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Food Safety Risk Identification Based on Big Data
Food Partner Network News The risk factors in food mainly include chemical factors such as pesticides, veterinary drugs, and pollutants, biological factors such as microorganisms, biotoxins, parasites, physical factors, and other factors. The use of big data technology for statistical analysis of past food safety data is one of the effective ways for food companies to identify food safety risks. At present, Food Partner Network has established a big data cloud platform for food safety information services. This paper mainly introduces the basic principles and methods of food safety risk identification based on big data.
1. Food safety big data content for risk identification
1.1 Food Standards Regulations and Regulatory Developments
According to China's food safety law, the revision of food standards and regulations must be based on risk assessment as a scientific basis. The revision of food standards and regulations, and the changes in each edition of food standards and regulations, all reflect the adjustment of China's food safety situation. Therefore, the food standards, regulations, and regulatory dynamics are the priorities for enterprises to carry out food safety risk identification. For example, GB 2762-2017 removes the limitation of rare earth elements in tea, indicating that after scientific evaluation no longer needs to control rare earth elements in tea, then rare earth will no longer be the main risk point in tea.
1.2 Food Safety Incidents
Due to the development of the media and the Internet, especially the rise of mobile Internet and self-media in recent years, food safety incidents will spread quickly and widely in public opinion and have a huge impact on the food industry. For example, the 2017 plastic seaweed rumors brought about the laver industry. Huge losses, as in previous reports of apple medicine bags, caused heavy losses to Yantai farmers. The accumulation of information on the Internet also facilitates the identification of food safety risks. The use of public opinion monitoring and analysis tools to trace back the major public opinions and hot public opinions of the food industry over the past few years, and analyzes its major trends, popularity, etc. will help find media and consumers' attention. Food safety risks to achieve risk identification. For example, using the food monitoring network's self-developed public opinion monitoring and gathering tool to search for food safety reports on rice, wheat, corn, soybeans, and other foods in recent years, according to mold and insects, genetically modified, illegally added, excessive additives, excessive pollutants, etc. Risk statistics for data statistics can identify the food categories most likely to cause food safety risks, as well as the corresponding risk factors, area, and time.
1.3 Food Safety Inspection and Early Warning Notification
Since its establishment, the General Food and Drug Administration has regulated the supervision and inspection of foods, and the General Administration of Pharmacy and Food and Drug Administration at all levels have regularly and transparently issued food safety sampling information. According to statistics from the food partner network random sampling inquiry analysis system, since 2015, China has issued more than one million food sampling information, and statistical analysis of the unqualified data in these sampling results can identify major unqualified causes in different types of food products. , Mainly susceptible to unqualified areas. For example, in China, more than 12,000 batches of infant formula foods were sampled in 2015. The main unqualified factor was the unqualified selenium content in the quality index. This reminds infant formula enterprises that selenium content should be controlled as a key point of control in production and monitoring should be strengthened to avoid such substandard occurrences.
1.4 Food Judgment Information
The ten-fold compensation clause of the Food Safety Law gave birth to the active anti-counterfeiters. The Supreme People's Court established the China Judicial Document Network and collected and sorted out the judgment documents of the people's courts at various levels, including the professional anti-counterfeiting cases related to food safety. It is helpful for food companies to identify the risk of professional anti-counterfeiting by statistical analysis of the categories involved, the reasons for prosecution, and whether they have won ten times compensation. For example, in recent years most of the complaints concerning claims of certain characteristic ingredients such as olive blend oil but not identified specific content have been compensated, and relevant food companies are reminded to pay attention to the compliance of food labeling and prevent such risks.
1.5 Food Administrative Punishment Information
Food administrative punishment information published by food and drug supervision at all levels is often easily overlooked by other companies. At present, there are many places where the publicized punishment information will explain in detail the cause of the penalty and the legal basis. The analysis and analysis of this information will also help the company identify certain risks.
2. Steps to identify food safety risks using big data
2.1 clearly identify the product object to be analyzed
The first is to identify the object to be analyzed, whether it is a product or a specific product. It is to identify all the risks of the product, or to evaluate the level of a specific risk at a specific time or at a specific location, only to clarify the analysis needed. The target can collect targeted data in order to accurately identify risks.
2.2 Gather the required basic data
Collect information related to specific time and location of all standards and regulations in specific areas, including food safety incidents, past food safety cases, food sampling and early warning notifications, food law cases, and administrative punishment information. You can use specialized search engines or third-party data. Collect service agencies.
2.3 Dismantling data for structured finishing
Disassemble the collected data to form structured data to facilitate statistics. For example, for a specific food, it is possible to count the major indicator requirements for standard revisions in recent years, the amount of reported data on food safety news related to various risk factors, and the number of unqualified reasons for various unqualified reasons, etc., to form a structured quantifiable data. In addition, different data can be weighted according to the different characteristics of different products, so as to establish a risk model more objectively.
2.4 Comprehensive Data Analysis and Risk Identification
Based on the data structure, comprehensive analysis of data from various sources is conducted to identify the main risks in food. Provides the basis for follow-up risk prevention and control.
Based on the above data and steps, we can initially identify possible risks in food and provide theoretical basis for the formulation of food regulatory control measures and plans. At the same time, the Food Partner Network, based on years of data accumulation, can provide you with customized risk identification report preparation services.