of complex mixtures by IR or UV detectors, especially in food chemistry and environmental analytical chemistry. A number of textbooks, tutorials, and reviews " have been published in this field, and software is offered by the instrument manufacturers. Applications of multivariate calibration is very widespread, ranging for instance from the determination of moisture in mushrooms to research for non-invasive measurement techniques of glucose in human blood. The multivariate methods mostly applied are PLS, PCR. and recently also neural networks, Typical calibration models describe the relationship between a set of x-variables (UV or IR absorbances) and one y-variable (concentration of one substance) although it is possible to derive models that simultaneously predict a set of y-variables. 

Artificial Intelligence in Chemistry Chemometrics Multivariate View on Chemical Problems Combinatorial Chemistry Design of Compounds for Physical Methods Genetic and Evolutionary Algorithms Machine Learning Techniques in Chemistry Neural Networks in Chemistry Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Protein Folding and Optimisation... 

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