Preprocessing the samples

Commercial application of the dendrimer-based reagent technology has been demonstrated by the successful development of The Stratus CS STAT fluorometric analyzer [5] marketed by Dade Behring Inc. This rapid automated point of care immunoassay system provides quantitative analysis of whole blood or preprocessed plasma samples via unit use assay test packs. Up to four test packs can be introduced for each sample. All reagents [5-9] required for specimen analyses are contained within the test packs. 

In this chapter a number of preprocessing tools are discussed. They are divided into two ba.sic types depending on whether they operate on samples or variables. Sample preproces.sing tools operate on one sample at a time over all variables. Variable preprocessing tools operate on one variable at a time over all samples. Therefore, if a sample is deleted from a data. set, variable preprocessing calculations must be repeated, while the sample preprocessing calculations will not be affected. 

A plot of the measurement vectors of all of the samples in the training set is shown in Figure 4.61 (an offset for each class was added for clarity). The measurement vectors within a class have similar features and there is significant overlap of features between classes. Examine this plot for outlier samples and/or measurement variables as well as an indication of the need for preprocessing. In this case, all measurements appear to be reasonable and preprocessing does not appear to be warranted. 

FIGURE 5.87. Preprocessed prediction samples, with the vertical line indicating the variables used in the MLR model. 

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. 

There are several possible ways to improve the saturation rate. One reason for the delay in the attainment of equilibrium is the decrease in effective surface area during the dissolution process. This can be overcome by using a substantial excess of solid in the solubility sample (Higuchi et al., 1979). The surface area of the solid can also be increased by preprocessing the solubility samples. Both votexing after adding a smallLton ball and sonication are very effective techniques for this purpose. 

To convert an optical signal into a concentration prediction, a linear relationship between the raw signal and the concentration is not necessary. Beer s law for absorption spectroscopy, for instance, models transmitted light as a decaying exponential function of concentration. In the case of Raman spectroscopy of biofluids, however, the measured signal often obeys two convenient linearity conditions without any need for preprocessing. The first condition is that any measured spectrum S of a sample from a certain population (say, of blood samples from a hospital) is a linear superposition of a finite number of pure basis spectra Pi that characterize that population. One of these basis spectra is presumably the pure spectrum Pa of the chemical of interest, A. The second linearity assumption is that the amount of Pa present in the net spectrum S is linearly proportional to the concentration ca of that chemical. In formulaic terms, the assumptions take the mathematical form... 

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. 

HAs content in preprocessed meat cuts produced in Canada were present in 16 different types of processed meat cuts (234). The highest mutagenic activity was found in a smoked mrkey breast sample four samples had low mutagenic activity, and 11 samples were not mutagenic. The only HA found in the samples with mutagenic activity was MelQx. Conclusions have been made that the consumption of... 

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... 

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