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Correlation of samples

Four levels of pattern recognition have been defined by Albano (2). Levels I and II are most frequently used to determine the similarity of objects, or to characterize clusters of samples and to classify unknown objects. Level III takes advantage of the reduction of data dimensions resulting from SIMCA and seeks to establish a correlation of sample scores with independent variables... [Pg.1]

Table 2.3. Carbon-Proton and Proton-Proton Correlations of Sample Compound C9H15N03, Taken From Fig. 2.57. Table 2.3. Carbon-Proton and Proton-Proton Correlations of Sample Compound C9H15N03, Taken From Fig. 2.57.
Contamination, characterization of reference standards, surface effects Sample identification, correlation of samples to crime scenes... [Pg.3091]

Apart from our interest in optimizing adsorbent selectivity, there are other reasons for being interested in sample A values as a function of the adsorbent. First, it is often desired to duplicate a previous adsorbent for the purpose of controlled separation i.e., sample A" values on the second adsorbent must be the same as those on the original adsorbent. It is rarely possible to prepare adsorbents which are precisely equivalent in this respect by merely repeating a previous scheme for the preparation or treatment of an adsorbent. Residual differences in adsorbent activity can be adjusted for or eliminated, however, if we know how these differences are related to adsorbent proce.ssing and sample /f" values. Second, we often want to use experimental A"" values (i.e., Ay, or A values) for the purpose of identifying unknown components in a separated sample. This requires comparison of A values for the unknown sample component with values determined for known compounds. In many cases these latter values have been measured previously on another adsorbent of the same type (e.g., in another laboratory), and it is then necessary to relate A values on one adsorbent to those on another. This generally requires the correlation of sample A values with adsorbent activity. Finally, comparisons of experimental A values as a function of adsorbent activity can serve occasionally to clarify the mechanism of adsorption [e.g., Refs. 1,2)]. [Pg.67]

Multivariate analysis were made on all data in Latentix 1.0 (Mathworks Inc, Natick, MA, USA). Principal Component Analsysis (PCA) was made for unsalted samples separately as well as for all sample groups. The results were plotted in a bi-plot showing the distribution and correlation of samples as well as characteristic measurements. [Pg.233]

This usefulness of the correlation is twq-fpld first it provides information on the cetane indices that are not aViit le in the literature as in the cases of polynuclear aromatics and sulfur-containing aromatics, and second it helps provide an evaluation of the cetane index based on a few milligrams of sample, instead of the liter or so required for the motor method. [Pg.52]

The spectroscopic methods, NMR and mass spectrometry for predicting cetane numbers have been established from correlations of a large number of samples. The NMR of carbon 13 or proton (see Chapter 3) can be employed. In terms of ease of operation, analysis time (15 minutes), accuracy of prediction (1.4 points average deviation from the measured number), it is... [Pg.220]

Interpretable high-resolution structural infomiation (e.g. preservation of dimensions, or correlation of the stmctiiral detail with a physiologically or biochemically controlled state) is therefore obtained exclusively from samples in which life has been stopped very quickly and with a sufficiently high time resolution for the cellular dynamics [19]. Modem concepts for specimen preparation therefore try to avoid traditional, chemical... [Pg.1633]

We assume that the sequential errors are not correlated in time, we can write the probability of sampling a sequence of errors as the product of the individual probabilities. We further use the finite time approximation for the delta function and have ... [Pg.269]

The structural unit associated with an electronic transition m UV VIS spectroscopy IS called a chromophore Chemists often refer to model compounds to help interpret UV VIS spectra An appropriate model is a simple compound of known structure that mcor porates the chromophore suspected of being present m the sample Because remote sub stituents do not affect Xmax of the chromophore a strong similarity between the spectrum of the model compound and that of the unknown can serve to identify the kind of rr electron system present m the sample There is a substantial body of data concerning the UV VIS spectra of a great many chromophores as well as empirical correlations of sub stituent effects on k Such data are helpful when using UV VIS spectroscopy as a tool for structure determination... [Pg.567]

Electron ionization occurs when an electron beam crosses an ion source (box) and interacts with sample molecules that have been vaporized into the source. Where the electrons and sample molecules interact, ions are formed, representing intact sample molecular ions and also fragments produced from them. These molecular and fragment ions compose the mass spectrum, which is a correlation of ion mass and its abundance. El spectra of tens of thousands of substances have been recorded and form the basis of spectral libraries, available either in book form or stored in computer memory banks. [Pg.15]

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). [Pg.425]

