Food contaminations with E. coli bacteria are a major concern for public health. Current techniques for detection are based on sample extractions, time-consuming sample preparations, and labor intensive analyses. Because some strains can be toxic at a level of tens of bacteria and some are not harmful at all, a method of colony localization and strain classification must be developed. In this study we present first results that are based on Fourier transform infrared (FT-IR) spectroscopy and FT-IR imaging. Due to the chemical similarity of different E. coli strains, the acquired spectra show a strong resemblance. It is demonstrated here that based on a correlation analysis samples of the same strain are classified as such and that different strains can be discriminated. The next step is to move from single-spot analyses towards spectroscopic imaging--a technique that facilitates detection of localized bacteria colonies. However, the sheer amount of data acquired in short periods of time prevents many chemical imaging techniques from being feasible for online sensing or for screening extended areas. To improve the time resolution, a data compression approach based on three-dimensional wavelet compression has been applied. It is shown that even with slight compression computation times can be cut down by over an order of magnitude while preserving enough information for localization and classification.

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In hyphenated measurement devices, temporal, spatial, and spectral resolutions continue to increase. While this is advantageous from a chemical sensing perspective, the amount of data grows exponentially; this imposes challenges on Chemometric algorithms resulting in long computation times. In online sensing, however, time resolution is of vital importance and time delays introduced by lengthy computations become unacceptable. Further, in many applications, data need to be documented and a continuous stream of large data sets presents another technical burden regarding archiving space. This work builds on the previous wavelet studies that have already been published. It was found that there is no reason to use the same wavelet for all dimensions of a data set. 'Hybrid wavelets' were introduced which combine different wavelets; this facilitates fine-tuning of the compression. The challenge in using hybrid wavelets lies in the very large number of possible wavelet combinations. A method is presented that automatically determines the optimum and near-optimum wavelet combinations for a given data set. These wavelet combinations are found by evaluating each dimension of the data set separately. This procedure enables an optimization of computation speed, compressed data set size and accuracy of the Chemometric model.Two data cubes acquired from two different experiments are used to show the selection algorithm's capabilities. These examples demonstrate that this algorithm selects hybrid wavelets that are superior to randomly selected wavelet combinations regarding data approximation, compressed data set size, and acceleration of computations.

Spectroscopic imaging has become a widely used tool for analyses of heterogeneous samples. Focal plane array detectors are incorporated into spectrometers that acquire a large number of spectra from different sample locations in parallel. This sensing technique facilitates analyses of spatial distributions of chemical information in an X-Y plane at high time resolution. In many cases, chemical reactions proceed in three spatial dimensions (X-Y-Z) and require the acquisition of spectroscopic information in an X-Y plane plus topographic (Z-dimension) information. However, capturing two-dimensional (2D, i.e., X-Y) images from three-dimensional (3D, i.e., X-Y-Z) samples inherently loses Z-dimension information. This technical note describes an augmented spectroscopic imager that gains both types of data, i.e., spatially resolved spectroscopic information and topography. For the latter purpose, a regular light pattern is generated and projected onto a sample. Due to its 3D topography, this light pattern is distorted. After extracting these distortions, the topography can be determined since the height structure is encoded in the light pattern. Because topographic probing must not affect infrared measurements, different wavelength ranges are used. Here spectroscopic information is acquired in the mid-IR while the light pattern probing the topography is generated in the visible. For relating distortions to physical height structures, the setup needs to be calibrated. For this purpose, calibration objects of known dimensions have been manufactured onto which the light pattern is projected. Determining distortions introduced by objects of known height derives a transform from distortions to topographies. Due to mechanical restrictions, the light pattern can only achieve a certain spatial resolution. In order to enhance the spatial resolution the topography is probed with, scanning the light pattern in X- and Y-direction is proposed.