• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.248-256
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• 2021

A total of 120 samples corresponding to 12 categories of dried processed fishery products distributed in Gyeonggi-do were examined for radioactivity contamination (¹³¹I, ¹³⁴Cs, ¹³⁷Cs) and heavy metals (lead, cadmium, arsenic, and mercury). One natural radioactive material, ⁴⁰K, was detected in all products, while the artificial radioactive materials ¹³¹I, ¹³⁴Cs and ¹³⁷Cs were not detected at above MDA (minimum detectable activity) values. The detection ranges of heavy metals converted by biological basis were found as follows: Pb (N.D.-0.332 mg/kg), Cd (N.D.-2.941 mg/kg), As (0.371-15.007 mg/kg), Hg (0.0005-0.0621 mg/kg). Heavy metals were detected within standard levels when there was an acceptable standard, but the arsenic content was high in most products, although none of the products had a permitted level of arsenic. In the case of dried processed fishery products, there are products that are consumed by restoring moisture to its original state, but there are also many products that are consumed directly in the dry state, so it will be necessary to set permitted levels for heavy metals considering this situation in the future. In addition, Japan has decided to release contaminated water from the Fukushima nuclear power plant into the ocean, so there is high public concern about radioactivity contamination of food, including fishery products. Therefore, continuous monitoring of various food items will be necessary to ease consumers' anxiety.

• Journal of Food Hygiene and Safety
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• v.38 no.2
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• pp.46-54
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• 2023

In this study, we examined the residual amounts of formaldehyde in hygiene products to determine the safety of these products in Gyeonggi-do. Formaldehyde is among the harmful substances that may remain within certain hygiene products. On the basis of an analysis of formaldehyde in a total of 222 items (6 disposable paper straws, 9 disposable paper napkins, 21 toilet papers, 13 disposable dishcloths, 16 disposable paper towels, 32 wet wipes for food service restaurants, 25 disposable cotton swabs, and 100 disposable diapers), we detected traces in three wet wipes for food service restaurants (1.87 to 4.45 mg/kg), which is approximately 9% to 22% of the standard level (20 mg/kg). We established that all the hygiene products assessed in the study met the individual standards for formaldehyde, thereby confirming that safe products are being distributed. In the standards and specifications for hygiene products, the formaldehyde test method is regulated for application with respect to three categories based on the type of product. The samples used in this study were of types for which method 1 or method 2 is applied, and the limits of detection, limits of quantification, linearity, and recovery rates were reviewed to verify the validity of each test method. When method 2 was applied, we experienced interference when performing analysis at a wavelength of 412 nm, which was associated with the influence of impurities in some samples of disposable cotton swabs and disposable diapers. Consequently, in these cases, the results were compared after analysis using method 1. By comparing the results obtained using method 2 with those obtained using method 1, the latter of which were unaffected by the interference of impurities, we were able to detect formaldehyde at low concentrations. These findings accordingly highlight the necessity to standardize the formaldehyde test method for future analyses.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.