[Effects and mechanisms of lotus leaf water extract on lipid metabolism of adult experimental obesity rats].

OBJECTIVE: To investigate the lipid metabolic effect and mechanism of water extract of lotus leaves(Traditional Chinese Medicine). METHODS: Isolated SD rat lipid tissues were suspended in organ baths containing Krebs solution, and the effect of lotus leaf water extract on free fatty acids (FFA) release was observed; The experimental obesity rat model was established by feeding them high glucose and fat diets, then the changes of body weight and blood lipid were measured in the model rats after intragastric administration with water extract of lotus leaves for four weeks, and the expressions of peroxisome proliferator-activated receptor gamma (PPAR-γ) and leptin were examined by RT-PCR and immunohistochemical. RESULTS: The ex vivo experiment showed that water extract of lotus leaves effectively promoted the FFA release from isolated lipid tissues. In vivo experiment, similarly to Orlistat, water extract of lotus leaves(60 mg/kg)markedly decreased the body weight and blood lipid of experimental obesity rats(P<0.05), and obviously reduced the expressions of PPAR-γ and leptin(P<0.05). CONCLUSIONS: Water extract of lotus leaves greatly improves the expression of PPAR-γ and leptin, which can promote the lipid mobilization and dissolution, reduce the body weight and blood lipid of adult rats with experimental obesity, therefore is expected to be developed into lipid-lowering diet pills.

[Rapid determination of beet sugar content using near infrared spectroscopy].

In order to classify and set different prices on basis of difference of beet sugar content in the acquisition process and promote the development of beet sugar industry healthily, a fast, nondestructive, accurate method to detect sugar content of beet was determined by applying near infrared spectroscopy technology. Eight hundred twenty samples from 28 representative varieties of beet were collected as calibration set and 70 samples were chosen as prediction set. Then near infrared spectra of calibration set samples were collected by scanning, effective information was extracted from NIR spectroscopy, and the original spectroscopy data was optimized by data preprocessing methods appropriately. Then partial least square(PLS)regression was used to establish beet sugar quantitative prediction mathematical model. The performances of the models were evaluated by the root mean square of cross-validation (RMSECV), the coefficient of determination (R2) of the calibration model and the standard error of prediction (SEP), and the predicted results of these models were compared. Results show that the established mathematical model by using first derivative (FD) and standard normal variate transformation (SNV) coupled with partial least squares has good predictive ability. The R2 of calibration models of sugar content of beet is 0.908 3, and the RMSECV is 0.376 7. Using this model to forecast the prediction set including 70 samples, the correlation coefficient is 0.921 4 between predicted values and measured values, and the standard error of prediction (SEP) is 0.439, without significant difference (p > 0.05) between predicted values and measured values. These results demonstrated that NIRS can take advantage of simple, rapid, nondestructive and environmental detection method and could be applied to predict beet sugar content. This model owned high accuracy and can meet the precision need of determination of beet sugar content. This detection method could be used to classify and set different prices on basis of difference of beet sugar content in the acquisition process.