Spectral preprocessing

The primary objective of preprocessing treatments is to remove the nonchemical biases from the spectral information. Scattering effects induced by particle size or surface roughness may lead to offsets or other more complex baseline distortions. Differences in sample density or the angle of presentation may induce overall intensity variations as well as additional baseline distortions. Most samples are not perfectly uniform on the microscopic scale, and these effects may dominate the initial contrast observed in an un-processed chemical image. In some cases this contrast may provide useful information to the analyst however, it is generally removed in order to focus on the chemical information. 

It is almost always necessary, or at least desirable, to preprocess the absorption spectra in some fashion the aim is to enhance the spectral features that carry information regarding the analyte of interest, and effectively to suppress or eliminate superfiuous features. The simplest form of preprocessing is the selection of appropriate wavelengths in MLR model development the analogy in PLS and PCR is the selection of a limited spectral region (or regions). 

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Wood et al. have combined acoustic levitation and Raman spectroscopy with the intention of developing a field tool for environmental monitoring of algal blooms and nutrient availability [62]. Heraud discussed the most appropriate spectral preprocessing techniques for analysis of Raman spectra of single microalgal cells and developed a method to predict the nutrient status of those cells from in vivo spectra [63,64]. 

Different spectral preprocessing and transformations available in SIMCA P-p (version 10.0, Umetrics, Sweden) were evaluated and the best approach for data handling and manipulation was determined. Data collected on the surrogate tablets were divided into a training set to generate the PLS models, and prediction set to test the PLS models. MCC powder, equilibrated at different RH, was also roller compacted at different roll speeds on a Fitzpatrick IR220 roller compactor fitted with smooth rolls. Powder feed rate and roll pressure were kept constant for all experiments. The key sample attributes measured on the surrogate tablets were also measured for the samples prepared by roller compaction. 

For qualitative spectrum interpretation, the conventional method for routine identification of chemical species is a library-search, based on spectral mapping algorithms. Before library-searching spectral preprocessing, i.e., elimination of baseline effects and noise, standardization, etc., is performed on the sample spectrum. Comparison of such a processed spectrum with a... 

Multivariate analysis of H MR spectra of brain tumour tissue has been shown to provide a high level of discrimination between low- and high-grade astrocytomas.37 Such accuracy was achievable using two novel spectral preprocessing methods a-scaling and an optimal attribute selector (Table 2).49... 

From the information theory (Chapter 11.6.1) it is known that a signal contains most information if all possible values of the signal have equal probability. To generate pattern vectors with equally distributed features intensity levels must be created for each mass number. Each level should have the same probability in the spectral file. If there are k intensity levels and d mass numbers a set of (k-1)d threshold values must be stored for spectral preprocessing C250D. 

Lasch and cowoikers describe in Chap. 8 their group s efforts to improve taxonomic resolution without compromising the simplicity and the speed of MALDI TOF MS. Such improvements may be achieved by signature database expansion with novel and diverse strains, optimization, and standardization of sample preparation and data-acquisition protocols. Further enhancement in data analysis pipelines including more advanced spectral preprocessing, feature selection, and supervised methods of multivariate classification analysis also contribute to taxonomic resolution enhancements. Strains of Staphylococcus aureus. Enterococcus faecium, and Bacillus cereus are selected to illustrate aspects of that strategy. 

Microbial MALDI-TOF mass spectra are complex signals which can carry an enormous amoimt of informatiom Mass spectra of microoiganisms may contain several dozens of mass peaks a comprehensive analysis involves several distinct analysis steps spectral preprocessing and peak detection followed by the classification/iden-tification analysis procednre in the strict sense. 

Preprocessing The main goals of spectral preprocessing can be summarized as follows (1) improvement of the robustness and accuracy of subsequent classification analysis, (2) improved interpietability, (3) detection and removal of outliers and trends, and (4) reduetion of the dimensionahty of subsequent data-mining tasks. This step often involves the removal of irrelevant and/or redundant information by feature seleetion (Laseh 2012). 

The preprocessing techniques described in this section are focused on operations on the basic Raman spectrum. Chemometrics sometimes differentiates between such raw spectral preprocessing and preprocessing of metrics derived from a spectrum (e.g., band width or area). With the exception of some issues with normalization, use of these latter operations are not particularly unique to Raman spectroscopy and their use is left to the discretion of the reader. 

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