Data handling significant figures

Table V contains data for two model substances, p-aminohippurate (PAH) and phenol red. Consideration of the highest values in this table tells you where the major portions of the substances appear. For example, urine and bile show the largest concentrations of PAH and phenol red. Both compounds appear in significant concentrations in the kidney while the values in muscle, brain and cerebrospinal fluid (CSF) are invariably lower than the values seen in plasma. The values in parentheses (Table V) are percent of the administered dose in a given tissue or fluid compartment. They add to the previous information by revealing the overall importance of a particular compartment in the disposition of a substance. For example, while the hepatic concentrations of PAH and phenol red at 4 hrs. are only about 2-fold those of plasma, the large size of the shark liver relative to its body weight, typically about 10%, leads to the appearance of 30-40% of these substances in the liver. The relative handling of these compounds by the urinary and biliary system is obvious from considering the percentage figures. Thus in 24 hours phenol red is about equally distributed in the bile and urine (38 vs 31%) the urinary route is the dominant route of excretion of PAH, i.e., 56 vs 2%.

With the next example shown in Figure 2.20, the speed-up of a gradient separation performed on modern UHPLC-type columns and instrumentation is discussed. The attempted use of a narrow bore (2.1 mm) column packed with 2.2 pm particles requires an instrument with significantly reduced extra-colunm volume. This also includes the adaptation of the flow cell volume and the requirement of faster data handhng by the detector electronics (which was already a prerequisite in the example in Figure 2.19). Moreover, the instrument must be able to handle precise injection of 2 pi sample volume and withstand a column pressure of almost 600 bar. 

Safety analysis data input consists of facts and figures on the production process used, on the materials handled, the technical design of the installation, the operations organization, and the environment. The sum total defines the object under analysis. For the analysis proper, systematic representation of the installation with due consideration of safety aspects as well as selection of work methods and evaluation critieria are significant. 

A second ICPMS analysis illustrates how variability can be addressed and quantitated. This report summarizes attempts to differentiate standard white office paper from several countries by using elemental analysis and simple statistical techniques, such as the f-test between means (Section 2.2.3). The authors evaluated a number of important considerations, such as differences between monthly batches and different rolls of paper produced on the same day. The results are summarized in Figures 14.3-14.6. In all elements analyzed, there was no significant difference in elemental concentrations between handled and unhandled paper. When several elements were evaluated, differences were seen between batches on the same day, as illustrated by cluster analysis, but principally between the first batch and all others. Overlaps between batches 2, 3, and 4 are evident. Variations between monthly batches are illustrated by means of cluster analysis and t-test results. These data show that the expected variation between batches is greater tiian that between different rolls produced on the same day. Also, data indicate a change in processing in May compared with the other months. This report is an excellent example of the need for meticulous and complete analytical work. Such backgroimd data are indispensable to the evaluation of evidentiary value, be it of paper, fibers, or any other type of evidence, esp>ecially mass-produced items. 

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