Authors:Yahya S. Al-Degs, Mohammed Al-Ghouti, and Gavin Walker
calibration (also known as chemometry) has found many applications in petrol chemistry, which extend from detecting impurities to multi-component determination [ 14 – 17 ]. The ultimate aim of multivariate calibration is to find useful relationships between
Authors:Hesham Salem, Nagiba Y. Hassan, Hayam M. Lotfy, and Sarah S. Saleh
This work presents a comparative study on the development and validation of two analytical techniques applied for the simultaneous determination of hydrocortisone acetate (HCA), fusidic acid (FSA), methyl paraben (MPB), and propyl paraben (PPB) formulated as a topical cream. The first technique was thin-layer chromatography (TLC)–densitometric method, which was developed by separating the four components on silica gel 60 F254 using methylene chloride–methanol–benzene in the ratio of 10:2:5, v/v, as the developing system, followed by densitometric measurement of the bands at 240 nm. The second technique was the chemometric method using two models: principle component regression model (PCR) and partial least squares (PLS). The suggested techniques were validated in compliance with the International Conference on Harmonization (ICH) guidelines and were successfully applied for the determination of the quaternary mixtures as prepared synthetically in laboratory and in the commercial topical pharmaceutical formulation.
The merits of chemometrics in categorizing different Egyptian olive chemovarieties based on their compositional integrity were implemented in this study. Fingerprints of 9 different olive leaves varieties cultivated in Egypt were established using reversed-phase high-performance thin-layer chromatography (RP-HPTLC) prior to and after post-chromatographic derivatization with natural product-polyethylene glycol (NP/PEG) reagent and image analysis using ImageJ® software in order to build 2 separate data matrices. The chromatographic fingerprints were separately subjected to unsupervised pattern recognition multivariate analysis to build 2 separate models using principal component analysis (PCA) and hierarchical clustering analysis (HCA) algorithms to explore the distribution pattern of different chemovarieties. The second model which involved olive samples’ fingerprints after post-chromatographic derivatization exhibited greater ability to reveal a broader spectrum of phytoconstituents with enhanced sensitivity. Densitometric RP-HPTLC quantification of oleuropein marker was compared to image analysis approach using Sorbfil TLC Videodensitometer® by newly developed and validated methods. Densitometry exhibited better performance characteristics than image analysis method and therefore was executed for determination of oleuropein concentration in the 9 Egyptian olive varieties. Oleuropein marker solely was found to be inadequate for standardization of olive leaves varieties. This study demonstrated a comprehensive approach for the rapid classification of different Egyptian olive varieties, which is crucial to warranting their chemical-consistency and, thereafter, effective consistency.
In case of spices or crude drugs of medicinal- and aromatic plant origin, sensory characteristics, especially odour, has great commercial importance. The instrumental sensory analysis the so-called 'electronic nose' has proved to be a significant, new and quick method in chemometry. The sensor signal responses (data recorded by the electronic nose instrument) of the electronic nose were evaluated by statistical methods, including principal component analysis (PCA) and canonical discriminant analysis (CDA) and the combination of these methods by applying the discriminant analysis on the first eight principal components. The aim of this paper is the comparative analysis of the above evaluation methods as data processing tools of the sensor signal response of the electronic nose (chemosensor array). The essential oil of oregano (Origanum vulgare subsp. hirtum) selected line No. 10 was compared to the oil distilled from the selected line No. 11; and dried root samples of lovage (Levisticum officinale) harvested at different times from the two- and three-year-old population, were investigated with electronic nose (NST-3320, AppliedSensor Sweden AB). Principal component analysis, as a first step of the evaluation, did not clearly distinguish either oregano or lovage samples. Further statistical evaluation of the original sensor signal responses of the electronic nose with canonical discriminant analysis improved the separation power of the model. The best separation could be achieved by the combination of the two methods, whereby canonical discriminant analysis was applied to the first eight principal components, which described 99% of the differences. In all cases more than 92%, while in several experiments 100% of cross-validated grouped cases were classified correctly. Based on the results, the application of the electronic nose and the combination of multivariate methods, PCA and CDA, could be an appropriate tool either for identification of cultivar to accelerate selection process or to distinguish crude drugs of different age or different harvesting period.
N ICOLAOU , N. & G OODACRE , R. ( 2008 ): Rapid and quantitative detection of the microbial spoilage in milk using Fourier transform infrared spectroscopy and chemometries . Analyst , 133 , 1424 – 1431