Study On The Quality Control Of Some Food And Traditional Chinese Medicine Samples By The Application Of Fingerprints And Chemometrics

Fingerprinting technology combined with chemometrics have been widely applied in the quality control of food and traditional Chinese medicine (TCM) products, which can effectively solve the problem of the intrisic quality control of food and TCM, and ensure the attributes of foods and pharmaceutical effects of TCM. Fingerprinting technology has the characterics of comprehensiveness, integrity, hierarchy, relevance, and fuzziness, the chemical information of food and TCM can be obtained comprehensively using this method. Therefore, it is important and necessary to construct a rapid and effective food and TCM quality control system for people health. In this study, some common food and TCM samples, such as Cynanchum stauntonii (CS) and its adulterants(Cyannchum atrati (CA) and Cynanchum paniculati (CP)); Chinese hawthorn fruit; Mint (Mentha haplocalyx Briq.); Perilla frutescens (L.) Britt, were used as samples in this study. The analytical methods included near-infrared spectroscopy (NIRS), high performance liquid chromatography-diode array detector (HPLC-DAD), ultra high performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS), and head space-gas chromatography-mass spectrometry (HS-GC-MS), several chemical components and antioxidant activity in Chinese hawthorn and Mint were determined using standard analytical methods, incorporating with several chemometric methods for data analysis, aiming at providing novel methods for the quality control and evaluation of the above-mentioned materials. The main conclusions were as follows:1. A rapid near-infrared spectroscopy (NIRS) analytical method which was supported by multi-variate calibration, e.g. partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN) was developed, in order to quantify the TCM and the adulterants. In this work, Cynanchum stauntonii (CS), a commonly used TCM, in mixtures with one or two adulterants-two morphological types of TCM, Cynanchum atrati (CA) and Cynanchum paniculati (CP), were determined using NIR reflectance spectroscopy. The three sample sets, CS adulterated with CA or CP, and CS with both CA and CP, were measured in the range of800-2500nm. Both PLSR and RBF-ANN calibration models provided satisfactory results, even at an adulteration level of5%(w/w), but the RBF-ANN models with better root mean square error of prediction (RMSEP) values for CS, CA, and CP arguably performed better. Consequently, this work demonstrates that the NIR method of sampling complex mixtures of similar substances such as CS adulterated by CA and/or CP is capable of producing data suitable for the quantitative analysis of mixtures consisting of the original TCM adulterated by one or two similar substances, provided the spectral data are interrogated by multi-variate methods of data analysis such as PLSR or RBF-ANN.2. Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn(Crataegus pinnatifida Bge. var. major) frruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods:linear discriminant analysis, partial least squares-discriminant analysis, and back-propagation artificial neural networks were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least squares regression, back propagation artificial neural networks and least squares-support vector machines (LS-SVM), were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity and validated by prediction data sets.3. Mint (Mentha haplocalyx Briq.) obtained from different geographical regions was characterized using head space-gas chromatography-mass spectrometry (HS-GC-MS) and ultra high performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS) and followed multivariate data analyses. Principal component analysis (PCA), and the rank-ordering multi-criteria decision making (MCDM) PROMETHEE and GAIA score plots from HS-GC-MS and HPLC-DAD data sets showed a clear distinction among mint from three different regions in China. Classification results showed that satisfactory performance of the prediction ability for back propagation-artificial neural networks (BP-ANN) and partial least suqares-discriminant analysis (PLS-DA). The major compounds that contributed to the discrimination were chlorogenic acid, unknown3, kaempherol7-O-rutinoside, salvianolic acid L, hesperidin, diosmein, unknown6, and pebrellin in mint on the basis of regression coefficients of PLS-DA model. The results indicated that HS-GC-MS and UPLC-Q-TOF-MS fingerprints in combination with chemometric analyses can be used to distingusih the grographical origins of plants and identify compounds were responsible for discrimination.4. A novel near-infrared spectroscopy (NIRS) has been researched and developed for the simultaneous analyses of the chemical components and associated properties of Mint (Mentha haplocalyx Briq.) samples, which are commonly used for tea brewing, namely, total polysaccharide content (TPSC), total flavonoid content (TFC), total phenolic content (TPC), and total antioxidant activity (TAA). To resolve the NIRS data matrix for such analyses, LS-SVM was found to be the best chemometrics method for prediction although it was closely followed the RBF-PLS model; notably, the commonly used PLS was unsatisfactory in this case. Additionally, principal component analysis (PCA) and hierarchical cluster analysis (HCA), were able to distinguish the Mint samples according to their four provinces of origin; this was further facilitated with the use of the chemometrics classification methods-K-nearest neighbors (KNN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA). In general, given the potential savings on sampling and analysis time as well as on the costs of special analytical reagents required for the standard individual methods, NIRS offers a very attractive alternative for the simultaneous analysis of Mint samples.5. Perilla frutescens (L.) Britt samples from different geographical origins in China were distinguished using liquid chromatography incorporated with chemometrics method, a total of84samples were used for data analysis. The chromatographic fingerprints demonstrated the chemical compositons of the samples, enabling the discrimination of samples from different origins is possible. Prior to classification, the datasets were subjected to pretreatment including baseline correction and retention time alignment. Principal component analysis (PCA) was performed to the aligned and compressed data matrix, respectively, to investigate the data distribution and evaluate the data quality, seperation among the three sets of samples was observed in the PCA score plots. Partial least squares-discriminant analysis (PLS-DA) provided the recognition ability and prediction ability of92.8%and89.6%, respectively, and a relative satisfactory results were obtained in this study.