Analyte signature pattern

After preprocessing the data, the search begins to find the features in the data that show the largest differences between patterns. This process is very much dependent on the type of detectors in the detection system but may involve comparing the relative amplitudes of the different detectors in the array, the derivative of the response, or even mathematical transforms of the data to select which features show the most differentiation between the patterns of different analytes. For our examples described in Section 5.3., we chose to use 120-data point analyte signature patterns sampled from the entire shape of the signal from a detector array of four cantilevers with coatings described in Section 3.5. 

For the purpose of analyte identification, the response of the detector array to exposure to the analyte vapor must be recorded, and a unique pattern or chemical signature for each analyte must be extracted from the data. A good signature pattern highlights the characteristic features of the data that yield high differentiation between patterns and eliminates those features that provide little or no differentiation information. 

In Fig. 8 are shown the chemical signature patterns for five known analytes and one unknown analyte. The patterns were extracted from the response of an array of four coated microcantilevers to exposure to each analyte vapor. The four cantilever coatings... 

Fig. 8. Analyte chemical signature patterns. The inset at the top of the Figure shows the temporal response of one of the four cantilevers in the detector array. The responses for all four detectors were recorded simultaneously, and the signature pattern for each analyte was created by combining the four responses...

The third noteworthy feature of near-infrared spectra presented in Figure 13.3 is the uniqueness of the spectral patterns for each analyte. Although the spectral features are highly overlapping, the spectrum for glucose is notably unique relative to the others. The uniqueness of each spectrum provides the selectivity that is required for sound analytical measurements. However, the extensive overlap dictates that an analysis of the full spectrum is needed to extract the unique spectral signature for the targeted analyte relative to the sample matrix. Powerful multivariate methods are available for this purpose, as described in Chapter 12. 

Another example that uses the global response of dynamic libraries to a template is the application of DCLs as sensors [29-32]. The idea is that the pattern of change in the concentration of aU network members carries the signature of the template (i.e., analyte) to which the DCL is exposed. If the library members have different optical properties (i.e., different UV-visible absorption spectra) then the change in... 

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