Ethanol regression analysis

Salama et al. [34] developed and validated a spectrophotometric method for the determination of omeprazole and pantoprazole sodium via their metal chelates. The procedures were based on the formation of 2 1 chelates of both drugs with different metal ions. The colored chelates of omeprazole in ethanol were determined spectrophotometrically at 411, 339, and 523 ran using iron(III), chromium(III), and cobalt(II), respectively. Regression analysis of Beer s plots showed good correlation in the concentration ranges 15-95, 10-60, and 15-150 /ig/ ml of pure omeprazole using iron(III), chromium(III), and cobalt(II), respectively. 

Keywords Traceability Uncertainty Chemical analysis Regression analysis Ethanol Beer... 

Fig. 6 Influence of HP-p-CD concentration on the fluorescence intensity (measured at Aem = 513 nm) of a 10-5 M 9-methyl- 12H-benzo [a]phenothiazine water/ethanol 99 1, v/v, mixture. The solid line was calculated through the use of Eq. 4, assuming a 1 1 stoichiometry and using Too and JCf values obtained from a non-linear regression analysis (from [19], with permission)...

Linear Regression Analysis of Tensile Strength (o.) and Ultimate Elongation (Ej ) Versus Volume of Elastomer In Ethanol Swelled Network... 

Linear Regression Analysis of Elastomers Tensile Strength Versus Swelled In Ethanol and Ultimate Elongation for MTBE ... 

Calculated from data for ethanol and heptanol. Calculated by regression analysis from data for 10 alcohols. 

When ethanol was added into the aqueous solutions, the system coefficients were altered because the ethanol proportions were high enough to change the physio-chemical properties of the medium. To study the solvent effects, the system coefficients were determined at different ethanol proportions. The system coefficients of a PDMS/ethanol-water system were obtained with the same procedures as those for the P/W system. For example, the partition coefficients of the PDMS/50% ethanol-water system (P/E50) were determined for all of the 32 probe compounds (Table 5.1). A new LFER equation matrix was generated from log of a compound and its solute descriptors [log A p/esq R, 3i, a, p, V]. The respective system coefficients of the P/E50 system obtained by multiple regression analysis of the equation matrix were [0.68, 0.31, -1.27, -1.53, -2.08, 1.35]p,g5o, R = 0.991. The solvent effects of 50% ethanol on the system coefficients can be obtained by subtraction of the system coefficients of the P/W system from the system coefficients of the P/E50 system ... 

This example involves vapor-liquid equilibrium (VLE) data for the design of a distillation tower to dehydrate ethanol. A portion of the T-x-y data for an ethanol-water mixture, measured at 1.013 bar (1 atm) using a Gillespie still (Rieder and Thompson, 1949), is shown in Figure 3.1a. Here, it is desired to use regression analysis to enable the UNIQUAC equation to represent the data accurately over the entire composition range. 

This method was adapted from Chen and Ho (72). The tested compounds were added into a 1.0 x 10 M ethanol solution of DPPH to make a final concentration at 30 pM. After mixing ftioroughly, the solutions were kept in the dark for 30 minutes. Thereafter, the absorbency of the samples was measured using a spectrophotometer (Milton Roy, Model 301) at 517 nm against ethanol without DPPH as a blank reference. The test for each sample was duplicated and the values were averaged. For the determination of ICso, each of the purified compounds was made into 8 different concentrations for DPPH testing. ICso was obtained by extrapolation from linear regression analysis. 

NIR spectroscopy is often used for nondestructive measurement of chemicals in various materials. The application of least-squares SVM regression (LS-SVMR) in predicting mixture composition from NIR spectra was investigated by Thissen et al. NIR spectra for ternary mixtures of ethanol, water, and 2-propanol were measured at 30°C, 40°C, 50°C, 60 C, and 70°C. The learning set consisted of 13 mixtures per temperature, whereas the test set consisted of 6 mixtures per temperature. For the test set, the least-squares SVM approach had an RMSE 2.6 times lower than that from a PLS analysis. 

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