Self-Weighted Correlation Coefficients and Their Application to Measure Spectral Similarity

Authors: Griffiths, Peter R.1; Shao, Limin2

Source: Applied Spectroscopy, Volume 63, Issue 8, Pages 184A-214A and 859-978, (August 2009) , pp. 916-919(4)

Publisher: Society for Applied Spectroscopy

Buy & download fulltext article:

OR

Price: $29.00 plus tax (Refund Policy)

Abstract:

A technique for spectral searching with noisy data is described that improves the performance over contemporary approaches. Instead of simply calculating the correlation coefficient between the spectrum of an unknown and a series of reference spectra, greater weight is given to the more intense features in the reference spectra. The weight array, w, is given by |r|/{1 + d}, where the vector r represents the reference spectrum and the difference vector, d, contains the difference between the sample and reference data points, equal to |s − kr|, where k is a scaling factor that eliminates the effect of signal strength. By this approach, a large weight is only given to those points that have relatively high absorbance and are close to their counterparts in the reference spectrum. This technique was shown to give significantly improved performance when applied to noisy spectra of trace atmospheric components obtained by target factor analysis.

Keywords: SPECTRAL SEARCHING; TARGET FACTOR ANALYSIS; TFA; WEIGHTED CORRELATION COEFFICIENT

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/000370209788964575

Affiliations: 1: Department of Chemistry, University of Idaho, Moscow, Idaho 83844-2343 2: Department of Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, P.R. China

Publication date: August 1, 2009

More about this publication?
Related content

Tools

Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content

Text size:

A | A | A | A
Share this item with others: These icons link to social bookmarking sites where readers can share and discover new web pages. print icon Print this page