Study On The Fingerprints Of Some Traditional Chinese Medicine And Foodstuff With The Aid Of Chemometrics

Fingerprint technology has been more and more widely used in quality control of the Traditional Chinese Medicine or the Food. Various analysis and detection methods, combining with chemometrics multivariate calibration, were utilized for quality evaluation. It provide favorable conditions for Complex compounds analytical with the continually improvement of Chemometrics methods of pattern recognition. High performance liquid chromatography, gas chromatography and near infrared spectrum were used for quality control of several common traditional Chinese medicine or food on the fingerprint study in his paper.In the first chapter, a review on research status and development foreground was arised, which the conception, characteristics, research content and its importance for quality control of TCM were introdced mainly. In addition, the methods applied for fingerprint analysis of TCM were also reviwed, and the chemometrics methods were discussed specially. The trend of fingerprint analysis was mentioned in the end.In the second chapter, a high performance liquid chromatography with diode array detector (HPLC-DAD) was employed for the chromatographic fingerprint analysis and characterization of Rhizoma Curcumae, a traditional Chinese medicine (TCM). a genetic algorithm-partial least squares (GA-PLS) were applied for feature variables selection based on the 11 common peaks. Then, two chemometrics pattern recognition methods:principal component analysis (PCA) and K-nearest neighbor (KNN) were applied to investigate the mean chromatograms of Rhizoma Curcumae samples, which were obtained from four different provinces with two different processing methods. It was found that these Rhizoma Curcumae samples can be successfully compared and distinguished according to their origin and processing methods. The multiple results manifested that the proposed method can be efficiently used to control the quality of Rhizoma Curcumae and providing a reference method for chemical pattern recognition of other TCMs.In the third chapter, The present work aims to analyze the feasibility of different analytical measurement procedures for Rhizoma Curcumae species classification. In order to more comprehensively identify and quantify the chemical compounds qualified for discriminating different species, Gas chromatography-Mass spectra (GC-MS); High performance liquid chromatography-diode-array detector (HPLC-DAD) were carried out. Fifty-six Rhizoma Curcumae samples, which from three different species, were analyzed in terms of their volatile compounds by GC-MS, as well as their curcumin compounds by HPLC-DAD. A combined data matrix of chromatographic GC-MS and HPLC-DAD fingerprint profiles was constructed as the two-dimensional fingerprint data matrixes. Then, Principal component analysis (PCA) of the two-dimensional data obtained a more clearly clustering of objects with respect to the species of Rhizoma Curcumae. The two-dimensional data were rank-ordered with the use of the sparingly applied multiple criteria decision making (MCDM) ranking methods, PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid), and a comprehensive quantitative description of the data was obtained. Moreover, multivariate prediction models: Linear discriminant analysis (LDA), Back propagation-artificial neural networks (BP-ANN) and Least squares-support vector machine (LS-SVM) were established to compare the performance of the two-dimensional data and the single-dimensional data. The result indicated that the LS-SVM model using the two-dimensional data achieved 100% classification rate according to the different species.In the fourth chapter, an NIR spectroscopic method was researched and developed for the analysis of potato crisps (chips) chosen as an example of a common, cheap but complex product. Four similar types of the 'original flavour' potato chips from different manufacturers were analysed by NIR spectroscopy; as well, the quality parameters - fat, moisture, acid and peroxide values of the extracted oil were predicted. Principal component analysis (PCA) of the NIR data displayed the clustering of objects with respect to the type of chips.NIR spectra were rank-ordered with the use of the sparingly applied multiple criteria decision making (MCDM) ranking methods, PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid), and a comprehensive quantitative description of the data was obtained.The four traditional parameters were predicted on the basis of the NIR spectra; the performance of the Partial Least Squares (PLS), and Kernel Partial Least Squares (KPLS) calibrations was compared with those from Least Squares-Support Vector Machines (LS-SVM) method. The LS-SVM calibrations, which model better data linearity and non-linearity, successfully predicted all four parameters.In the fifth chapter, Chemometric treatment of near-infrared (NIR) and mid-infrared (MIR) combined spectra was used to discriminate Illicium Verum Hook. F. and its noxious adulterant huicium lanceolatum A. C. Smith. The two spectral ranges were used separately and conjunctively. Linear discriminant analysis (LDA) was applied as a classification technique on the multivariate analysis. In order to obtain a higher discriminate rate, LDA was preceded either by feature selected or variables compression. A Successive Projections Algorithm (SPA) was used for feature selected and a Discrete Wavelet Transform was used for data compression. The result indicated that most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression. This work has demonstrated that NIR and MIR methodology with the used of chemometrics can describe comprehensively qualitative properties to discriminate the Illicium Verum Hook. F. and Iuicium lanceolatum A. C. Smith.