• The Korean Journal of Food And Nutrition
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• v.28 no.2
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• pp.202-218
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• 2015

Many countries have introduced new imported food safety measures, following the accident at Fukushima Daiichi Nuclear Power Station. This study was conducted to evaluate the measures contents and effects on food trades values. Eight percent of members were notified the introduced measures to the World Trade Organization. The measures' contents were banning imports, enhancing inspection and adding certification requirement. The covered regions were some prefectures, entire Japan or all affected countries. European Union introduced a measure that subjecting foods originating from 12 prefectures to import at designated ports with required certification. The measures were amended 8 times until March 2014 to apply listed foods from 15 prefectures. The trade value of fishery products and miscellaneous foods were affected. Australia introduced a measure that required additional inspection of dairy, fishery and plants products from 13 prefectures with subsequent amendments. The trade value had no effect in tested foods. Chinese Taipei introduced a temporary import ban for all foods from 6 prefectures. Trade values for fruits were affected. The United States issued an import alert for detention without examination for listed prefectures and goods without introducing new measures. Although no specific products were affected, trade values for all foods were affected.

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.