ABSTRACT
The volatile oil of Curcumae Rhizoma has many active components,which are the key to the quality of Curcumae Rhizoma. Exploring the difference between volatile oil of different kinds of Curcumae Rhizoma facilitates the quality control and rational application of resources. In this study,GC-MS was applied to realize online qualitative and semi-quantitative analysis of the chemical composition spectrum of volatile oil from Curcuma wenyujin( CW),C. phaeocaulis( CP),and C. kwangsiensis( CK). Forty components were identified and their fingerprints were compared and evaluated. Hierarchical cluster analysis( HCA),principal component analysis( PCA),and orthogonal partial least squares discrimination analysis( OPLS-DA) were adopted to analyze the overall and outlier data. The results showed that the whole data could be divided into three kinds according to each analysis mode,and the volatile components of Curcumae Rhizoma vary greatly among species. PCA explored the difference between outliers and the mean value of the group and found that some volatile oils from CW may be greatly affected by the origin. By OPLS-DA,the samples from Zhejiang were able to gather,but those from Guizhou remained isolated,indicating the influence of growing environment on Curcumae Rhizoma metabolites. Based on VIP results combined with the heat map,characteristic volatile oil components of Curcumae Rhizoma from different varieties were screened out: curdione and linalool for CW; 2-undecanone for CP; humulene,γ-selinene,and zederone for CK. The GCMS method established in this study describes Curcumae Rhizoma samples comprehensively and accurately,and the characteristic components screened based on chemometrics can be used to distinguish Curcumae Rhizoma from different varieties and give them unique pharmacodynamic significance,which is fast,convenient,stable,and reliable and supports the rational application of Curcu-mae Rhizoma resources. It is found that the region of origin has great influence on CW,which is worthy of further study.
Subject(s)
Oils, Volatile , Curcuma , Gas Chromatography-Mass Spectrometry , Principal Component Analysis , RhizomeABSTRACT
This study aimed to establish a rapid and accurate method for identification of raw and vinegar-processed rhizomes of Curcuma kwangsiensis, in order to predict the content of curcumin compounds for scientific evaluation. A complete set of bionics recognition mode was adopted. The digital odor signal of raw and vinegar-processed rhizomes of Curcuma kwangsiensis were obtained by e-nose, and analyzed by back propagation(BP) neural network algorithm, with the accuracy, the sensitivity and specificity in discriminant model, correlation coefficient as well as the mean square error in regression model as the evaluation indexes. The experimental results showed that the three indexes of the e-nose signal discrimination model established by the neural network algorithm were 100% in training set, correction set and prediction set, which were obviously better than the traditional decision tree, naive bayes, support vector machine, K nearest neighbor and boost classification, and could accurately differentiate the raw and vinegar products. Correlation coefficient and mean square error of the regression model in prediction set were 0.974 8 and 0.117 5 respectively, and could well predict curcumin compounds content in Curcuma kwangsiensis, and demonstrate the superiority of the simulation biometrics model in the analysis of traditional Chinese medicine. By BP neural network algorithm, e-nose odor fingerprint could quickly, conveniently and accurately realize the discrimination and regression, which suggested that more bionics information acquisition and identification patterns could be combined in the field of traditional Chinese medicine, so as to provide ideas and methods for the rapid evaluation and stan-dardization of the quality of traditional Chinese medicine.
Subject(s)
Curcumin , Electronic Nose , Acetic Acid , Bayes Theorem , Curcuma , Neural Networks, Computer , RhizomeABSTRACT
The complete genomes of more cyanobacterial strains have been completed for sequence, and genetic engineering of cyanobacteria has evolved in the post-genome era. Since Kaneko and colleagues had completed the sequence for the complete genome of Anabaena sp. PCC 7120 in 2001, functions of some genes in this genome, including fructose-1,6-bisphosphate aldolase (FBA) gene, have been predicated using the method of bioinformatics. However, little information is available regarding whether this gene can encode FBA and its product characteristics of related enzyme. Here, to explore this information, the predicted II-FBA gene-encoding region in Cyanobase database was cloned by PCR method and then ligated into pET-32a to generate the expression vector, pET-FBA-II. The results of SDS-PAGE indicated that the expression level of the expected target polypeptide was approximate 23.4 percent as compared to total protein and the molecular weight is about 40 kDa as compared to the protein molecular marker. The results of enzyme activity analysis showed that the activity of II-FBA was ~11.8 U per mg protein and owned a standard activity of II-FBA. To sum up, the results not only prove the functional prediction of this II-FBA gene from the Cyanobase database, but also provide the important conditions for further studying its physiological and biochemical characteristics and functions of the gene expression product.
