Chemometric data analysis

FFT spectra contain an abundance of potential information (as a function of frequency) related to the process/products characterized by the acoustic sensors. FFT spectra constitute fit for purpose input to any chemometric data analytical method deemed beneficial for making the final model relating the acoustic emissions (vibrations), X, to the salient functional properties (quality, quantity) of the products or process 

From the outset acoustic chemometrics is fully dependent upon the powerful ability of chemometric full spectrum data analysis to elucidate exactly where in the spectral range (which frequencies) the most influential information is found. The complete suite of chemometric approaches, for example PCA, PLS regression, SIMCA (classification/discrimination) are at the disposition of the acoustic spectral data analyst there is no need here to delve further into this extremely well documented field. (See Chapter 12 for more detail.) 

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Brereton, R.G. (2003) Chemometrics Data Analysis for the Laboratory and Chemical Plant, Wiley, Chichester, UK. 

Recently, introductory books about chemometrics have been published by R. G. Brereton, Chemometrics—Data Analysis for the Laboratory and Chemical Plant (Brereton 2006) and Applied Chemometrics for Scientists (Brereton 2007), and by M. Otto, Chemometrics—Statistics and Computer Application in Analytical Chemistry (Otto 2007). Dedicated to quantitative chemical analysis, especially using infrared spectroscopy data, are A User-Friendly Guide to Multivariate Calibration and Classification (Naes et al. 2004), Chemometric Techniques for Quantitative Analysis (Kramer 1998), Chemometrics A Practical Guide (Beebe et al. 1998), and Statistics and Chemometrics for Analytical Chemistry (Miller and Miller 2000). 

Barnes, R. J., Dhanoa, M. S., Lister, S. J. Appl. Spectrosc. 43,1989, 772-777. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Barnes, R. J., Dhanoa, M. S., Lister, S. J. J. Near Infrared Spectrosc. 1, 1993, 185-186. Correction of the description of standard normal variate (SNV) and De-Trend transformations in practical spectroscopy with applications in food and beverage analysis. Brereton, R. G. Chemometrics—Data Analysis for the Laboratory and Chemical Plant. Wiley, Chichester, United Kingdom, 2006. 

A comparison of Figures 9.18 and 9.19 shows that the acoustic chemometric approach is much more sensitive to changes in the process state(s) of the fluidized bed than the traditional process data alone. Of course an industrial implementation of this process monitoring facility would include both acoustic data and process data, together with relevant chemometric data analysis (PCA, PLS) and the resulting appropriate plots. 

Y. Liu, B.R. Upadhyaya and M. Naghedolfeizi, Chemometric data analysis using artificial neural networks, Appl. Spectrosc., 47, 12-23 (1993). 

Engineered variants of enzymes could be another approach in biosensor design for the discrimination and detection of various enzyme-inhibiting compounds when used in combination with chemometric data analysis using ANN. The crucial issues that should be addressed in the development of new analytical methods are the possibility of simultaneous and discriminative monitoring of several contaminants in a multi-component sample and the conversion of the biosensing systems to marketable devices suitable for large-scale environmental and food applications. 

Chemometrics Data Analysis for the Laboratory and Chemical Plant. 

Chemometrics data analysis for the laboratory and chemical plant / Richard Brereton. p. cm. 

It is generally known that the repeatability of CE analyses is not optimal due to irreproducible flow rates (37). Therefore, it is recommended to align the corresponding peaks in the different electropherograms before chemometric data analysis (exploration or classification) is started. This alignment results in a data matrix, where the signals of the corresponding peaks of the... 

This investigation has demonstrated that NIR spectroscopy, coupled with fiberoptics and chemometric data analysis, is ideal for real-time prediction of the main component PVC and two additives, an impact-resistant modifier and a lubricant, in industrial PVC melt compounds. The robustness of the calibration model was tested by using data from different days and also during real-time in-line predictions of real world samples. The results were promising for implementation in polymer manufacturing plants. 

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