SU MMARYGrass pea (Lathyrus sativus) is a potentially valuable feed and food crop in semi-arid regions. Much work has been done on lowering toxicity and on selection of low toxicity varieties, while research on the eco-physiological characteristics of grass pea is very rare. Stomatal character, photosynthetic character and seed chemical composition were measured in four varieties of L. sativus to investigate their relationships at different water availabilities. For L. sativus cv. Yongshou (YS), L. sativus cv. Dingxi (DX), L. sativus cv. Heilongjiang (HLJ) and L. sativus cv. Xide (XD), stomatal density was in the order XD>HLJ>DX>YS under both control and drought conditions. Stomatal aperture, photosynthetic rate (Pn), transpiration rate (E), and the concentrations of seed b-N-oxalyl-L-a, b-diaminopropionic acid (ODAP), protein and starch were in the order YS>HLJ>DX>XD, while the opposite order was found for water use efficiency (WUE). Under drought conditions, stomatal aperture, Pn and E were lower than those under the control, while the other parameters were higher. A significant positive correlation was observed between stomatal density and WUE, while negative correlations were found between stomatal density and the remaining parameters. Obvious positive correlations were also observed between stomatal aperture and Pn, E, the concentrations of seed ODAP, protein and starch, while a negative correlation appeared between stomatal aperture and WUE. Under drought conditions, R 2 values were more comparable with the control. Intriguingly, the R 2 values of stomatal aperture were higher than of stomatal density, especially under drought conditions. These results indicate that stomatal aperture may be more closely related to photosynthetic character and seed chemical composition in grass pea, and water deficit may enhance the correlations.

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Classi¼cation models of the fragrance properties of chemical compounds were performed using linear and non-linear models. The dataset was divided into three classes on the basis of their fragrances: apple, pineapple and rose. The three-class problem was ¼rst explored by a linear classi¼er approach, using linear discriminant analysis (LDA). A more accurate prediction model, the non-linear machine-learning technique, support vector machine (SVM), was subsequently investigated. Descriptors calculated from the molecular structures alone were used to represent the characteristics of compounds. The model containing four descriptors founded by SVM showed better predictive ability than LDA. The accuracy in the prediction for the three datasets was 96.6%, 80.0% and 100% for SVM, respectively. The results indicate that SVM can be used as a powerful modelling tool for QSAR studies and the selected descriptors can represent the fragrances of these chemical compounds.

Quantitative-structure property relationships, as related to peptide electrophoretic mobility, are presented in this review. The methods of discussion ranged from linear to non-linear method. It is the intent that the review will provide the present state of knowledge and current trends in this area for a new investigator in this field.