Experiments and Data Processing

Detection, characterization, and identification of metabolites require multiple mass spectrometry experiments such as full scan LC/MS and LC/MS/MS. The process becomes very time consuming, particularly when a large number of metabolites are formed. The interpretation of LC/MS/MS data is very laborious and inefficient, and can be a rate-limiting step in the metabolite identification process. Therefore, new approaches to data acquisition that would minimize the need for multiple experiments, and data processing tools that would simplify mass spectral interpretation, are highly desired. 

Much of the experience and data from wastewater treatment has been gained from municipal treatment plants. Industrial liquid wastes are similar to wastewater but differ in significant ways. Thus, typical design parameters and standards developed for municipal wastewater operations must not be blindly utilized for industrial wastewater. It is best to run laboratory and small pilot tests with the specific industrial wastewater as part of the design process. It is most important to understand the temporal variations in industrial wastewater strength, flow, and waste components and their effect on the performance of various treatment processes. Industry personnel in an effort to reduce cost often neglect laboratory and pilot studies and depend on waste characteristics from similar plants. This strategy often results in failure, delay, and increased costs. Careful studies on the actual waste at a plant site cannot be overemphasized. 

Figure 20.2. Schematic outline of typical pump-probe-detect experiments with femtosecond pulses, a molecular beam source, and mass spectrometric detection of transient species. Computer control and data processing instruments, as well as various optical components, are not shown. The time separation Af between pump and probe pulses is dictated by the difference in optical path lengths. Ad, traversed by the two components of the original pulse.

The lack of critical attention to the physical development of the biologic product is problematic not only do sufficient experience and data need to be collected to support licensure, the same data set is needed to justify changes and maintain comparability to the manufacturing process. 

Nevertheless, the results of the experiments and the simulation confirms earlier experiences of the need of an early and complete mixing of the combustion air and a high temperature for low CO emissions. Additionally, the applicability of the proposed measurement and data processing strategy has been shown. The methodological results can be transferred to comparable macro mixing prob lems. 

Even in those cases where an aiialysis is qualitative, quantitative measures are employed in the processes associated with signal acquisition, data extraction, and data processing. The comparison of, say, a sample s infrared spectrum with a set of standard spectra contained in a pre-recorded database involves some quantitative measure of similarity in order to find and identify the best match. Differences in spectrometer performance, sample preparation methods, and the variability in sample composition due to impurities will all serve to make an exact match extremely unlikely. In quantitative analysis the variability in results may be even more evident. Within-laboratory tests amongst staff and inter-laboratory round-robin exercises often demonstrate the far from perfect nature of practical quantitative analysis. These experiments serve to confirm the need for analysts to appreciate the source of observed differences and to understand how such errors can be treated to obtain meaningful conclusions from the analysis. 

The computerized system which helps most in product development resembles more the so-called expert system, which is a set of relationships quantified by an experiment for the purpose of similar products. Such systems are increasingly more effective with the amount of data (information) increasing. Considering such need, this book will have in the future a companion CD-ROM containing a base of available data which will be periodically updated to build an incremental wealth of information serving two purposes material selection and data processing for the needs of the formulator. 

The use of internal action limits by the manufacturer to assess the consistency of the process at less critical steps is also important. Data obtained during development and validation runs should provide the basis for provisional action limits to be set for the manufacturing process. These limits, which are the responsibility of the manufacturer, may be used to initiate investigation or further action. They should be further refined as additional manufacturing experience and data are obtained after product approval. 

It is essential to select a limiting number of independent variable levels at the planning stage of an active experiment. It is also required to select an end rational number of experimental measurements. If a small volume of experimental measurements is planned, then the aim of the test may not be achieved. In contrast, if the measuring data volume is too big, the cost of the experiment has risen and its duration and data processing are taking too much time. 

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