GC-FID Technique for the Quantitative Evaluation of Multiple Residual Organic Solvent Impurities in Fosaprepitant Dimeglumine Drug

A proficient and distinct methodology is established for the quantification of multiple residual organic solvent impurities in fosaprepitant dimeglumine drug substance by gas chromatography with headspace sampler (HS-GC) and flame ionization detector (FID). Chromatographic separation was executed on a fused silica dimethylpolysiloxane capillary column (HP-1; USP G2 phase having dimensions, 60 m length × 0.53 mm dia & 5 μm film thickness). The validation of optimized method was carried out in accordance with relevant validation principles. The authenticated procedure was noticed to be specific, precise, linear, accurate, robust and rugged with concentration ranging from lowest quantification level (LQL) to 200% specification level for each residual organic solvent impurities (methanol, ethanol, acetone, isopropyl alcohol, dichloromethane, methyl tert-butyl ether, ethyl acetate, tetrahydrofuran, cyclohexane and toluene). The established technique was productively useful to determine the residual solvent impurities in fosaprepitant dimeglumine.

Headspace Gas ChromatographyFlame Ionization Detector Method for Organic Solvent Residue Analysis in Dietary Supplements

An analytical method has been developed for the identification and quantification of 20 organic solvent residues in dietary supplements. The method utilizes a headspace sampler interfaced with gas chromatography and flame ionization detection. With split injection (5:1) and a DB-624 column, most of the organic solvents are separated in 9 min. The method has been validated and was found to be relatively simple and fast, and it can be applied to most common organic solvent residues. With the mass detector, the method was able to identify organic solvents beyond the 20 standards tested.

The Quality Estimation of Different Tobacco Types Examined by Headspace Vapor Analysis

In order to judge the quality of tobacco leaf, it is necessary to conduct sensory smoke evaluations. However, these are subjective and the results are difficult to quantify. Therefore, we have attempted to establish a quantitative method for evaluating tobacco quality by comparing results of headspace analysis. Forty-seven leaf samples of different types (flue-cured, Burley, Oriental) were analyzed. The first step in this study was to have a panel of experts smoke cigarettes made from the test tobaccos and have them evaluate 10 sensory attributes. The scores were then analyzed by the technique of principal component analysis (PCA). Results showed that the score for the flavor note attribute indicated the type of tobacco and the scores of the other 9 attributes were combined as a total to indicate smoking quality. Following the sensory study, headspace vapors of the test tobaccos were analyzed with a headspace sampler, gas chromatography, mass spectroscopy system (HS-GC-MS), in which the gas sampling loop and the HS-GC transfer line were deactivated. In order to obtain conditions for good reproducibility, the heating temperature and time of the headspace vials were examined. PCA was performed for the headspace vapor (HSV) analysis results for 31 selected peaks. The first and second principal components could be used to classify tobacco types. The third principal component partially indicated differences of smoking qualities. Finally, multiple regression analysis was performed on the HSV analysis results in order to estimate the smoking quality scores. The regression model of all samples combined had a low regression coefficient. Then, we separated the results of the three tobacco types, as we considered that the headspace data might reveal information about the classifications themselves. The final outcome was a regression model that could be applied to each type with a higher accuracy. The variables that entered the models were compared.