超高效液相色谱-串联质谱法测定动物源性食品中硝基呋喃代谢物

目的建立动物源性食品中四种硝基呋喃代谢物的超高效液相色谱串联质谱(ultra pressure liquid chromatography-tandem mass spectrometry,UPLC-MS/MS)的快速检测方法。方法样品经盐酸溶液水解,邻硝基苯甲醛(2-NBA)37℃衍生16 h后,调节衍生液的pH=7.6,用乙酸乙酯提取。分析物采用电喷雾电离,正离子扫描,多反应检测模式(MRM),基质匹配内标标准曲线法定量。结果四种代谢物的工作曲线在0.1～10.0μg/L浓度范围内线性良好,相关系数R2均达到了0.995以上,氨基脲(SEM或SCA)与1-氨基-乙内酰脲(AHD)最低检出限均为0.1μg/kg,5-吗啉甲基-3-氨基-2-恶唑烷基酮(AMOZ)与3-氨基-2-恶唑烷基酮(AOZ)最低检出限均为0.05μg/kg。平均回收率在91.8%～107.0%之间,相对标准偏差均小于10%。结论样品前处理简单、测定时间短、灵敏度高,适用于猪肉、牛肉、鸡肉、猪肝和水产品(鱼类及虾类)中硝基呋喃类代谢物残留的快速测定。     

基于M-ACO算法的空气污染源反演

为了解决空气污染源反演的盲目性和低效率问题,本文提出了一种基于改进型蚁群算法(modified-ant colony optimization, M-ACO)的空气污染源反演方法.利用点源高斯扩散模型建立污染源反演模型,采取蚁群算法(ant colony optimization, ACO)来求解.针对蚁群算法中存在的缺点,引入遗传算法的选择交叉思想,从而丰富种群的多样性来避免陷入局部极值;同时设计奖惩因子机制,对信息素更新规则进行改进来使算法更快地收敛,进而归纳为M-ACO算法.通过对比实验,证明了M-ACO算法相比于传统ACO算法来说,能够使得污染源的反演结果更准确和高效,为空气污染源反演的实际应用提供了有效的理论支撑.     

氯碱生产中循环水泵叶轮节能改造

介绍了氯碱装置长期低负荷运行时,实际所需循环水流量低于水泵设计流量的现状,提出了此现状下重新设计并更换循环水泵叶轮的节能改造措施,达到降低循环水泵的设计流量和运行电流的节能目的。     

基于小波变换的苹果汁多光程近红外光谱信息提取研究

利用浸入式光纤采集鲜榨苹果汁分别在5mm、10mm、15mm和20mm光程下的透/反射近红外光谱,实现对苹果汁中糖度(可溶性固形物,SSC)和酸度(pH值)的定量预测.结果表明,SSC和pH具有不同的最佳光程长,分别为5mm和20mm.为了兼顾各待测量对象的浓度范围和各组分的最佳光程长,从而提高模型的性能,采用多光程光谱混合建模,研究了多光程光谱信息的提取方法.采用原始光谱直接展开所建的模型虽然能有效利用多光程光谱的信息,但增加了模型复杂度,致使建模时间增长.因此,提出了两种基于小波变换的信息提取方法,它们在高效提取多光程信息的同时,能显著缩短建模时间并简化模型.其中基于展开光谱的小波近似系数建立的模型性能最优,SSC和pH值模型的SECV值分别达到0.4761oBrix和0.0779.     

工艺条件对高性能石英砂烧结砖的显微结构影响研究

本文研究了利用低品位石英砂在不同工艺条件下制备的高性能石英砂烧结砖的显微结构,并通过显微结构与成型方法、成型压力、烧结温度及保温时间的关系分析,确立该烧结砖得到最高性价比时的最佳工艺条件.结果表明:该烧结砖采用100MPa冷等静压成型,在1150℃烧结2h的制备工艺所得的砖的显微结构最佳.     

Manufacture and antibacterial characteristics of Eucommia ulmoides leaves vinegar

Food Science and Biotechnology - In this work, the fermentation conditions and the antibacterial characteristics of Eucommia ulmoides leaves vinegar (EV) were studied. By single factor orthogonal...     

