Download Analytic Functions, Blazejewko 1982 by J. Lawrynowicz, J. Lodz PDF

By J. Lawrynowicz, J. Lodz

ISBN-10: 0387127127

ISBN-13: 9780387127125

Textual content: English, French

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Additional info for Analytic Functions, Blazejewko 1982

Example text

Example of reference-independent methods . . . . . . . . . . . . . . Reference-dependent techniques . . . . . . . . . . . . . . . . . . Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . Results and discussion .

The technical similarities between SNV and the original MSC have been commented on by Dhanoa et al. (1994) and will not be discussed here any further. In general, SNV and the original MSC lead to very similar results (both spectrally and regression-wise). 5 illustrates the application of SNV to the sugar data set. 4, it can be seen that the MSC and SNV corrected spectra are very similar, with the main difference being the ordinate axis scale. As in the case for MSC and EMSC, SNV has transformed the data in such a way that the corrected data generally have a more linear relationship between signal and concentration.

Principal component regression . . . . . . . . . . . . . . . . . . Partial least squares regression . . . . . . . . . . . . . . . . . . Artificial neural networks . . . . . . . . . . . . . . . . . . . . . Architecture of neural networks . . . . . . . . . . . . . . . . . . Transfer functions . . . . . . . . . . . . . . . . . . . . . . . Back-propagation learning rule . . . . . .

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