Hit quality index

Use mathematical methods, such as Hit quality index (HQI) value (or similarity value vs. library reference sample). An example list of such HQI methods includes... 

The results of data treatment are documented and evaluated in ES 5 and the interpretation in ES 6 is guided by the analyst s constraints and requirements. For instance, simple visual pattern comparisions may be acceptable for sample identification, or a combined database (GC-FTIR/GC-MS), (PGC/FTIR), (GC/TA), etc., analysis may be required. Judgmental decisions must be trained into the system as to depth of analysis, its acceptability and reliability (e.g., the hit quality index (HQI) of the MS search combined with that from the FTIR search may confirm within a 95% confidence level the GC peak or sample identity). 

The hit quality index (HQI) ranks the candidate reference spectra by their similarity to the unknown spectrum. As warned above it is not wise to take the hit list ranked by HQI at face value but to look down the hit list where it may well be possible to find what a human expert would regard as a better candidate solution to the search than the algorithm has picked out. 

Another often used algorithm for simple spectral matching is the vector correlation method. It is similar to the Euclidean distance method but does not require that the spectra be normalized. In this method, each spectrum is centered around the mean response value to calculate the hit quality index. 

Finally, the hit quality index does not provide any absolute measure of the probability that the sample actually is the same as the library sample. The arbitrary scale of the hit quality values (0-1) does not give a very good statistical measure of the similarity of the spectra. In short, using only a single training spectrum to represent all possible samples in the future does not give the analyst any statistical assurance that the spectra are truly the same or different. It provides only a relative measure for all the library samples. For anyone who has tried simple library search techniques for spectrally similar samples, this result is all too obvious. 

AU search algorithms have a goodness-of-fit or hit quality index metric to indicate which spectrum is the best match. In a true Euchdean distance metric, where the spectra are normalized to unity, the metric wiU report a value between zero and 1, where the value of zero represents a perfect match. Commercial software will often scale or adjust this number. For example, if the Euclidean distance is subtracted from 1 and the result multiplied by 1000, a perfect match gives rise to a hit quality index of 1000, and a complete mismatch gives a score of zero. Regardless, the algorithm to compare the spectra is virtually identical only the way the results are reported varies. 

Search systems are powerful tools, but as for most algorithms described in this chapter, care must be taken when they are used. The unknown and the hbrary spectra must both obey Beer s law. Care must be taken that the wavenumber calibration of an instrument is correct as wavenumber shifts may occur between the unknown and the hbrary spectra. The algorithms compensate for spectral shifts to some degree, but they cannot compensate for large shifts. In addition, the spec-troscopist must remember two important caveats (1) a hbrary search cannot identify an unknown unless the unknown is in the hbrary and (2) a high value of the hit quality index is not necessarily an indication of a correct match. The judicious spec-troscopist will visually compare the spectra of the unknown to the best matches to determine if they are indeed identical. In fact, the best test is to perform a spectral subtraction between the unknown and the hbrary match to see if there is a nonzero residual. If the two spectra are not identical, a nonzero subtraction will result. Finally, it should be recognized that spectral similarity does not indicate structural similarity. Certainly, some of the functional groups in structurally similar molecules will be the same, but the overall structure of an unknown and the best matches that result from a search may be quite different. 

Most commercial FTIR software packages come equipped with a library searching capability, or it can be purchased for an additional fee. When a spectral library search is performed, the unknown spectrum is compared to each spectrum in the libraries selected. Thus, if there are 1000 spectra in a library, 1000 comparisons are performed. As a result of each comparison a number called the hit quality index (HQI) is calculated, which is a numerical measure of the similarity between two spectra. In an ideal world the library search will turn up a spectrum similar to the unknown spectrum. It is then assumed the unknown sample is chemically similar to the library sample. This way known spectra can be used to identify unknown spectra. Library searching is so useful in identifying unknowns and interpreting mixture spectra that I encourage all FTIR users to have access to library searching capabilities. 

HQI = Hit Quality Index Libi = Absorbances in library spectrum Unki = Absorbances in unknown spectrum n = Number of data points in the spectrum i = Index of data points... 

Hit Quality Index (HQI) In library searching, the number that shows how closely matched a library spectrum is to a sample spectrum. 

Library Searching An automated process where an unknown spectrum is compared to a collection of known spectra kept in a spectral library. The comparison gives a number called the hit quality index, which represents how closely related two spectra are to each other. If a match is of high quality, it may be possible to identify an unknown. 

Search Algorithm In library searching, the mathematical calculation used to compare two spectra to produce a hit quality index. 

Search Report The end product of a library search. A search report ranks the quality of library matches using the hit quality index and then presents these results in a table. 

Technique 29, Section 29.11). You may need to search a computerized database to get the necessary information, or you may be able to find it in the Aldrich Catalog Handbook of Fine Chemicals, issued by the Aldrich Chemical Company. Current issues of this catalogue include listings of substances by CAS number. In your report, you should also report the relative percentage of the substance in the tablet extract. Finally, your instructor may also ask you to include the "quality" parameter from the "hit list." If possible, determine which components have antihistamine activity and which ones are present for another purpose. The Merck Index may provide this information. 

---