Analysis categories

Principal component regression and partial least squares are two widely used methods in the factor analysis category. PCR decomposes the matrix of calibration spectra into orthogonal principal components that best capture the variance in the data. These new variables eliminate redundant information and, by using a subset of these principal components, filter noise from the original data. With this compacted and simplified form of the data, equation (12.7) may be inverted to arrive at b. 

The top tier of proteomic analysis categories—proteomic mapping and proteomic profiling—will continue to dominate the field of proteomics because investigators are interested in what proteins are present in their sample and how much. How much is typically a proportion of experimental treatment to control, but proteomic technologies are improving on their abilities to provide amounts and concentrations. Therefore, an important consideration in proteomic analysis is a realistic sense of how much of the proteome can actually be measured with the proteomic platform available in order to best answer the scientific problem at hand. 

Such analysis can help detect potential problems so that any necessary reworking can be implemented early in the design process. Each analysis category is governed by a set of engineering reference file (ERF) values, usually unique to each production facility, thereby supporting full job portability between different assembly sites. 

The modules of the Analysis category analyse the signal passed through them without changing it in any way. Their purpose is solely to generate a visual representation of the analysis data. The available analysis techniques include SIFT (see Chapter 3) and sonogram. 

Below are brief descriptions of some of the particle-surface interactions important in surface science. The descriptions are intended to provide a basic understanding of how surfaces are probed, as most of the infonuation that we have about surfaces was obtained tluough the use of techniques that are based on such interactions. The section is divided into some general categories, and the important physics of the interactions used for analysis are emphasized. All of these teclmiques are described in greater detail in subsequent sections of the encyclopaedia. Also, note that there are many more teclmiques than just those discussed here. These particular teclmiques were chosen not to be comprehensive, but instead to illustrate the kind of infonuation that can be obtained from surfaces and interfaces. 

Besides these main categories, a large number of hybrid visualization techniques also exist, which arc combinations of the methods described. Well-known hybrid approaches arc the 2D or 3D glyph displays. These techniques combine the multidimensional representation capabilities of icon-based methods with the easy and intuitive representations of scatter-plot displays, Therefore these techniques can also be frequently found within chemical data analysis applications. 

In dissimilarity-based compound selection the required subset of molecules is identified directly, using an appropriate measure of dissimilarity (often taken to be the complement of the similarity). This contrasts with the two-stage procedure in cluster analysis, where it is first necessary to group together the molecules and then decide which to select. Most methods for dissimilarity-based selection fall into one of two categories maximum dissimilarity algorithms and sphere exclusion algorithms [Snarey et al. 1997]. 

Precision is a measure of the spread of data about a central value and may be expressed as the range, the standard deviation, or the variance. Precision is commonly divided into two categories repeatability and reproducibility. Repeatability is the precision obtained when all measurements are made by the same analyst during a single period of laboratory work, using the same solutions and equipment. Reproducibility, on the other hand, is the precision obtained under any other set of conditions, including that between analysts, or between laboratory sessions for a single analyst. Since reproducibility includes additional sources of variability, the reproducibility of an analysis can be no better than its repeatability. 

Significance testing for comparing two mean values is divided into two categories depending on the source of the data. Data are said to be unpaired when each mean is derived from the analysis of several samples drawn from the same source. Paired data are encountered when analyzing a series of samples drawn from different sources. 

The following experiments may he used to illustrate the application of titrimetry to quantitative, qtmlitative, or characterization problems. Experiments are grouped into four categories based on the type of reaction (acid-base, complexation, redox, and precipitation). A brief description is included with each experiment providing details such as the type of sample analyzed, the method for locating end points, or the analysis of data. Additional experiments emphasizing potentiometric electrodes are found in Chapter 11. 

To examine a sample by inductively coupled plasma mass spectrometry (ICP/MS) or inductively coupled plasma atomic-emission spectroscopy (ICP/AES) the sample must be transported into the flame of a plasma torch. Once in the flame, sample molecules are literally ripped apart to form ions of their constituent elements. These fragmentation and ionization processes are described in Chapters 6 and 14. To introduce samples into the center of the (plasma) flame, they must be transported there as gases, as finely dispersed droplets of a solution, or as fine particulate matter. The various methods of sample introduction are described here in three parts — A, B, and C Chapters 15, 16, and 17 — to cover gases, solutions (liquids), and solids. Some types of sample inlets are multipurpose and can be used with gases and liquids or with liquids and solids, but others have been designed specifically for only one kind of analysis. However, the principles governing the operation of inlet systems fall into a small number of categories. This chapter discusses specifically substances that are normally liquids at ambient temperatures. This sort of inlet is the commonest in analytical work. 

In heavy-metal analysis of the same pigments, metals found were present in only trace amounts. The data Hsted place the products tested in the category of nontoxic materials. The Radiant Color Co. has conducted toxicity tests on its own products similar to the A-Series and has found them to be nontoxic. Heavy metals were found only in trace amounts in these tests. 

Two main categories of the wet process exist, depending on whether the calcium sulfate is precipitated as the dihydrate or the hemihydrate. Operation at 70—80°C and 30% P20 in the Hquid phase results in the precipitation of CaSO 2 filterable form 80—90°C and 40% P20 provide a filterable CaSO O.5H2O. Operation outside these conditions generally results in poor filtration rates. A typical analysis of wet-process acid is given in Table 4. For more detailed discussion of the wet-process acid, see Fertilizers. 

Sugar analysis by hplc has advanced greatly as a result of the development of columns specifically designed for carbohydrate separation. These columns fall into several categories. (/) Aminopropyl-bonded siHca used in reverse-phase mode with acetonitrile—water as the eluent. (2) Ion-moderated cation-exchange resins using water as the eluent. Efficiency of these columns is enhanced at elevated temperature, ca 80—90°C. Calcium is the usual counterion for carbohydrate analysis, but lead, silver, hydrogen, sodium, and potassium are used to confer specific selectivities for mono-, di-, and... 

Analysis of Clean Water Act Effluent Guidelines Pollutants. Summary of the Chemicals Regulated by Industrial Point Source Category U.S. EPA, Washiagton, D.C., 40 CFR Parts 400-475, 1991. 

Often the goal of a data analysis problem requites more than simple classification of samples into known categories. It is very often desirable to have a means to detect oudiers and to derive an estimate of the level of confidence in a classification result. These ate things that go beyond sttictiy nonparametric pattern recognition procedures. Also of interest is the abiUty to empirically model each category so that it is possible to make quantitative correlations and predictions with external continuous properties. As a result, a modeling and classification method called SIMCA has been developed to provide these capabihties (29—31). 

Appendix 4 gives definitions and rules for stress analysis for shells, flat and formed heads, and tube sheets, layered vessels, and nozzles including discontinuity stresses. Of particular importance are Table 4-120.1, Classification of Stresses for Some Typical Cases, and Fig. 4-130.1, Stress Categories and Limits of Stress Intensity. These are veiy useful in that they clarify a number of paragraphs and simphfy stress analysis. 

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