ABSTRACT
The complex characteristics of p-sulfonated calix[n]arene (SCnA) and two tryptophans N-[(tert-butoxy) carbonyl]-tryptophan (trp-A) and N-carbobenzoxy-tryptophane (trp-B) were examined through various techniques. Spectrofluorimetry was performed at different temperatures to determine the stability constants and evaluate the thermodynamic parameters of the two complexes. The effect of pH on complex formation was estimated. According to the fluorescence data, the assumption about the steric hindrance of the tert-butyl group of trp-A and the phenyl group of trp-B was put forward. (1)H NMR was also performed to determine the binding interaction mechanism. Results showed that the indole benzene rings of the two tryptophans partly penetrated into the cavity of p-sulfonated calix[n]arene. The shift in Ha, Hb and Hc, Hd positions became more significant as the number of phenolic units of the calixarene ring increased. Molecular modeling of the complexes elucidated the assumption about the steric hindrance of the tert-butyl group of trp-A and the phenyl group of trp-B. These observations of molecular modeling computation are consistent with previous fluorescence data and (1)H NMR results.
Subject(s)
Calixarenes/chemistry , Sulfonic Acids/chemistry , Tryptophan/chemistry , Macromolecular Substances/chemistry , Models, Molecular , Molecular Structure , Spectrometry, FluorescenceABSTRACT
In order to identify Ilex Kudingcha, two kinds of models of artificial neural networks (ANN), i.e. competitive neural network and back propagation neural network, were used to analyze their infrared spectra. Ilex Kudingcha samples were collected by Fourier transform infrared (FTIR) spectra. Twenty five samples were gathered as a train set, and 11 samples as a test set, then their training was performed using two networks each. The results show that the identification of Ilex Kudingcha from different areas can be effectively performed with the competitive neural network and BP network, but the competitive neural network is used in the identification of different grades of Ilex Kudingcha. The results were better in training speed and accuracy with the competitive neural network. In conclusion, the competitive neural network combined with FTIR spectroscopy is a good method for the identification of Ilex Kudingcha.
Subject(s)
Beverages/analysis , Ilex/chemistry , Neural Networks, Computer , Spectroscopy, Fourier Transform Infrared/methods , Algorithms , Beverages/standards , China , Geography , Reference Standards , Reproducibility of ResultsABSTRACT
In the present article, two new indexes, common peak ratio and variant peak ratio, were applied and their values were calculated by means of sequential analysis, in which each Ilex Kudingcha sample s IR fingerprint spectra were set up and the common peak ratio sequences were arranged in order of size in comparision with other samples. As a result, the method could be used to distinguish Ilex Kudingcha of different areas and classes. The duel-index sequential analysis enables us to distinct two or more herb's IR fingerprints. It is a new method to analyze IR fingerprint spectra, and can used in line with the characteristics of traditional Chinese medicine.
