Spectral match index

The second approach is one of correlation. A spectral match is calculated by comparing the slope generated from comparative data points on the sample and reference spectra. The cosine between the slopes is calculated and a spectral match index or correlation determined (Fig. 9.23). 

Fig. 9.24 Interpreting the meaning of spectral match index data.

Because almost all materials used in the pharmaceutical industry have NIR spectra, the use of NIR for assuring blend homogeneity may prove to be a valuable application. Ciurczak [24,25] reported some of the first work on this subject. His work involved the use of a fiber probe to collect spectra from various locations in the mixer. Spectral matching and principal component analysis (PCA) were used to measure how similar the powder mix in a particular portion of the blender was to a predetermined good, or complete, mix. The match index or PCA scores were plotted versus time to assess the optimal blending time. 

What this actually means is the nearer the spectral match value is to 1.000, the better the correlation. Correlations near 1.000 are generated from spectra that are similar but offset. The lower the index the poorer the correlation, through to -1.000 when the spectra are mirror images (Fig. 9.24). 

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. 

Mass Spectroscopy. A coUection of 125,000 spectra is maintained at Cornell University and is avaUable from John WUey Sons, Inc. (New York) on CD-ROM or magnetic tape. The spectra can be evaluated using a quaHty index algorithm (63,76). Software for use with the magnetic tape version to match unknowns is distributed by Cornell (77). The coUection contains aU avaUable spectral information, including isotopicaUy labeled derivatives, partial spectra, and multiple spectra of a single compound. 

When identifying an eluted compound by comparing its mass spectrum with a mass spectral library, false matches are frequent. If retention index is used as a second characteristic, false matches are reduced. 

However, other considerations are also involved if LB films are to compete eflFectively with materials such as lithium niobate and gallium arsenide. Molecular engineering will also be required to cater to needs such as phase matching, suitable spectral response and refractive index, good optical damage threshold, low scattering coefiScients, and mechanical stability before practical objectives can be accomplished. 

The analysis of essential oil components was performed by GC/MS analysis. Individual identifications were made by matching their spectra with those from mass spectral libraries (Wiley 275.L) and the identity of each component was confirmed by comparison of its Kovat s index (72) with those from hterature(73) (Table 111). Individual oil constituents were reported as relative percent of total essential oil. 

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