Assessment of the Genetic Diversity of Different Job's Tears (Coix lacryma-jobi L.) Accessions and the Active Composition and Anticancer Effect of Its Seed Oil.

Job's tears (Coix lachryma-jobi L.) is an important crop used as food and herbal medicine in Asian countries. A drug made of Job's tears seed oil has been clinically applied to treat multiple cancers. In this study, the genetic diversity of Job's tears accessions and the fatty acid composition, triglyceride composition, and anti-proliferative effect of Job's tears seed oil were analyzed using morphological characteristics and ISSR markers, GC-MS, HPLC-ELSD, and the MTT method. ISSR analysis demonstrated low genetic diversity of Job's tears at the species level (h = 0.21, I = 0.33) and the accession level (h = 0.07, I = 0.10), and strong genetic differentiation (GST = 0.6702) among all accessions. It also clustered the 11 accessions into three cultivated clades corresponding with geographical locations and two evidently divergent wild clades. The grouping patterns based on morphological characteristics and chemical profiles were in accordance with those clustered by ISSR analysis. Significant differences in morphological characteristics, fatty acid composition, triglyceride composition, and inhibition rates of seed oil were detected among different accessions, which showed a highly significant positive correlation with genetic variation. These results suggest that the seed morphological characteristics, fatty acid composition, and triglyceride composition may be mainly attributed to genetic factors. The proportion of palmitic acid and linoleic acid to oleic acid displayed a highly significant positive correlation with the inhibition rates of Job's tears seed oil for T24 cells, and thus can be an important indicator for quality control for Job's tears.

[Radial basis function networks and IR spectrometry applied for identification of official rhubarb samples].

The Fourier transform infrared spectrometry (FTIRS) and radial basis function neural network (RBF-NN) have been applied to develop classification models for identifying official and unofficial rhubarb samples. The original data were compressed from 775 variables to 49 variables by using wavelet transformation method. The compressed spectra with reduced variables maintain the characteristics of the IR spectra and speed up the network training process. The effects of network parameters including error goal and spread constant, were investigated. The rate of correct classification is up to 97.78% at optimized conditions. Results show that the combination of IRS and ANN can be used as fast and convenient tool for identification of Chinese herbal samples.