Regression atomic spectroscopy

Xiaoning et al. have proposed a methodology to linearize atomic spectroscopy regression curves by using a transformation of the form Y = Yp. The value of p is determined by minimizing the quality coefficient [17]. Excellent results have been obtained by minimizing the sum of the squared residuals (MIN 2(T, — X,)2 ). This approach can even be used for standard addition curves. 

Finally, Chapter 6 goes into two new regression paradigms artificial neural networks and support vector machines. Quite different from the other regression methods presented in the book, they are gaining acceptance because they can handle non-linear systems and/or noisy data. This step forward is introduced briefly and, once more, a review is presented with practical applications in the atomic spectroscopy field. Not surprisingly, most papers deal with complex measurements [e.g. non-linear calibration or... 

C. Schierle and M. Otto, Comparison of a neural network with multiple linear regression for quantitative analysis in ICP-atomic emission spectroscopy, Fresenius J. Anal. Chem., 344(4-5), 1992, 190-194. 

A regression analysis has been performed (45) on the chemical shifts of some azine-N-oxides in terms of 10 parameters which include interactions between the N-0 moiety and the nitrogen atoms at positions 2, 3, and 4 in the six-membered ring. Effects due to additional fused rings in the system and corrections for interactions between pyridine-type nitrogen atoms are also included. The additivity rules for the shifts are then used to predict those for a number of azine N-oxides which have been hitherto unknown or not examined by nitrogen NMR spectroscopy. (45)... 

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