Matching Algorithm for

Xu, J. (1996) GMA a generic match algorithm for structural homomorphism, isomorphism, and maximal common substructure match and its applications./. Ghem. Inf. Gomput. Sci, 36, 25-34. 

Johnson, K.J. Wright, B.W. Jarman, K.H. Synovec, R.E. (2003). High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. Journal of Chromatography A, Vol.996, No.1-2, (May 2003), pp. 141-155, ISSN 0021-9673... 

Raymond JW, Willett P. Maximum common subgraph isomorphism algorithms for the matching of chemical structures. J Comput-Aided Mol Des 2002 16 521-33. 

Summarizing, the Procrustes matching problem for two configurations X and Y can be solved with the following algorithm ... 

The extent of homogeneous mixing of pharmaceutical components such as active drug and excipients has been studied by near-IR spectroscopy. In an application note from NIRSystems, Inc. [47], principal component analysis and spectral matching techniques were used to develop a near-IR technique/algorithm for determination of an optimal mixture based upon spectral comparison with a standard mixture. One advantage of this technique is the use of second-derivative spectroscopy techniques to remove any slight baseline differences due to particle size variations. 

Several algorithms are available for the analysis of MS/MS spectra including SEQUEST, MASCOT, and X Tandem among others. Note that additional secondary quality control of assessment of MS/MS data has recently been implemented to assess identification probabilities and false positivity rates. The MS/MS spectra from an experiment can be interrogated against a concatenated forward and reverse database and an assessment of the intrinsic error rate of the data set can be made. Other approaches for secondary analysis of matching scores for peptide sequencing data include XCorr score normalization routines that are independent of peptide and database size.33... 

Of course one may employ automated library searches ( library percent reports ) to check for compound identities, but algorithms for library matching are not infallible, and mass spectral libraries are not exhaustive, thus some compounds of interest will likely not be identified. Additional dilemmas are presented by mere reliance on retention times and library percent reports to ascertain the presence of common or unique peaks from among multiple mass spectral data files. As illustrated in Table 2.1, the TICs from the GC-MS of urine from four elephants evidence a peak at essentially the same retention time, but the library search results are inconclusive as to their common identity or lack thereof. As will be seen below, our novel macros can assist in making such decisions for a large number of peaks. 

Gotoh, O. (1982). An improved algorithm for matching biological sequences./ Mol. Biol. 162, 705-708. 

Sphere-matching algorithm. Systematic search for conformational, preferences... 

---