食堂手工食品有害残留物的相关风险评估数据

食堂手工食品有害残留物的相关风险评估数据是一个创新的量化工具，可用于评估自制食物的有害物质残留（如甲醛、吊白块、过氧化氢、亚硝酸盐、日落黄等）情况可能对食品安全造成的风险程度。 1.食堂可将本指数作为食品安全的日常监控工具，识别因自制食物中有害物质含量偏高而可能造成的食品安全风险，强化食物制作过程的质量控制。2.餐饮监管部门可以利用本数据作为监管食堂食品安全的依据之一，可通过指数的变化及时发现食品安全风险较高的食堂，提前进行干预和指导。3.食堂或和监管机构可以将本数据对外披露公开，体现本单位或本地区对食物安全的重视和承诺，有利于增强用餐者的信任。4.保险公司可根据本数据提前识别目标食堂客户的投保风险，从而确定相关保险产品的定价，如食品安全责任险。5.本数据还能为食物有害物质测试仪器厂家对仪器进行功能改进或提升提供依据。1.数据抽取和预处理： （1）数据抽取：在自研的5G智慧食安工业物联网数字化管理平台数据库中抽取相关食堂的自制食物的有害物质残留数据，包括日期、时间、食堂编号、所在地区、食材名称、有害物质类型、有害物质残留超标情况。（2）数据预处理：对抽取的数据进行清洗，去除重复、错误或无关的信息，以便后续的分析和建模。 2.基于采购食材的农残统计数据预测食堂食品安全风险： （1）计算近30日残留测试次数、超标次数和连续超标次数：利用SUM函数对近30日的测试次数进行累加；利用CountIf函数分别对近30日的超标次数和连续超标次数进行累加；（2）计算近30日残留超标率和连续超标次数占比：近30日残留超标率=近30日残留超标次数÷近30日残留测试次数×100%；近30日残留连续超标次数占比=近30日残留连续超标次数÷近30日残留测试次数×100%；（3）建立食堂食品安全风险评估模型：基于自制食物有害物质残留统计的食堂食品安全风险指数=近30日残留超标率×a+近30日残留连续超标次数占比×b；a和b为对应的系数，属于我司商业秘密，故不作详细列举。

The risk assessment data for hazardous residues in canteen handcrafted foods is an innovative quantitative tool for evaluating the risk level posed to food safety by hazardous substance residues (e.g., formaldehyde, rongalite, hydrogen peroxide, nitrite, sunset yellow, etc.) in homemade foods.

1. Canteens can use this index as a daily monitoring tool for food safety, identify potential food safety risks caused by excessive hazardous substance content in homemade foods, and strengthen quality control during food production.
2. Food and beverage regulatory authorities can use this data as one of the bases for supervising canteen food safety, identify canteens with high food safety risks in a timely manner through changes in the index, and carry out early intervention and guidance.
3. Canteens or regulatory agencies can publicly disclose this data to demonstrate their emphasis on and commitment to food safety, which helps enhance diners' trust.
4. Insurance companies can use this data to identify the underwriting risks of target canteen clients in advance, so as to determine the pricing of relevant insurance products, such as food safety liability insurance.
5. This data can also provide a basis for food hazardous substance testing instrument manufacturers to improve or upgrade the functions of their instruments.

1. Data Extraction and Preprocessing:
(1) Data Extraction: Extract hazardous substance residue data of homemade foods from relevant canteens from the database of the self-developed 5G smart food safety industrial IoT digital management platform, including date, time, canteen ID, location, food material name, hazardous substance type, and status of exceeding standard limits for hazardous substance residues.
(2) Data Preprocessing: Clean the extracted data to remove duplicate, erroneous or irrelevant information for subsequent analysis and modeling.

2. Predicting Canteen Food Safety Risks Based on Residue Statistics Data of Purchased Ingredients:
(1) Calculate the number of residue tests, number of exceeding-standard cases and number of consecutive exceeding-standard cases in the past 30 days: Use the SUM function to accumulate the number of tests in the past 30 days; use the COUNTIF function to accumulate the number of exceeding-standard cases and the number of consecutive exceeding-standard cases in the past 30 days respectively.
(2) Calculate the residue exceeding-standard rate and the proportion of consecutive exceeding-standard cases in the past 30 days:
Residue exceeding-standard rate in the past 30 days = (Number of exceeding-standard cases in the past 30 days ÷ Number of residue tests in the past 30 days) × 100%;
Proportion of consecutive exceeding-standard cases in the past 30 days = (Number of consecutive exceeding-standard cases in the past 30 days ÷ Number of residue tests in the past 30 days) × 100%.
(3) Establish a canteen food safety risk assessment model: The canteen food safety risk index based on hazardous substance residue statistics of homemade foods = (Residue exceeding-standard rate in the past 30 days × a) + (Proportion of consecutive exceeding-standard cases in the past 30 days × b); Coefficients a and b are trade secrets of our company and thus not listed in detail.