• Title/Summary/Keyword: 수입식품

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A Method of Machine Learning-based Defective Health Functional Food Detection System for Efficient Inspection of Imported Food (효율적 수입식품 검사를 위한 머신러닝 기반 부적합 건강기능식품 탐지 방법)

  • Lee, Kyoungsu;Bak, Yerin;Shin, Yoonjong;Sohn, Kwonsang;Kwon, Ohbyung
    • Journal of Intelligence and Information Systems
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    • v.28 no.3
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    • pp.139-159
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    • 2022

  • As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

  • https://doi.org/10.13088/jiis.2022.28.3.139 인용 PDF KSCI

Tenderness Comparision of Korean and Imported Beef Using Time - Intensity Metho dology (시간-강도 분석에 따른 한우육과 수입우육의 연한정도 비교)

  • 차경희
    • Korean journal of food and cookery science
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    • v.12 no.2
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    • pp.123-128
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    • 1996

  • Tenderness of loin and brisket muscles of Korean and imported beef was measured using the Time-lntensity (Tl) techniques. From the Tl curve, the Rx, Imax, Dur and AUC parameters were determined. For the loin muscle, Korean beef showed significantly (p < 0.05) larger Rx, Imax, and AUC. This result represents Korean beef loin has higher tenderness than that of imported one. For the brisket muscle, imported beef show.

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Preliminary Inspection Prediction Model to select the on-Site Inspected Foreign Food Facility using Multiple Correspondence Analysis (차원축소를 활용한 해외제조업체 대상 사전점검 예측 모형에 관한 연구)

  • Hae Jin Park;Jae Suk Choi;Sang Goo Cho
    • Journal of Intelligence and Information Systems
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    • v.29 no.1
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    • pp.121-142
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    • 2023

  • As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

  • https://doi.org/10.13088/jiis.2023.29.1.121 인용 PDF