荧光法测定香烟烟气中总的多环芳烃含量

有机溶剂萃取不同香烟烟气焦油中有机物 ,用荧光法分析了烟草的总体特征光谱 ,同时以多环芳烃菲作参比 ,定量分析了各种品牌香烟在去掉过滤嘴和保留过滤嘴条件下 ,烟气在溶剂中的多环芳烃含量 ,为卷烟品质、烟气中有害物质的评价提供了一种实用的测定方法     

磺硝酚偶氮若丹宁固相萃取光度法测定生物样品中的汞

在 p H2 .0的氯乙酸 -氢氧化钠缓冲介质与吐温 - 80存在下 ,磺硝酚偶氮若丹宁 (NSPAR)与汞反应生成 2∶ 1稳定络合物 ,可被 Waters Sep- Pak C18小柱固相萃取 ,氮 -氮二甲基甲酰氨 (DMF)洗脱后光度法测定 ,λmax=5 5 0 nm,体系摩尔吸光系数ε=8.12× 10 4L· mol-1· cm-1。汞含量在 0 .0 5— 4 .0μg/m L内符合比耳定律 ,用于生物样品中汞含量的测定 ,结果令人满意     

荞麦花多糖的提取及含量测定

采用热水提取、乙醇沉淀的方法从荞麦花中提取出了水溶性多糖,采用硫酸-苯酚法在波长为497nm处进行了糖含量的测定,求得其校准曲线的回归方程为y=0.00508x 0.0294,相关系数为r=0.9978.平均回收率为99%-103%,RSD为0.74%.结果显示:荞麦花多糖的产率为2.5%.糖含量为41.63%.     

萃取—火焰原子吸收法测定骨骼中的微量元素Cu,Zn,Cd和Fe

本文研究了以邻菲罗啉为金属螯合剂，高氯酸钠为配体，在一定酸度下用１，２－二氯乙烷萃取－火焰原子吸收法测定动物骨骼中微量Ｃｕ，Ｚｎ，Ｃｄ和Ｆｅ的条件，并确定了测定方法，方法简单，准确，可靠，元素的检测限分别为：Ｃｕ：０．０３８μｇ／ｍｌ，Ｚｎ：０．００４２μｇ／ｍｌ，Ｃｄ：０．００１９μｇ／ｍｌ，Ｆｅ：０．０２μｇ／ｍｌ，回收率在９２．８％～１０５％之间。     

蒙成药如达多糖的研究

从蒙成药如达中提取水溶性多糖,硫酸-苯酚法测定其含量。平均回收率为99.8%,RSD为0.94%。GC测定了水溶性多糖的组成为葡萄糖、半乳糖和甘露糖。其摩尔比为:6.33:3.25:1.59。     

超临界CO_2萃取板蓝根靛玉红的工艺优化研究

采用正交试验法,对板蓝根靛玉红超临界CO2萃取工艺进行优化。选用萃取温度、萃取压力、萃取时间作为正交试验的3个因素,每个因素选3个水平,以提取物中靛玉红的含量来确定最佳提取条件,并且在最佳条件下确定所加夹带剂的最佳剂量。结果表明最佳工艺为:萃取温度45℃、萃取压力35MPa、萃取时间3h,夹带剂为75%的乙醇200mL。最佳工艺条件下提取靛玉红的得率为4.95μg/g,为传统提取方法的1.6倍。     

紫草的提取工艺

对紫草提取工艺中各参数进行了优化,为紫草工业化提取提供一定的参考;以左旋紫草素含量的测定为工艺指标,通过正交设计试验,对乙醇浓度,乙醇体积,浸泡时间,及渗漉时间各因素进行了考察;紫草最佳提取工艺为：5倍量80%乙醇浸泡16h,再加10倍量80%的乙醇渗漉4h;适宜工艺为：5倍量80%乙醇浸泡12h,再加10倍量80%的乙醇渗漉4h;本工艺设计合理,工艺稳定,左旋紫草素提取率高,质量可控,适宜于工业化生产。     

大蓟多糖提取分离及含量测定

用水提-醇沉法从大蓟中提取多糖,脱脂、去蛋白,经冷冻干燥后得到粗多糖。通过苯酚-硫酸法测定糖的含量,粗多糖得率为6.5%。再采用DEAE-纤维素离子交换柱层析进一步分离纯化,得到5个次级组分DJ1,DJ2,DJ3,DJ4和DJ5。其中得率最高的是DJ2组分,为8.06‰。     

基于轮廓波的纹理图像特征提取方法

文利用轮廓波分解的方向性和能量变化特性,提出了一种适用于纹理图像的特征提取方法。该方法首先通过方向可控滤波器（Steerable Filter）获得纹理图像的角度信息,然后进行轮廓波分解提取纹理在不同分解方向上的特征向量,最后将纹理的角度和方向分解的能量信息一起作为纹理的特征。由于轮廓波的方向和分解层次数目可以灵活地调节,因此特征值数量也是调节的,以达到最好的识别分类效果。本文利用简单的距离分类器对所提出的算法进行了验证,以Brodatz纹理库中的旋转纹理图像为标准,纹理分类的结果达到了满意的效果。      

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.