Abstract The aim of this study was to investigate whether anaerobic metabolites could induce volatile compounds and improve aroma of dried jujube (Ziziphus jujuba Miller). Jujube fruits were incubated in a polyvinyl chloride bag containing 5% CO2 and 95% N2 for up to 168 h at 25 °C and 3 samples were randomly removed every 6 h and oven dried to a moisture content of ≅ 20%. The volatile compounds of control and 5% CO2-pretreated Chinese jujube fruits were extracted by simultaneous distillation extraction and identified by gas chromatography-mass spectrometry(GC-MS). The acetaldehyde and ethanol contents were determined by gas chromatography (GC). The results indicated that a large accumulation of acetaldehyde and ethanol caused changes in aroma composition of dried jujube products and 5% CO2 pretreatment led to an increase in the levels of some compounds, particularly esters, acetaldehydes, and ethanol, whereas the amount of acids were decreased significantly. Principal component analysis showed that integrative scores of 5% CO2 pretreatment at 120 h were the highest, and aroma quality was better than that of the control. Relatively low concentrations of anaerobic respiration metabolites are good for jujube fruit aroma composition.

Floods are a serious hazard to life and property. The traditional probability statistical method is acceptable in analysing the flood risk but requires a large sample size of hydrological data. This paper puts forward a composite method based on artificial neural network (ANN) and information diffusion method (IDM) for flood analysis. Information diffusion theory helps to extract as much useful information as possible from the sample and thus improves the accuracy of system recognition. Meanwhile, an artificial neural network model, back-propagation (BP) neural network, is used to map the multidimensional space of a disaster situation to a one-dimensional disaster space and to enable resolution of the grade of flood disaster loss. These techniques all contribute to a reasonable prediction of natural disaster risk. As an example, application of the method is verified in a flood risk analysis in China, and the risks of different flood grades are determined. Our model yielded very good results and suggests that the methodology is effective and practical, with the potentiality to be used to forecast flood risk for use in flood risk management. It is also hoped that by conducting such analyses lessons can be learned so that the impact of natural disasters such as floods can be mitigated in the future.