β-胡萝卜素的生物活性研究进展

β-胡萝卜素是一种广泛存在于自然界的类胡萝卜素,是机体维生素A的重要来源。β-胡萝卜素拥有共轭多烯不饱和双键结构,具有多种生物活性,包括清除自由基、猝灭单线态氧、抑制炎症信号传导,其应用从最初的食品着色,逐渐转向医药、化妆品、保健品和动物饲料等多个领域。本文对β-胡萝卜素的来源、结构、毒理学研究以及生物学功能等方面进行概述,为β-胡萝卜素的开发和研究提供参考依据。     

Determination of Recognition Threshold and Just Noticeable Difference in the Sensory Perception of Pungency of Zanthoxylum bangeanum

Zanthoxylum bungeanum, also called the Szechuan pepper, is a pungent-flavored spice. Together with the chili pepper, it constitutes the typical flavor of famous Chinese Szechuan cuisine. With the worldwide popularity of Sichuan cuisine and pungent food, the research on sensory perception of pungency of Z. bungeanum becomes more important and necessary. This article first determined the sensory recognition threshold and just noticeable difference of pungency of Z. bungeanum. The pungent extract, containing hydroxyl α-sanshool (63.6%), hydroxyl β-sanshool (22.8%), and hydroxyl γ-sanshool (7.4%) from Z. bungeanum was used as the tested sample prepared in ethanol–water solution. Ten selected assessors participated in three sessions of sensory tests in standard sensory evaluation booths established according to ISO 8589 by following two-alternative forced choice procedure. The data showed that the recognition threshold was 0.036 mg pungent extract per 1 mL ethanol–water solution and just noticeable difference exponentially increased with the increase of the concentration of pungent extract solution. The response of pungency among assessors followed Weber’s Law with a stable Weber fraction (0.118) from 0.0360 to 0.500 mg pungent extract per 1 mL. Nevertheless, the fraction changed when the concentration of pungent extract solution was above 0.500 mg per 1 mL.     

Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance Pooling

Recently, steganalytic methods based on deep learning have achieved much better performance than traditional methods based on handcrafted features. However, most existing methods based on deep learning are specially designed for one image domain (i.e., spatial or JPEG), and they often take long time to train. To make a balance between the detection performance and the training time, in this paper, we propose an effective and relatively fast steganalytic network called US-CovNet (Universal Steganalytic Covariance Network) for both {the} spatial and JPEG domains. To this end, we carefully design several important components of {US-CovNet} that will significantly affect the detection performance, including the high-pass filter set, the shortcut connection and the pooling {layer}. Extensive experimental results show that compared with the current best steganalytic networks (i.e., SRNet and J-YeNet), {US-CovNet} can achieve the state-of-the-art results for detecting spatial steganography and have competitive performance for detecting JPEG steganography. For example, the detection accuracy of US-CovNet is at least 0.56% higher than that of SRNet in the spatial domain. In the JPEG domain, US-CovNet performs slightly worse than J-YeNet in some cases with the degradation of less than 0.78%. However, the training time of US-CovNet is significantly reduced, which is less than 1/4 and 1/2 of SRNet and J-YeNet respectively.     

基于GCN-LSTM的空气质量预测

随着我国环境监测技术的不断发展,环境空气质量的网格化监测体系越来越受到相关工作人员的青睐,为应对空气污染的网格化监测体系中的小型、微型监测站的空气质量预测问题,本文提出了一种基于GCN和LSTM的空气质量预测模型.首先利用GCN网络提取网格化监测体系中的小微型监测站之间的空间特征,然后再使用LSTM提取时间特征,最后使用线性回归层来综合时空特征并产生空气质量的预测结果.为了验证本文提出的预测模型的性能,我们使用了沈阳市浑南区的14个小微型监测站的空气质量监测数据进行实验.实验结果显示,基于GCN-LSTM的空气质量预测模型在空间关联较强的网格化监测中的小微型监测站上的预测结果的精度要优于单一的LSTM预测模型.