Retention data ofs

Yet, indirect information on enantioselective complexation by silica-bound antibiotics in HPLC can be extracted from the analysis of retention data of several ligands whose structure is systematically varied to explore chemical diversity in terms of functional groups, stereogenic elements, molecular complexity, and rigidity-flexibility. 

By determining the retention data of polymer standards with known and thus molecular diameter... 

Cluster analysis (numerical toxonomic aggregation) is applied to arrange phases according to their chromatographic behaviour. A set of retention data for 16 monofimctional benzenes, 110 difunctional benzenes and 15 trifunctional benzenes was subjected to analysis. Three groups of stationary phases can be distinguished polar, non-polar, and polyfluorinated. A linear relationship between the retention data of two stationary phases of the same class can be worked out. This linear relationship fits the model... 

Qualitative analysis by gas chromatography (GC) in the classical sense involves the comparison of retention data of an unknown sample with that of a known sample. The alternate approach involves combination and comparison of gas chromatographic data with data from other instrumental and chemical methods. 

Jinno, K., and K. Kawasaki, Correlation Between the Retention Data of Polycyclic Aromatic Hydrocarbons and Several Descriptors in Reversed-Phase HPLC. Chromato-graphia, 1983 17, 445-449. 

Many scales, either empirical or measured, have been proposed for the hydrophobicity of amino acid residues in proteins (Nakai and Li-Chan, 1988). The most extensive study on tlje hydrophobicity index of amino acids was published by Wilce et al. (1995). The authors derived four new scales of coefficients from the reversed-phase high-performance liquid chromatographic retention data of 1738 peptides and compared them with 12 previously published scales. 

Changes in mobile-phase components such as pH, ionic strength, and water content have been systematically studied [3,310,316,317]. These studies indicate that retention of basic analytes is mediated primarily by the cation-exchange properties of the silica [2]. Interestingly, it has been suggested from retention data of various pharmaceuticals that the retention mechanisms of silica with aqueous eluents and reversed-phase systems are similar [317,318]. Due to the ion-exchange properties of silica, mobile-phase pH adjustments are useful in changing the retention of ionic compounds. 

Table 7.3 Comparison of SFC and HPLC Relative Retention Data of Chlordiazepoxide Mixtures...

For all the interpretive methods described in section 5.5 it is essential to know the retention data of all the individual components in a sample. Section 5.6 deals with possibilities to obtain all this chromatographic information. 

Figure 5.16a can be constructed once the retention data of all solutes have been measured on the two pure phases. It is assumed that retention (K) varies linearly with composition (

straight lines, which represent the (expected) variation of retention with stationary phase composition for the four solutes W,X, Y and Z. 

All interpretive optimization methods are by definition required to obtain the retention data of all sample components at each experimental location. If the sample components are known and available they may be injected separately (at the cost of a large increase in the required number of experiments). For unknown samples, for samples of which the individual components are not available, and in those situations in which we are not prepared to perform a very large number of experiments (as will usually be the case in the optimization of programmed analysis) we need to rely on the recognition of all the individual sample components in each chromatogram (see section 5.6). 

The Sentinel gradient optimization method, by analogy with the isocratic Sentinel method, requires a minimum of 7 chromatograms to be recorded before the optimum conditions can be predicted and it requires the retention data of all solute components to be established at each experimental location. 

The predictive method of Jandera et al. [628] requires knowledge of the isocratic retention data of all solute components in binary and (preferably) ternary mobile phase mixtures. Once these data are available, the method may be very helpful in obtaining an adequate (but not an optimum) separation with a ternary gradient. Unfortunately, the data required for the application of this predictive method are almost never available, and hence a large number of experiments need to be performed before any predictions can take place. When this is the case the method is of very little practical use. 

Butts [89] presented retention data of TMS derivatives of a number of biochemically important substances, such as amines, pyrimidines, purines, imidazoles, indoles, various acids and other substances on two stationary phases, OV-1 and OV-17. He prepared the derivatives using the following procedure. A 1-mg amount of the substance was placed in a 3.5-ml septum-stoppered vial, then 100-pl portions of dry pyridine and BSTFA containing 1% of TMCS were added, the contents were stirred thoroughly and heated for 16 h at 60°C. Portions of 4 pi were injected directly into the gas chromatograph. 

