Quantitative evaluation of library searching performance

Abstract

A procedure Is descrlbed for the quantltatlve evaluatlon of the performance of a llbrary searchlng system employed for molecular structure elucldatlon. The technlque allows the evaluatlon of any form of spectral representation or any spectral comparison metrlc. The approach Is applicable to any type of spectrometry used for llbrary searchlng (LS) or to comblnatlons of different types of spectra. All comparisons are made relative to a chosen standard of quallty for library searching performance. I t Is assumed that the results of searchlng a llbrary of full-Intensity, full-resolution spectra by a least-squares metrlc will provlde an acceptable standard for comparlson. Results are presented to demonstrate the utlllty of thls procedure for the examlnatlon of alternatlves for llbrary searching of vapor-phase Infrared spectra.Library searching (LS) has been used effectively for both the identification and interpretation of spectra. The continued interest in instruments such as the gas chormatography/ Fourier transform infrarled spectrometer (GC/FTIR) underscores the need for an objective approach to the development of versatile and efficient LS systems. The tremendous quantity of data which can be generated in a single GC/FTIR experiment necessitates computer-assisted data processing.Consequently, 1,s systems are included in most commercial GC/FTIR instruments.Two important choices must be made in the design of a library searching system: (1) the manner in which the spectra are to be processed prior to storage in the reference library, and (2) the comparison >metric to be used during the search. A diversity of options for each of these has appeared in the literature (1-9) but it is not clear what combination of options would provide the best L S system for a given application. As a result, the development of LS systems is typically approached empirically. Various possibilities are tried until "satisfactory" porformance is obtained. Commercial FTIR search systems reflect this problem, in that they generally offer a number of options for spectral processing and choice of search metric, with little guidance as to how to design an optimal searching system.The need for a quantitative evaluation of library searching systems has received some attention in the literature. LS systems designed for th,e identification of spectra are most easily treated. Here the goal is unambiguous. The search system succeeds if it gets the right answer. Quality for LS systems of this sort has been expressed as the percentage of correctly identified spectra (10).For interpretive LS systems, the problem is more complex. These systems are designed to give useful information for spectal interpretation even when the unknown spectrum is not present in the refer~ence library. The entire "hit-list" of closest matches thus becomes important in determining quality. The most cominon approach to assessing quality in such systems has been by subjective visual inspection of hit-lists (11). Such "evaluation by acclamation" is too qualitative to provide a soun...

Effect of noise on spectral library searching performance

“…The qualitative evaluation of library searching performance (QELS) (17)(18)(19) was used to evaluate the search performance of the five noise replicates of each of the 20 test spectra (a total of 100 spectra) at each of the 11 S/N levels. The standards used with QELS were generated from library-1842 using the dot product metric.…”

“…Quantitative Evaluation of Library Searching Performance (QELS). A generally applicable quantitative approach to LS performance evaluation has been developed in this laboratory (18). In QELS, the performance of any combination of library spectra representation and comparison metric is compared against the performance of any chosen LS standard.…”

“…Previous uses of QELS (17)(18)(19) involved evaluating the utility of a compressed library representation or of a comparison metric. This study is slightly different in that we are using QELS to evaluate the quality of unknown spectra needed for acceptable LS performance.…”

Rapid Identification of Atmospheric Gaseous Pollutants Using Fourier-Transform Infrared Spectroscopy Combined with Independent Component Analysis

“…e identification of each component in the FTIR spectrum is crucial for accurate quantitative analysis. In the early stage of FTIR spectrum identification, library searching (LS) methods [18] were adopted when the spectral components were single or the absorption peaks rarely overlapped. Lavine et al [19] used pattern recognition methods as the search prefilters for library searching and made the identification more precise and faster.…”

Optimization of a similarity metric for library searching of highly compressed vapor-phase infrared spectra

“…The quantitative approach for LS performance evaluation, developed in this laboratory, is described in detail elsewhere. 14 In QELS, the performance of any combination of library spectra representation and comparison metric is compared against an LS standard. In the present case, we employ full-resolution spectra and the Euclidean distance comparison metric as our standard.…”