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In this study, the antioxidant and bile salt binding activities of flavonoids from Aronia melanocarpas' leaves (AMF) by ultrasound-assisted extraction. Based on single factors (liquid-solid ratio, ethanol concentration, ultrasonic power and ultrasonic time) and response surface method, the extraction process of AMF was optimized with flavonoid yield as the index. The antioxidant activity of AMF was evaluated by DPPH·, ABTS+· scavenging rate and reduction power. The bile salt binding ability of AMF was investigated by using sodium cholate, sodium taurocholate and sodium glycocholate as binding objects. The results showed that the optimal extraction conditions were as follows: The liquid-solid ratio of 61:1 mL/g, the ethanol concentration of 60%, the ultrasonic power of 100 W, and the ultrasonic time of 59 min. Under the optimized conditions, the yield of flavonoids was 24.22%±0.29%, which approximated the predicted value (24.27%). The IC50 of DPPH and ABTS+ radicals scavenging capacities of AMF were 0.32 and 0.16 mg/mL, respectively. The AMF demonstrated the strongest reducing power with an absorbance value of 1.09 at the sample concentration of 0.5 mg/mL. The IC50 of AMF for the binding rate of sodium cholate, sodium taurocholate and sodium glycocholate were 0.76, 3.01 and 6.49 mg/mL, respectively. In summary, AMF exhibited strong antioxidant and bile salt binding capacity. This study could provide a basis for AMF to further develop natural antioxidants and hypolipidemic drugs.

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Normalized difference vegetation index (NDVI) can be used as an ideal indicator of how the crops grow, with which the crops in different growing stages, harmful insects and diseases, water and fertilizer, and the yields can be well predicted. However, the accuracy of NDVI does not remain unchanged due to the ever-changing environmental factors, apart from the impact from the crops growing factors in different stages. The article investigates, using GreenSeeker, the soybean NDVI of its three growing stages of seeding, flowering & podding and maturing in successive hours as the testing unit, for a more accurate monitoring of NDVI affected by air temperature, humidity, solar radiation, and wind speed, etc. in different periods of a day. The results show that the soybean NDVI values, being dynamic in different periods of a day, become smaller from 08:00 or 09:00 am in the morning, reach the valley in 14:00 pm in the afternoon, then give a gradual rise, the whole dynamic process is similar to a quasi-parabola. Moreover, the soybean NDVI values demonstrate different daily variation ranges in different soybean growing stages. From 08:00 am to 18:00pm, the biggest daily variation ranges are respectively among 0.13-0.23, 0.08-0.17 and 0.09-0.19, the biggest relative daily variation ranges are respectively among 20%-26%, 9%-19% and 11%-24%. The correlation study of the soybean NDVI values and the environment meteorological factors show that the changes in soybean NDVI values in its three growing stages are upon great influence by the solar radiation, air temperature, humidity and the wind speed. In four meteorological factors, the solar radiation and air temperature have a negative correlation with the soybean NDVI values at three stages (R2 =0.424, 0.503, 0.631 and 0.602, 0.743, 0.757), humidity shows a positive correlation with the soybean NDVI values(R2 =0.281, 0.435 and 0.654), and the wind speed exerts different influences in different soybean growing stages, specifically, a negative correlation in seeding, flowering & podding (R2 = 0.432, 0.218), and in maturing stage(R2 = 0.127). The regression models were set up to test the impact of four meteorological factors on soybean NDVI and analyze the quantitative relations among them, namely Partial Least Squares (PLS), Stepwise Regression and Ridge Regression. It was found that among four meteorological factors affecting soybean NDVI values, the major factors are the solar radiation and the air temperature, while the minor ones are the wind speed and humidity and their influence on soybean NDVI values can be neglected. In significance tests, predictive accuracy of the three regression models for soybean NDVI in all three growing stages are all statistically significant (P<0.01). By contrast, Ridge Regression has a slightly higher coefficient than partial least squares (PLS) and Stepwise Regression, while the latter two models have almost the same correlation. In contrast of the predictive values and the real test results of the three regression models for soybean NDVI, the Ridge Regression ranks the highest on predictive accuracy, with the Root Mean Square Error (RMSE) of 0.034˛0.018 and 0.016 and R2of 0.820, 0.908 and 0.934 in three stages of seeding, flowering & podding and maturating, followed by a less accurate predictive level of Stepwise Regression and the least accuracy of PLS. Regression model can have a better prediction of daily variation trend of soybean NDVI values and a better accuracy of NDVI monitoring. [ABSTRACT FROM AUTHOR]