Research progress on microbial contamination and its prevention and control measures in ready-made beverages

Freshly squeezed fruit and vegetable juice， freshly ground coffee， new tea and other freshly made beverages are very popular among consumers. However， due to the loose quality control of raw materials and non-standard processing operation， the risk of microbial contamination exists in the freshly made beverages. This paper summarizes the status of microbiological pollution in the present system beverage and the current situation of industry supervision， analyzes the causes of microbiological pollution in the process of the present system beverage， finds out the key control points and proposes the intervention measures， in order to promote the healthy development of the present system beverage industry

Sparse Representations for Hyperspectral Data Classification

We investigate the use of sparse principal components for representing hyperspectral imagery when performing feature selection. For conventional multispectral data with low dimensionality, dimension reduction can be achieved by using traditional feature selection techniques for producing a subset of features that provide the highest class separability, or by feature extraction techniques via linear transformation. When dealing with hyperspectral data, feature selection is a time consuming task, often requiring exhaustive search of all the feature subset combinations. Instead, feature extraction technique such as PCA is commonly used. Unfortunately, PCA usually involves non-zero linear combinations or \u27loadings\u27 of all of the data. Sparse principal components are the sets of sparse vectors spanning a low-dimensional space that explain most of the variance present in the data. Our experiments show that sparse principal components having low-dimensionality still characterize the variance in the data. Sparse data representations are generally desirable for hyperspectral images because sparse representations help in human understanding and in classification

Subdividing Hexagon-Clustered Wireless Sensor Networks for Power-Efficiency

Hexagons are an ideal shape for clustering sensor networks, because clustered areas can be seamlessly divided by the hexagons. In addition, hexagons are the largest regular polygon (in terms of the number of sides) that has this property. In this paper, we propose a novel scheme for subdividing a hexagonal cluster where sensors are densely populated and distributed uniformly throughout the cluster. We provide an analytical estimate of power saving resulting from the subdivision, and show that our proposal gives rise to a substantial reduction in power consumption. We also perform subdivisions at various scales, and analyze the corresponding power saving patterns. Our results show that the proposed scheme will yield significant saving in overall power consumption of a cluster, and the deeper the subdivision, the less the power consumption

Heuristic Fault-Tolerant Routing in Mesh using Minimal-Connected-Component Fault Blocks

Rectangular fault block model is designated to solve the problem of fault-tolerant route in mesh and was improved as Minimal-Connected-Component (MCC) model. Based on MCC, we construct an overlapping graph and give a set of algorithm according to the graph to work out the route as short as possible to avoid the appearance of fault block when Manhattan route does not exist. The simulated test shows that the route found by the algorithm mentioned above is nearly the shortest one. Hence compared to other methods costing much more time, this new heuristic fault-tolerant algorithm is of no doubt a better method in finding the shortest route

Correlation Analysis of Microbial Contamination and Alkaline Phosphatase Activity in Raw Milk and Dairy Products

Microbial contamination in raw milk and dairy products can detrimentally affect product quality and human health. In this study, the aerobic plate count, aerobic Bacillus abundance, thermophilic aerobic Bacillus abundance, and alkaline phosphatase activity were determined in 435 raw milk, 451 pasteurized milk, and 617 sterilized milk samples collected from 13 Chinese provinces (or municipalities). Approximately 9.89% and 2.22% of raw milk and pasteurized milk samples exceeded the threshold values for the aerobic plate count, respectively. The proportions of aerobic Bacillus in raw milk, pasteurized milk, and sterilized milk were 54.02%, 14.41%, and 1.30%, respectively. The proportions of thermophilic aerobic Bacillus species were 7.36% in raw milk and 4.88% in pasteurized milk samples, and no bacteria were counted in sterilized milk. Approximately 36.18% of raw milk samples contained >500,000 mU/L of alkaline phosphatase activity, while 9.71% of pasteurized milk samples contained >350 mU/L. For raw milk, there was a positive correlation between the aerobic plate count, the aerobic Bacillus abundance, and the alkaline phosphatase activity, and there was a positive correlation between the aerobic Bacillus abundance, the thermophilic aerobic Bacillus count, and the alkaline phosphatase activity. For pasteurized milk, there was a positive correlation between the aerobic plate count, the aerobic Bacillus abundance, and the thermophilic aerobic Bacillus count; however, the alkaline phosphatase activity had a negative correlation with the aerobic plate count, the aerobic Bacillus abundance, and the thermophilic aerobic Bacillus abundance. These results facilitate the awareness of public health safety issues and the involvement of dairy product regulatory agencies in China

Contamination Status and Risk Assessment of Paralytic Shellfish Toxins in Shellfish along the Coastal Areas of China

Paralytic shellfish toxins (PSTs) are widely distributed in shellfish along the coast of China, causing a serious threat to consumer health; however, there is still a lack of large-scale systematic investigations and risk assessments. Herein, 641 shellfish samples were collected from March to November 2020, and the PSTs’ toxicity was detected via liquid chromatography–tandem mass spectrometry. Furthermore, the contamination status and potential dietary risks of PSTs were discussed. PSTs were detected in 241 shellfish samples with a detection rate of 37.60%. The average PST toxicities in mussels and ark shells were considerably higher than those in other shellfish. The PSTs mainly included N-sulfonylcarbamoyl toxins (class C) and carbamoyl toxins (class GTX), and the highest PST toxicity was 546.09 μg STX eq. kg−1. The PST toxicity in spring was significantly higher than those in summer and autumn (p < 0.05). Hebei Province had the highest average PST toxicity in spring. An acute exposure assessment showed that consumers in Hebei Province had a higher dietary risk, with mussels posing a significantly higher dietary risk to consumers. This research provides reference for the green and sustainable development of the shellfish industry and the establishment of a shellfish toxin prevention and control system

ANCR—An Adaptive Network Coding Routing Scheme for WSNs with Different-Success-Rate Links †

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs—the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes’ coding opportunities based on the links’ transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR

ANCR-an Adaptive Network Coding Routing Scheme For WSNs with Different-Success-Rate Links

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node\u27s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs-the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes\u27 coding opportunities based on the links\u27 transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR

ANCR—An Adaptive Network Coding Routing Scheme for WSNs with Different-Success-Rate Links

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs—the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes’ coding opportunities based on the links’ transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR