الفهرس 
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Abstract
Herbal medicine is the oldest form of healthcare known to humanity and has been used in all cultures throughout history. In spite of the great advances observed in modern medicine in recent decades, plants still playing an important role in the clinical therapy in many oriental countries. According to the World Health Organization (WHO), about 80% of the world population, especially in Asia, Africa and Latin America, still use herbs and other traditional medicines for their primary health care needs. According to the Center for Drug Evaluation and Research, one of the most crucial concerns for botanical drugs is ensuring that the therapeutic effect is consistent by certifying batch-to-batch reproducibility, due to the heterogeneous and complex nature of botanical drugs and possible variation in the active constituents. However, the unpredictable quality of herbal medications is an obstacle to globalization and modernization.
Unlike synthetic drugs, the therapeutic effect of herbal medicines and their preparations can be attributed to the holistic effects of their multiple phytochemicals and multi-targets. Analyzing a single herbal product is difficult; however, the issue becomes more complicated when dealing with polyherbal mixtures. The physical and chemical qualities, as well as morphological and microscopical identification, are used to assess the quality of herbal products. In addition, a few chemical markers or pharmacologically active components are selected to assess the quality and authenticity of the herbal preparations. However, by all the above methods, the complexity of herbal medicine cannot be elaborated. Therefore, the development of a suitable quality control method for polyherbal products is of high importance.
Fingerprint analysis was accepted by the WHO as a methodology for the assessment of natural products. Chemical fingerprints are characteristic profiles reflecting the overall nature or “complete information” of herbal medicines, and are therefore useful in their quality control and ensures consistency of product therapeutic efficacy. Fingerprints can be obtained using spectroscopic, chromatographic or electrophoretic techniques either alone or coupled with multivariate analyses for achieving quality control of different herbal medicines.
Although multivariate analysis of different spectroscopic and chromatographic methods for the analysis of herbal products have already been published, however, studies describing the applications of this approach in assessing the quality of polyherbal products are limited.
The objective of this thesis is to develop simple, rapid, inexpensive and reliable methods to evaluate the consistencies of herbal products, qualitatively and quantitatively, by applying chemometric analysis on different spectroscopic and chromatographic fingerprints data. Two polyherbal formulas were selected for the study, the ACO (Trachyserimum ammi, Cuminum cymenum, Origanum majorana Linn; 1:1:1 ratio) and ACFOR (T. ammi Linn., C. cymenum, Foeniculum vulgare, Origanum majorana and Ruta graveolens; 1:1:1:0.25:1 ratio)
Different ACO formula samples (composed of three herbs) were prepared to represent the accepted formula (equal ratios of the herbs as well as a 10% excess in one component) and unaccepted formulas (from 15% to 30% excess in one herb). Another polyherbal formula (ACFOR) containing five herbs in a 1:1:1:0.25:1 ratio with 10% excess in one component (accepted formula) was prepared, in addition to other formula samples containing excess in one constituent in different proportions (20% - 50%), representing the unaccepted formulas.
Central composit design was applied to study the effect of the following factors: sonication time (X1), stirring time (X2), and extraction temperature (X3) for extraction optimization of the ACO formula samples. The volatile oil was extracted using microwave assisted distillation. UV and ATR-FTIR spectroscopic fingerprints as well as the HPLC and GC-MS chromatographic fingerprints were recorded. The fingerprints converted into a data matrix and subjected to chemometric analysis such as the unsupervised pattern recognition techniques: Principal Component Analysis (PCA); supervised pattern recognition techniques: Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Square-Discriminant Analysis (PLS-DA); the multivariate regression techniques: Partial Least Square (PLS) and Orthogonal Projection to the Latent Structure (OPLS).