Spectroscopic imaging techniques

Chemical compound homogeneity is an important issue for pharmaceutical sohd forms. A classical spectrometer integrates the spatial information. In solid form analysis, use of a mean spectrum on a surface can be a drawback. For example, in the pharmaceutical industry it is important to map the distribution of active ingredients and excipients in a tablet so as to reveal physical interaction between the compounds and help to solve homogeneity issues. Spectroscopic imaging techniques that visualize chemical component distribution are thus of great interest to the pharmaceutical community. 

FT-IR and Raman spectroscopic imaging techniques may employ three general approaches to obtain spatially resolved chemical information mapping, imaging with a multi-element detector, and spatial encoding and decoding. 

Qualmann and Kessels have reported the synthesis of carborane-containing lysine dendrimers (123) (Fig. 72), with a better defined number of boron atoms, for use as protein labels in immunocytochemistry using electron microscopic techniques such as electron energy loss spectroscopy (EELS) and electron spectroscopic imaging (ESI).149... 

The Scanning Tunneling Microscope has demonstrated unique capabilities for the examination of electrode topography, the vibrational spectroscopic imaging of surface adsorbed species, and the high resolution electrochemical modification of conductive surfaces. Here we discuss recent progress in electrochemical STM. Included are a comparison of STM with other ex situ and in situ surface analytic techniques, a discussion of relevant STM design considerations, and a semi-quantitative examination of faradaic current contributions for STM at solution-covered surfaces. Applications of STM to the ex situ and in situ study of electrode surfaces are presented. 

In this section, we present the development of an automated protocol for prostate tissue histology [164] from infrared spectroscopic imaging data as an example of the techniques described (Fig. 8.11). The data is three dimensional with x-y—axes representing the image plane and the 2-axis representing the spectral dimension. After data acquisition, two important pre-processing steps, namely baseline correction and de-noising, are performed. Since the entire data set is derived from human tissue samples, the spectra have similar characteristics and, therefore, a manually chosen set of pre-defined wave number could be used as the reference points for baseline correction. It is... 

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