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In the Xiangxi region of western Hunan province, China, 335 taxa belonging to 87 families and 119 genera are utilised as wild vegetables. In order to take advantage of this naturally occurring resource we examined the horticultural and the associated socio-economic aspects of these taxa. Wild vegetables, as the mainstay of human diet and Chinese traditional medicines, have played an important role in the daily life and income of local ethnic groups for centuries. We examine candidate species for their prevalence and their potential to offer returns, for example in cereal production and tourism, and indicate horticultural management and processing technologies which may exploit wild vegetable availability.

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Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A method based on a genetic algorithm interval partial least squares regression (GAiPLS) combined successive projections algorithm (SPA) was proposed for variable selection in NIR spectroscopy. GAiPLS was used to select informative interval regions among the spectrum, and then SPA was employed to select the most informative variables and to minimize collinearity between those variables in the model. The performance of the proposed method was compared with the full-spectrum model, conventional interval partial least squares regression (iPLS), and backward interval partial least squares regression (BiPLS) for modeling the NIR data sets of pigments in cucumber leaf samples. The multiple linear regression (MLR) model was obtained with eight variables for chlorophylls and five variables for carotenoids selected by SPA. When the SPA model was applied to the prediction of the validation set, the correlation coefficients of the predicted value by MLR and the measured value for the validation data set (r(p)) of chlorophylls and carotenoids were 0.917 and 0.932, respectively. Results show that the proposed method was able to select important wavelengths from the NIR spectra and makes the prediction more robust and accurate in quantitative analysis.

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Objective to optimize the isolation condition of Epidermal Cell Protoplasts within the Allium cepa L. Methods According to orthogonal experiment design L 25 5 6 of enzyme, osmotic pressure stabilizer(D-Mannitol), pH, enzymolysis time, we got the optimal isolation condition by the number of protoplasts. Results The optimal isolation condition is showed in results: enzymolysis time 2h, pH 5.5, D-Mannitol 0.6 mol/L, 1.5% cellulose and 0.1% pectolyase and 0.1%BSA, temperature 28.It also means that optimal isolation condition is D 5 B 3 C 4 A 2.Conclusion The optimal isolation condition is viable.