指纹图谱技术在生鲜乳掺假检测中的研究进展

生鲜乳是乳制品行业发展的主要原料,是决定乳制品质量的关键因素。然而近年来国内外在乳制品方面的食品安全事件频发,不法分子通过在生鲜乳中掺入虚假物质以获取经济利益的行为已经成为严重的安全问题,对人们健康以及整个乳制品行业造成不良影响。指纹图谱技术是对通过一定的分析工具产生的图像进行判别的一种检测技术,可以对生鲜乳的掺假进行更灵敏、准确和快速的检测。本文通过对生鲜乳的安全现状进行剖析,总结了电泳法、光谱法、色谱法和电子感官技术法4种指纹图谱技术在牛乳掺假检测中的应用,比较了4种技术的优点和局限性,并对未来的研究方向进行了展望,为提高生鲜乳的品质与安全以及保证消费者健康提供理论依据与参考。     

Headspace fingerprinting approach to identify the major pathway influencing volatile patterns of vinasse-cured duck processed by high pressure,as well as its impact on physicochemical and sensory attributes

As a decisive attribute, flavour could be influenced by HP treatments through multiple physical and chemical pathways within the high pressure (HP)-assisted meat curing process. This investigation aimed to identify the major pathway influencing volatile flavour patterns of two representative vinasse-cured duck (VCD) products with HP treatments (150–300 MPa/15 min), including wet and dry types, by employing headspace fingerprinting as an untargeted approach. Results suggested that HP treatments greatly lowered moisture contents and increased Warner-Bratzler shear force and thiobarbituric acid reactive substances of the cured samples. According to multivariate models, the volatile flavour patterns of the HP-processed VCD could be clearly separated from the unprocessed samples, but the VCD pressurised at different intensities represented similar volatile fingerprinting, which was validated by e-nose analysis. The discriminant analysis (OPLS-DA) model outlined vinasse-derived ethanol, acetic acid, 3-methyl-1-butanol, 2-methyl-1-butanol, phenethyl alcohol and 2-methyl-3-octanone as the major discriminant aromas across the unpressurised and pressurised samples.     

Graph-based video fingerprinting using double optimal projection

A double optimal projection method that involves projections for intra-cluster and inter-cluster dimensionality reduction are proposed for video fingerprinting. The video is initially set as a graph with frames as its vertices in a high-dimensional space. A similarity measure that can compute the weights of the edges is then proposed. Subsequently, the video frames are partitioned into different clusters based on the graph model. Double optimal projection is used to explore the optimal mapping points in a low-dimensional space to reduce the video dimensions. The statistics and geometrical fingerprints are generated to determine whether a query video is copied from one of the videos in the database. During matching, the video can be roughly matched by utilizing the statistics fingerprint. Further matching is thereafter performed in the corresponding group using geometrical fingerprints. Experimental results show the good performance of the proposed video fingerprinting method in robustness and discrimination.     

网络匿名扫描系统设计及优化

针对网络扫描工具在进行扫描时面临的溯源问题，提出了一种匿名网络扫描系统。首先将匿名系统与网络扫描工具结合以实现匿名扫描；然后在现有匿名系统的基础上实现了该系统的本地私有化；接着通过流量分析发现，Nmap的多进程扫描因为代理链的原因会变成单进程扫描而导致其扫描扫描性能较低；最后提出了一种基于多Namp进程并发的性能优化方案，将总体扫描任务分割为多个扫描任务，并分配给多个单独的Nmap进程并行运行。实验结果表明，该性能优化方案的扫描时延接近正常扫描情况下的时延，达到了提高匿名扫描系统性能的目的。因此，该优化后的网络匿名扫描系统在阻碍溯源的同时提升了扫描效率。     

Molecular subtypes of Campylobacter spp., Salmonella enterica, and Escherichia coli O157:H7 isolated from faecal and surface water samples in the Oldman River watershed, Alberta, Canada

Campylobacter spp., Salmonella enterica, and Escherichia coli O157:H7 isolated from 898 faecal, 43 sewage, and 342 surface water samples from the Oldman River were characterized using bacterial subtyping methods in order to investigate potential sources of contamination of the watershed. Among these pathogens, Campylobacter spp. were the most frequently isolated from faecal, sewage, and surface water samples (266/895, 11/43, and 91/342, respectively), followed by Salmonella (67/898, 8/43, and 29/342, respectively), and E. coli O157:H7 (16/898, 2/43, and 8/342, respectively). Salmonella Rubislaw was the most common serovar isolated from water. This serovar was also isolated from two wild bird species. Most other serovars isolated from water were either not isolated from animals or were isolated from multiple species. E. coli O157:H7 was predominantly isolated from cattle. The most common phage-types of this pathogen from cattle were also the most common among water isolates, and there were exact pulsed field gel electrophoresis and comparative genomic fingerprint matches between cattle, sewage, and water isolates. Campylobacters were commonly isolated from surface waters and faeces from most animal species. Restriction fragment length polymorphism of the Campylobacter flaA gene identified several location and host species-specific (cattle, goose, pig) fingerprints. Molecular subtyping of these bacterial pathogens shows considerable promise as a tool for determining the sources of faecal pollution of water.     

Comparison of the efficacy of an existing versus a locally developed metabolic fingerprint database to identify non-point sources of faecal contamination in a coastal lake

A comparison of the efficacy of an existing large metabolic fingerprint database of enterococci and Escherichia coli with a locally developed database was undertaken to identify the sources of faecal contamination in a coastal lake, in southeast Qld., Australia. The local database comprised of 776 enterococci and 780 E. coli isolates from six host groups. In all, 189 enterococci and 245 E. coli biochemical phenotypes (BPTs) were found, of which 118 and 137 BPTs were unique (UQ) to host groups. The existing database comprised of 295 enterococci UQ-BPTs and 273 E. coli UQ-BPTs from 10 host groups. The representativeness and the stability of the existing database were assessed by comparing with isolates that were external to the database. In all, 197 enterococci BPTs and 179 E. coli BPTs were found in water samples. The existing database was able to identify 62.4% of enterococci BPTs and 64.8% of E. coli BPTs as human and animal sources. The results indicated that a representative database developed from a catchment can be used to predict the sources of faecal contamination in another catchment with similar landuse features within the same geographical area. However, the representativeness and the stability of the database should be evaluated prior to its application in such investigation.     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Energy-efficient WiFi scanning for localization

WiFi radio signals are commonly used for the localization of mobile devices. However, frequent scanning of WiFi reduces user experiences because it consumes significant energy on mobile devices. In this paper, we propose an energy-efficient WiFi scan system for localization. The proposed system preserves the quality of service of each application and minimizes the energy consumption of WiFi scanning. The main idea is that the system adaptively controls the dwell time of beacon-listening in given locations. We predict the number of scanned access points (APs) according to locations and optimize the dwell time of beacon-listening to obtain the minimum number of scanned APs. The evaluation shows that the proposed system saves the energy consumption of WiFi scans by 33.6% and 45.7%, according to the number of scanned APs, while not decreasing the accuracy of localization in indoor and outdoor scenarios.     

基于EIGamal加密算法的匿名叛逆者追踪方案

基于离散对数的EIGamal加密算法体制，提出一种匿名的叛逆者追踪方案。在此方案中，广播信息通过对称加密算法加密后广播给用户，而加密使用的会话密钥是在注册用户通过匿名身份验证后，通过数据提供商发送的数据头传送给用户。由性能分析可知，该方案不仅具有用户的匿名安全性、可撤销性与可恢复性的特点，还能够抵抗线性组合攻击。