Unit layout as installed is the next step of preparation. This may take some effort if analysts have not been involvea with the unit prior to the plant-performance analysis. The equipment in the plant should correspond to that shown on the PFDs and P IDs. Wmere differences are found, analysts must seek explanations. While a hne-by-line trace is not required, details of the equipment installation and condition must be understood. It is particularly useful to correlate the sample and measurement locations and the bypasses shown on the P IDs to those ac tuaUy piped in the unit. Gas vents and liquid (particularly water-phase) discharges may have been added to the unit based on operating experience out not shown on the P IDs. While these flows may ultimately be small within the context of plant-performance an ysis, they may have sufficient impact to alter conclusions regarding trace component flows, particularly those that have a tendency to build in a process. [Pg.2553]

The quantitative computations were conducted using equilibrium thenuodynamic model. The proposed model for thermochemical processes divides layer of the sample into contacting and non-contacting zones with the material of the atomizer. The correlation of all initial components in thermodynamic system has been validated. Principles of results comparison with numerous experimental data to confirm the correctness of proposed mechanism have been validated as well. [Pg.414]

The study on commercial HDPE samples could not provide a correlation of the izod impact test with the field performance test, i.e., drop impact resistance on moulded products [113]. It was found that the sample of highest density and lowest izod impact strength passed the drop impact test, but other samples of lower density and higher izod impact strength could not withstand shock loading by drop impact and failed in brittle manner. This may be due to the fact that velocities and modes of loading vary widely in different impact tests. It has been reported that even the qualitative agreement between the different impact tests is poor because the test bars and moulded products often have different orientation characteristics, particularly near the surface [115]. [Pg.288]

At the time of writing, in all papers published on adsorption studies on oxides surfaces, spectra have been reported of samples held at the ambient temperature of the sample compartment. It is obvious that when dealing with very volatile adsorbates, low temperature sample cells may be required to increase adsorption and also to prevent rapid desorption of the adsorbed species. In some instances, it is also desirable to record the spectra of species held at elevated temperatures for better correlation with industrial catalytic systems. It should be noted that there are only a few infrared spectra reported in the literature for high temperature studies of catalytic reactions. Sample emission at elevated temperature is a significant experimental complication in investigations of this type. [Pg.319]

The factors that correlate with treatment success do not clearly apply to success after detoxification from methadone maintenance. Correlates of successful detoxification include 1) less criminal behavior 2) more stable family 3) more stable employment 4) shorter drug history 5) long maintenance with lower dosage and 6) discharge status, with patient and staff consensus as opposed to unilateral discharge from treatment (Dole and Joseph 1978). In one study, addicts were followed an average of 2 years after detoxification (Stim-mel et al. 1977). Although only 28% of the total sample remained abstinent, 83% of those who had fully completed treatment remained abstinent. Another study of 105 patients detoxified after methadone maintenance treatment documented an 82% relapse rate within 12 months (Ball and Ross 1991). These... [Pg.83]

Figure 4.22. Correlation of assay values for components A and B, for three dosage levels of A, with 10 samples per group. The comer symbols indicate the 10% specification limits for each component. For manual injection (left panel) only relative standard deviations of 1-2% are found, but no correlation. Automatic injection (right panel) has a lower intrinsic relative standard deviation, but the data are smeared out along the proportionality line because no internal standard was used to correct for variability of the injected volume. The proportionality line does not go through the comers of the specification box because component B is either somewhat overdosed (2.4%). analytical bias, or because an interference results in too high area readings for B. The... Figure 4.22. Correlation of assay values for components A and B, for three dosage levels of A, with 10 samples per group. The comer symbols indicate the 10% specification limits for each component. For manual injection (left panel) only relative standard deviations of 1-2% are found, but no correlation. Automatic injection (right panel) has a lower intrinsic relative standard deviation, but the data are smeared out along the proportionality line because no internal standard was used to correct for variability of the injected volume. The proportionality line does not go through the comers of the specification box because component B is either somewhat overdosed (2.4%). analytical bias, or because an interference results in too high area readings for B. The...
Structural type. The possible function of flavonoids as antiherbivore defense com-ponnds was discnssed, bnt Mears (1980b) found no correlation between complexity of flavonoid profile and latitnde, as might be the case if complexity of profile increases as one goes from temperate to more tropical climates with concomitant increase in insect predators. Althongh the nnmber of samples stndied was not large, there was a relationship between latitnde and complexity of pigment profiles in taxa restricted to calcareons snbstrates. No driving force for this apparent relationship is evident. [Pg.133]


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See also in sourсe #XX -- [ Pg.82 , Pg.83 ]




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