Software correlator

In preparation for a field wide quick look correlation, all well logs need to be corrected for borehole inclination. This is done routinely with software which uses the measured depth below the derrick floor ( alonghole depth below derrick floor AHBDFor measured depth , MD) and the acquired directional surveys to calculate the true vertical depth subsea (TVSS). This is the vertical distance of a point below a common reference level, for instance chart datum (CD) or mean sea level (MSL). Figure 5.41 shows the relationship between the different depth measurements. 

The drawing software comprises a comprehensive collection of standard tools to sketch 2D chemical structures. To specify all its facilities and tools would go far beyond the scope of this overview, but there are some nice features that are very useful for chemists so they are mentioned here briefly. One of these enables the prediction of H and NMR shifts from structures and the correlation of atoms with NMR peaks (Figure 2-127). lUPAC standard names can be generated... 

Using this simplified model, CP simulations can be performed easily as a function of solution and such operating variables as pressure, temperature, and flow rate, usiag software packages such as Mathcad. Solution of the CP equation (eq. 8) along with the solution—diffusion transport equations (eqs. 5 and 6) allow the prediction of CP, rejection, and permeate flux as a function of the Reynolds number, Ke. To faciUtate these calculations, the foUowiag data and correlations can be used (/) for mass-transfer correlation, the Sherwood number, Sb, is defined as Sh = 0.04 S c , where Sc is the Schmidt... 

Another recent database, still in evolution, is the Linus Pauling File (covering both metals and other inorganics) and, like the Cambridge Crystallographic Database, it has a "smart software part which allows derivative information, such as the statistical distribution of structures between symmetry types, to be obtained. Such uses are described in an article about the file (Villars et al. 1998). The Linus Pauling File incorporates other data besides crystal structures, such as melting temperature, and this feature allows numerous correlations to be displayed. 

With a new software program it is possible to measure the Texture Constant" of pectins. This Texture Constant K is calculated by the ratio of the maximum force during the time interval of the measurement and the measured area below the force-time curve. The resulting constants K correlate well with the dynamic Weissenberg number of oscillating measurements carried through with the same pectin gels. 

The data from sensory evaluation and texture profile analysis of the jellies made with amidated pectin and sunflower pectin were subjected to Principal component analysis (PC) using the statistical software based on Jacobi method (Univac, 1973). The results of PC analysis are shown in figure 7. The plane of two principal components (F1,F2) explain 89,75 % of the variance contained in the original data. The attributes related with textural evaluation are highly correlated with the first principal component (Had.=0.95, Spr.=0.97, Che.=0.98, Gum.=0.95, Coe=0.98, HS=0.82 and SP=-0.93). As it could be expected, spreadability increases along the negative side of the axis unlike other textural parameters. 

The results obtained in gradient mode by Kaliszan et al. for logkw determination are based on a relatively complex mathematical treatment, using RPLC optimization software. As described, logP values were determined in the range -2correlation coefficient between log k and log P was satisfactory, and included in the range 0.94[Pg.343]

As a side aspect, the HPLC-Raman correlation results allow us to calibrate the RRS instruments in terms of carotenoid concentration. According to the regression analysis, the cumulative skin carotenoid content c, measured in pg per g of skin tissue, is linked to the height of the C=C RRS skin carotenoid intensity, I, via c [pg/g]=4.3 x 10 5=/ [photon counts]. Integrating the RRS spectra with the instrument s data acquiring software therefore allows us to display skin carotenoid content directly in concentration units, i.e., in pg carotenoid content per g of tissue. 

In addition, the calculation of many different ID, 2D and 3D descriptors is possible using a range of commercially available software packages, such as Sybyl, Cerius2, Tsar, Molconn-Z and Hybot. Several new descriptor sets are based on quantification of 3D molecular surface properties, and these have been explored for the prediction of, e.g., Caco-2 permeability and oral absorption. It is pointed out here that a number of these new descriptors are strongly correlated to the more traditional physico-chemical properties. 

Using our dataset which includes all of the descriptors mentioned so far, we conducted a PLS analysis using SIMCA software [34], In the initial PLS model, MW, V, and a (Alpha) were removed because they are in each case highly correlated with CMR (r > 0.95). SIMCA s VIP function selected only qmin (Qnegmin) for removal on the basis of it making no important contribution to the model. In the second model, 2q+/a (SQpos A) and ECa/a (SCa A) coincided nearly exactly in the three-component space of these two, we decided to keep only ECa/a in the third and final model. This model consisted of three components and accounted for 75% of the variance in log SQ the Q2 value was 0.66. 

The correlation was made using PLS analysis within the VolSurf software. The solubility was quantified via the —log[Soly]-values, where Soly was expressed in mol L 1 at 25°C. The quantitative PLS analysis resulted in a two-component model. The recalculated versus experimental PLS plot (Fig. 17.3) shows the correlation obtained. From the objects pattern, a differentiation between very poorly/ poorly/medium/highly/very highly soluble compounds was seen to be possible, though fine quantitative predictions were difficult to achieve. 

In chromatography techniques, selectivity can be proved by the existence of good separation between the analyte and the other components (such as the matrix, impurities, degradation product(s), and metabolites). A consequence of this requirement is that the resolution of the analyte from the other components should be more than 1.5-2.0. In order to detect the possibility of coelution of other substance(s), the purity of the analyte peak should also be determined. For instance, the UV-Vis spectrum of the analyte peak/spot can be used to determine 4the purity of the analyte peak/spot, in this case the correlation coefficient V (this term is used by the software of DAD System Manager Hitachi, and CATS from Camag). With the same meaning and mathematical equation, other terms are used, such as Match... 

Sole use of the correlation coefficient (r) alone is not recommended as a means to demonstrate linearity. The correlation coefficient describes the relation between two random parameters, and shows no relevance for the analytical calibration [31]. The correlation coefficient does not indicate the linearity or lack thereof, unless r exceeds 0.999 [8, 32, 33]. If the value of r is less than 0.999, other parameters such as Vxo, Xp value, ANOVA linear testing, etc., should be calculated. Ebel [34] described using the transformation of r (i.e., Vu ) for expressing the degree of linearity, where the acceptance value of (1 — r ) should be less than 0.05. Camag (Muttents) described the sdv parameter (i.e., the relative standard deviation of the calibration curve) for expressing the linearity of a calibration curve for TLC/HPTLC in its CATS software, and can be calculated as follows ... 

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