Define, measure, analyze analysis

Infrared (IR) spectroscopy offers many unique advantages for measurements within an industrial environment, whether they are for environmental or for production-based applications. Historically, the technique has been used for a broad range of applications ranging from the composition of gas and/or liquid mixtures to the analysis of trace components for gas purity or environmental analysis. The instrumentation used ranges in complexity from simple filter-based photometers to optomechanically complicated devices, such as Fourier transform infrared (FTIR) spectrometers. Simple nondispersive infrared (NDIR) insttuments are in common use for measurements that feature well-defined methods of analysis, such as the analysis of combustion gases for carbon oxides and hydrocarbons. For more complex measurements it is normally necessary to obtain a greater amount of spectral information, and so either Ml-spectrum or multiple wavelength analyzers are required. 

The physical techniques used in IC analysis all employ some type of primary analytical beam to irradiate a substrate and interact with the substrate s physical or chemical properties, producing a secondary effect that is measured and interpreted. The three most commonly used analytical beams are electron, ion, and photon x-ray beams. Each combination of primary irradiation and secondary effect defines a specific analytical technique. The IC substrate properties that are most frequendy analyzed include size, elemental and compositional identification, topology, morphology, lateral and depth resolution of surface features or implantation profiles, and film thickness and conformance. A summary of commonly used analytical techniques for VLSI technology can be found in Table 3. 

In non-metric MDS the analysis takes into account the measurement level of the raw data (nominal, ordinal, interval or ratio scale see Section 2.1.2). This is most relevant for sensory testing where often the scale of scores is not well-defined and the differences derived may not represent Euclidean distances. For this reason one may rank-order the distances and analyze the rank numbers with, for example, the popular method and algorithm for non-metric MDS that is due to Kruskal [7]. Here one defines a non-linear loss function, called STRESS, which is to be minimized ... 

In several chapters we discussed how the quality of the analytical result defines the amount of information which is obtained on a sampled system. Obvious quality criteria are accuracy and precision. An equally important criterion is the analysis time. This is particularly true when dynamic systems are analyzed. For instance a relationship exists between the measurability and the sampling rate, analysis time and precision (see Chapter 20). The monitoring of environmental and chemical processes are typical examples where the management of the analysis time is... 

With these reservations in mind, we will next consider three approaches that have been used in the past to measure the efficacy of a partial agonist acting on an intact tissue. Each will be analyzed in two ways with the details given in Appendix 1.4C (Section 1.4.9.3). The first is of historical interest only and is based on Stephenson s original formulation, as expressed in Eq. (1.27) (Section 1.4.2) and with receptor occupancy given by the Hill-Langmuir equation in its simplest form, which we have already seen to be inadequate for agonists. The second analysis defines receptor occupancy as all the receptors that are occupied, active plus inactive. 

The most straightforward method for analyzing a solid material by infrared spectrometry is to dissolve it in a suitable solvent and then to measure this solution using a liquid sampling cell such as one of the several described in Section 8.8. Thus it becomes a liquid sampling problem, the experimental details of which have already been discussed (Section 8.8). It is the only method of solid sampling suitable for quantitative analysis because it is the only one that has a defined and reproduced pathlength. 

One or more of these bias components are encountered when analyzing RMs. In general, RMs are divided into certified RMs (CRMs, either pure substances/solu-tions or matrix CRMs) and (noncertified) laboratory RMs (LRMs), also called QC samples [89]. CRMs can address all aspects of bias (method, laboratory, and run bias) they are defined with a statement of uncertainty and traceable to international standards. Therefore, CRMs are considered useful tools to achieve traceability in analytical measurements, to calibrat equipment and methods (in certain cases), to monitor laboratory performance, to validate methods, and to allow comparison of methods [4, 15, 30]. However, the use of CRMs does not necessarely guarantee trueness of the results. The best way to assess bias practically is by replicate analysis of samples with known concentrations such as reference materials (see also Section 8.2.2). The ideal reference material is a matrix CRM, as this is very similar to the samples of interest (the latter is called matrix matching). A correct result obtained with a matrix CRM, however, does not guarantee that the results of unknown samples with other matrix compositions will be correct [4, 89]. 

To measure innovation we examined the rate of flow of NCEs into human testing, the earliest point at which reliable information appears outside the pharmaceutical industry and the point at which NCEs enter the regulatory pathway. The rates at which these compounds pass the milestones of the U.S. regulatory pathway (the points of IND filing, NDA submission, and NDA approval) were defined. In addition to the overall analysis, the data were analyzed by individual therapeutic areas. The observed differences between categories of NCEs imply the existence of scientific, industrial, and/or administrative differences between these categories. 

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