紅外譜檢索策略_歸一化及歐幾里得距離與首次衍生算法

Search Strategies for IR Spectra - Normalization and Euclidean Distance vs. First Derivative Algorithm

Bio-Rad's purpose in this note is to try to help you with your selection of algorithms and interpretation of your results. This Application Note will explain the basics of normalizing spectra and examine the difference between the Euclidean Distance, and the First Derivative search algorithms.

Summary

If  your  unknown  spectrum  has  a  flat  baseline  near  zero absorbance units, then a Euclidean algorithm would be a good first choice. If your unknown has a sloping baseline, then either correct the baseline before searching or use the First Derivative algorithm.

The Euclidean Distance algorithm is essentially trying to match areas under the curve. It does not have any knowledge of what is  spectroscopically  significant.  This  leads  to  bands  with  a broad area like the OH stretch, being weighted very heavily in the search due to its large area, while a sharp band such as a CoN may be ignored because of its small area compared to the rest of the spectrum.