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为探究浓醪发酵葛根酒最佳发酵条件,在考察了野葛和粉葛基本组成成分的基础上,采用野葛全粉和野葛淀粉配伍进行浓醪发酵葛根酒的研制,以发酵后酒液的酒精度和总黄酮的含量为目标,对野葛全粉和野葛淀粉的配伍比例、料液比、糖化时间进行初步优化,并使用响应面中心组合实验确定料液比和糖化酶的用量。结果表明,野葛全粉和野葛淀粉按1∶1配比,料液比为1∶1.587,糖化酶用量为256.2 U/g,糖化时间为0 min时,酒精度为15.2%vol,葛根总黄酮为25.9 mg/mL,在此条件下,酒精度和总黄酮含量均较高。 相似文献
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为研究小米在浓香型白酒酿造中的应用,采用传统浓香型白酒酿造工艺以及气相色谱定量分析,通过比较小米和五粮原料白酒中风味成分和香气活性值(odor activity value,OAV),结合感官品评,研究小米白酒的风格特征。结果表明,在相同工艺条件下,使用小米酿造浓香型白酒,发酵顶温高于五粮原料酿造。小米白酒中风味成分总含量和OAV分别为19527 mg/L和132553,而五粮白酒只有15219 mg/L和108365,小米白酒风味成分更丰富、香气更浓郁。小米白酒主要香气特征成分依次为己酸乙酯、丁酸乙酯和戊酸乙酯,而五粮白酒依次为己酸乙酯、异戊醛和辛酸乙酯,两者存在区别。此外,通过感官品评,发现小米白酒酯香更突出,酒体醇甜、丰满、醇厚感更强,品质优于五粮白酒。该研究为传统浓香型白酒酿酒原料多元化及品质提升,提供了思路和参考。 相似文献
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Network traffic classification, which matches network traffic for a specific class of different granularities, plays a vital role in the domain of network administration and cyber security. With the rapid development of network communication techniques, more and more network applications adopt encryption techniques during communication, which brings significant challenges to traditional network traffic classification methods. On the one hand, traditional methods mainly depend on matching features on the application layer of the ISO/OSI reference model, which leads to the failure of classifying encrypted traffic. On the other hand, machine learning-based methods require human-made features from network traffic data by human experts, which renders it difficult for them to deal with complex network protocols. In this paper, the convolution attention network (CAT) is proposed to overcom those difficulties. As an end-to-end model, CAT takes raw data as input and returns classification results automatically, with engineering by human experts. In CAT, firstly, the importance of different bytes with an attention mechanism of network traffic is achieved. Then, convolution neural network (CNN) is used to learn features automatically and feed the output into a softmax function to get classification results. It enables CAT to learn enough information from network traffic data and ensure the classified accuracy. Extensive experiments on the public encrypted network traffic dataset ISCX2016 demonstrate the effectiveness of the proposed model. 相似文献
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网络入侵检测通过分析流量特征来区分正常和异常的网络行为以实现入侵流量的检测,是网络安全领域的重要研究课题.针对已有入侵检测模型特征提取过程复杂、信息提取不足等问题,提出了一种基于内外卷积网络的入侵检测模型.首先使用一维卷积神经网络提取流量数据的内部特征,然后通过对内部特征计算相似度建模得到无向同质图,此外将流量在外部网络侧的通信行为建模为有向异质图,并对两图使用图卷积网络学习包含网络流量多种交互行为的嵌入向量,最后将学习到的流量嵌入向量输入到分类器中用于最终的分类.实验结果表明,所提模型的检测准确率和误报率均优于对比模型. 相似文献
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