• Journal of Food Hygiene and Safety
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• v.36 no.3
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• pp.220-227
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• 2021

Fluoroimide is a fungicide and is also used as a pesticide for persimmons and potatoes. The established fluoroimide pesticide analysis method takes a long time to perform and uses benzene, a carcinogen. In addition, a lower limit of quantification is required due to enforcement of the Positive List System. Therefore, this study aimed to improve the analysis method for residual fluoroimide to resolve the problems associated with the current method. The analytical method was improved with reference to the increased stability of fluoroimide under acidic conditions. Fluoroimide was extracted under acidic conditions by hydrogen chloride (4 N) and acetic acid. MgSO₄ and NaCl were used with acetonitrile. C₁₈ (octadecylsilane) 500 mg and graphitized carbon black 40 mg were used in the purification process. The experiment was conducted with agricultural products (hulled rice, potato, soybean, mandarin, green pepper), and liquid chromatograph-tandem mass spectrometry was used for the instrumental analysis. Recovery of fluoroimide was 85.7-106.9% with relative standard deviations (RSDs) of less than 15.6%. This study reports an improved method for the analysis of fluoroimide that might contribute to safety by substituting the use of benzene, a harmful solvent. Furthermore, the use of QuEChERS increased the efficiency of the improved method. Finally, this research confirmed the precise limit of quantification and these results could be used to improve the analysis of other residual pesticides in agricultural products.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.