TMS derivatives of biogenic amines are used in combination with acyl derivatives for electron-capture detection. Horning et al. [97] presented retention data of TMS-N-acetyl and TMS-N-HFB derivatives of a number of these substances on SE-30, OV-1 and OV-17. The derivatives were prepared by the following procedure. A 1-mg amount of the amine or amino hydrochloride was dissolved in 0.1 ml of acetonitrile and 0.2 ml of TMS-imida-zole was added. After heating for 3 h at 60°C, 5 mg of N-acetylimidazole (or 0.1 ml of HFB-imidazole) were added and the solution was heated at 80°C for 3 h (30 min at 60°C). The solution was used directly for the GC analysis. 

Corina [147] reported retention data of benzyl esters of a number of acids from various biological materials on E-30 silicone stationary phase. 

Retention data of these derivatives with various combinations of Ri—R4 have been published [158] for C1-C7 monocarboxylic acids on Apiezon L, Carbowax 20M and neopentylglycol succinate. Using the alkali FID, these acids can be determined at the level of 10 pg. 

Components were identified with GC/MS. A thiazolidine derivative is easily identified using single ion. monitoring with m/z 88 (thiazolidine ring - H). Table II shows mass spectra and GC retention data of thiazolidine derivatives. The gas chromatogram of the extract from the reaction mixture indicated that methyl glyoxal produced three products, 2-acetylthiazoline (peak 7), 2-acetylthiazolidine (peak 8), and 2-formyl-2-methylthiazolidine (peak 9). 

The validity of Eq. (1.44) was tested on the retention data of various phenols and herbicides on a silica gel column with ternary selectivity gradients of 2-propanol and dioxane in n-heptane. The differences between the experimental elution volumes and the values calculated from Eq. (1.44) were 0.5 ml or less [96]. 

Ounnar and co-workers [31,32] widely apply in their QSRR studies the approach called correspondence factor analysis (CFA). CFA is mathematically related to PCA, differing in the preprocessing and scaling of the data. Those authors often succeeded in assigning definite physical sense to abstract factors, e.g., they identified the Hammett constants of substituents in meta and para positions of 72 substituted /V-benzylideneanilines (NBA) in determining the first factorial axis resulting from the CFA analysis of retention data of NBA in diverse normal-phase HPLC systems. 

If the retention data of a series of analytes are obtained at the same chromatographic conditions (i.e., the same stationary and mobile phase) then Eq. (11.11) assumes the following form ... 

In recent years, three-dimensional quantitative structure biological activity relationship methods known as comparative molecular field analysis (CoMFA) has been applied to construct a 3D-QSRR model for prediction of retention data. The CoMFA 3D-QSRR model is obtained by systematically sampling the steric and electrostatic fields surrounding a set of analyte molecules. Next, the differences in these fields are correlated to the corresponding differences in retention. The CoMFA model was successfully applied to HPLC retention data of polycyclic aromatic hydrocarbons [60]. 

Smitz et al. [ 114] subjected to multivariate analysis the retention data of 9 selected test analytes determined on 26 reversed-phase columns. By PCA the columns were grouped in three classes of similar properties. The authors were able to detect the deviation of some columns from the typical trend observed for RP-8, RP-18 or polymer-coated materials. The deviation could be explained in terms of the extreme physicochemical properties of the column. 

The correlation of isiK-ralic vs. gradient retention data of 76 structurally unrelated drug-ltkc (with permission from Ref. 4.t ). 

Jinno, K. Kawasaki, K. Correlations between the retention data of polycyclic aromatic hydrocarbons and several descriptors in reversed-phase HPLC. Chromatographia 1983, 77(8), 445-449. 

Corbella, R., Rodriguez, M.A., Sanchez, M.J. and Montelongo, EG. (1995). Correlations Between Gas Chromatographic Retention Data of Polycyclic Aromatic Hydrocarbons and Several Molecular Descriptors. Chromatogmphia, 40, 532-538. 

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