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Peanut is an important oil crop in China. Peanut seed itself and its processed products can produce a unique aroma, and its aroma quality is one of the most important indicators to determine the market. Peanut seed can produce different aroma substances through different processing methods, and peanut aroma is composed of a variety of volatile substances, including pyrazines, pyrroles, furans, pyridine, aldehydes, ketones, alcohols, hydrocarbons, aromatic compounds and other compounds, and different volatile substances have different aromas. There are three main sources of aroma substances in peanut: Maillard reaction, fat oxidation and thermal degradation reaction, and degradation reaction of amino and sugars. At present, there are few studies on the biosynthetic pathways of aroma substances in peanut. With the development of science and technology, more and more volatile compounds have been identified. At present, peanut aroma is mainly extracted by headspace solid phase microextraction and detected by gas chromatography-mass spectrometry. The formation of peanut aroma is mainly affected by factors such as genotype, processing mode and processing time, seed water content and other factors. A large number of literatures have shown that cultivation modes affect the accumulation of aroma substances in rice, but there is no report on the effect of different cultivation mode on the accumulation of aroma substances in peanut. At the same time, the use of volatile compounds can also inhibit the pollution of peanut aflatoxin, reduce the economic loss of peanuts, and use volatile substances to distinguish different kinds of vegetable oil. Taking peanut seed as the object, this paper reviwed the composition analysis of peanut aroma substances, the classification of peanut aroma substances, the extraction and analysis methods of peanut aroma substances, the factors affecting the formation of peanut aroma substances and the influence of Aspergillus flavus pollution by peanut aroma substances, providing theoretical basis for the study on the formation of peanut aroma quality.

[Introduction] In the current context of increasing energy demand and environmental conservation advocacy, it is necessary to seek alternative energy sources for traditional fuel generators, and better fuels can be identify by studying the evaporation characteristics of fuels. [Method] The droplet suspension experiments were conducted to investigate the evaporation characteristics of droplets of methyl oleate fuel with ethanol contents of 0%, 20%, 30%, and 50% at the ambient temperature of 673 K and 773 K. [Results] The results indicate that as the ambient temperature increases from 673 K to 773 K, the evaporation rate of the blended droplets rises. Furthermore, with the continuous increase in ethanol contents, the difference in droplet evaporation rates between the two ambient temperatures becomes more pronounced. At lower ambient temperatures, the evaporation process of the droplets is relatively stable, with the droplet evaporation rate showing a gradual decrease trend. The addition of ethanol leads to an extension of the droplet's equilibrium evaporation stage, increasing the evaporation time and inhibiting the evaporation process. Conversely, at higher ambient temperatures, the evaporation process exhibits greater fluctuations, with the droplet evaporation rate showing a gradual increase trend and reaching its maximum at the ethanol content of 50%. The addition of ethanol induces micro-explosion events, reducing the evaporation time and enhancing the evaporation process. Moreover, increasing the ambient temperature and ethanol content promotes the occurrence, intensity, and frequency of micro-explosion events in droplets. [Conclusions] Based on the experimental results of droplet suspension experiments on the evaporation characteristics of methyl oleate and ethanol blended droplets, this study provides important data support and references for related fuel research and applications.

Aiming at the heavy and over load issue in operation of distribution network,a heavy overload forecasting model to achieve short-term forecast of heavy and overload events was established.Using the actual data of the business system,the proposed method was validated.The results show that the proposed method can describe the heavy and overload events more systematically and comprehensively,and the heavy overload forecasting model based on association rules performs well in the hit rate and accuracy rate.This method provides a new technical means that has certain practical value to enhance the distribution network management which could be seen as important experience of attempts in grid big data as well.