| نویسندگان | سعید معصوم,منیژه بهرامی,صالحه قاهری |
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| همایش | بیست و یکمین کنفرانس شیمی تجزیه |
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| تاریخ برگزاری همایش | ۲۰۱۵-۳-۱۴ |
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| محل برگزاری همایش | اهواز |
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| نوع ارائه | سخنرانی |
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| سطح همایش | ملی |
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چکیده مقاله
Herbal medicines (HMs) are getting more and more popular globally for improving the health conditions of human beings as well as preventing and curing diseases. Because of widespread use of herbal medicines and their essential oils, they must be analyzed by modern techniques to ensure their quality, safety and to reveal their compositions. Gas chromatography- mass spectrometry (GC-MS) has been introduced as the most appropriate analytical technique for this type of analysis [1]. But because of complexity of their chemical composition the analysis may be faced to some common chromatographic problems such as overlapping peaks. So accurate quantitative and qualitative analysis could be failed.
Chemometric techniques provide good opportunity for mining more useful chemical information from the original overlapped data sets. Multivariate curve resolution (MCR) methods offer the possibility of resolution, identification and quantitation of all the components in an unknown mixture without previous chemical and physical separation [2]. But because of the rotational ambiguity there are infinite numbers of solution for an overlapped dataset [3]. However, when appropriate constraints are applied the feasible solutions can be limited considerably, and in favorable cases, even give a unique solution coincident with the true one. In this study the abilities of different MCR methods under different constraints such as nonnegativity, unimodality, selectivity and trilinearity in simulated GC/MS datasets are evaluated to find out which method can represent a true solution between all of feasible solutions. After that some challenging areas of the total ion chromatogram (TIC) of Rosemary, Thyme and Mindium Laevigatum with overlapped peaks were analyzed by the best MCR methods under the most proper constraints for qualitative and quantitative analysis.