Five 11α, 12α-epoxy pentacyclic triterpenoid saponins with antithrombus activities from Glechoma longituba.

The present study reports the phytochemical investigation of n-butanol-soluble extracts of Glechoma longituba. Five new oleanane-type triterpenoid saponins with an 11α, 12α-epoxy unit, named glechomanosides A - E, were isolated from the n-butanol soluble fraction of G. longituba. Their chemical structures were established using HRESIMS, IR, 1D NMR, and 2D NMR techniques. The compounds were all evaluated for their antithrombus activities by monitoring thrombin time (TT), prothrombin time (PT), activated partial thromboplastin time (APTT), and antiplatelet aggregation assays. These results suggest that G. longituba might be a candidate plant source of an interesting antithrombotic activity.

[Research on fast detecting tomato seedlings nitrogen content based on NIR characteristic spectrum selection].

In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.

[Study on germination rate of zoysia (Zoysia japonica Steud.) seeds using near infrared reflectance spectroscopy].

With 37 zoysia seed samples with different germination rates ranging from 58.5% to 92%, harvested in different years from 2009 to 2011 and from different locations of China, a model for determining germination rate of zoysia seeds was tried to be built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). All the seeds samples were divided into two groups: calibration set (including 28 samples) and validation set (including 9 samples). The results showed that with the spectral range from 6 000 to 7 000 cm(-1) and 6 main components, there was a better fitting between the predictive value and true value. Determination coefficients (R2) of calibration and validation sets are 90.73% and 91.80%, the coefficients of correlation are 0.986 6 and 0.987 2, the standard errors are 9.80 and 9.47, and the average absolute errors are 7.64% and 6.98% respectively. Even with different calibration samples, the models have a high determination coefficient (R2 over building of NIR model for determining 90%), low standard errors (about 10.00) and low absolute errors (about 8.00%). The building of NIR model for determining germination rate of zoysia seeds could promote the application of high quality seeds in production.

[Study on varieties identification of Kentucky bluegrass using hyperspectral imaging and discriminant analysis].

Hyperspectral images of six varieties of Kentucky bluegrass were acquired using hyperspectral imager (550-1 000 nm) and the leaf spectral properties were extracted. Wilks' lambda stepwise method was used and 9 optimal wavelengths were selected from the original 94 wavelengths and the discriminant models for varieties identification of Kentucky bluegrass were built based on Fisher' s linear discriminant function. The results showed that the Fisher' s linear discriminant model with 9 wavelengths achieved classification accuracies of 100% for both training and testing samples. While for the models with three wavelengths and six wavelengths, classification accuracies reached 83.3% and 96.7% for the testing samples, respectively. It indicates that hyperspectral images combined with discriminant analysis might be a good method to identify the varieties of Kentucky bluegrass.

[Spectrum analysis of crop and weeds at seedling].

The infestation information on field weeds is the basis of variable spraying herbicides. It was found that the method using the spectral characteristics of plant is superior in real-time respect. The Fourier transform infrared spectrum technique was applied to measure the reflectance of wheat and weeds in the range from 700 to 1100 nm. The discrimination analysis was done using the SPSS software. Firstly, the source spectrum data were compressed and normalized. Secondly, the characteristic wavelengths were selected by using stepwise method. Thirdly, the discrimination model was set up to use the selected wavelengths as the variables for detecting wheat and weeds. It was shown by the result of discrimination analysis that the correct classification rate of wheat and weeds detection with the selected wavelength points achieved 97%. In addition, the selected wavelength points were marked in the "red edge" of reflectance within some range, and the rate of correct classification increased with the increase in the numbers of the selected wavelength points. According to the selected wavelength points, the proper filters were chosen to perform the multi-spectral images captured and processed with the machine vision system.