电商平台宠物猫粮基础营养成分分析数据

随着宠物市场规模不断扩大，宠物食品消费逐渐成为消费市场大头。对各种各样网售猫粮，养宠消费者在购买猫粮无法确认实际营养成分是否达标，是否符合要求。采集各电商平台宠物猫粮的抽检及检测数据，通过分析各平台各品牌宠物粮各项基础营养成分数据，形成电商平台宠物猫粮基础营养成分分析数据集。电商平台可根据数据集提供的多维度宠物粮基础营养成分数据分析，生成推荐榜单，便于消费者对不同品牌的宠物粮进行对比、分析，帮助消费者选择和决策，同时提高下单效率。1.数据采集：通过抽检的宠物猫粮，测得电商平台宠物猫粮各项包括粗蛋白、粗脂肪、粗灰分、粗纤维、水分在内的多种营养成分指标实测值及检测结论数据。
2.数据处理：对采集到的数据按电商平台、宠物粮品牌行分类、合并、累加等。
3．算法加工：
1）测得出电商平台销售的品牌宠物猫粮各项营养指标实测值xn（n=1、2…，表示各类营养成分指标)及其检测结论Yn（n=1、2…，表示各项营养指标检测结论）；
2）计算得出当前品牌猫粮在其销售的电商平台的所有合格宠物猫粮单个营养指标平均值Zn（n=1、2…，表示各类营养成分指标);
3）当单个猫粮的某个营养指数xn>=其销售平台所有猫粮某个营养指数平均值Zn，则推荐某个营养成分为高，是否推荐赋值为1，否则赋值为0；
4）当单个猫粮的某个营养指标在历史抽检检测数据（所有平台）中存在不合格数据的宠物粮对是否提示风险字段赋值为1，否则赋值为0。

As the scale of the pet market continues to expand, pet food consumption has gradually become a core segment of the consumer market. When purchasing various cat foods sold on e-commerce platforms, pet owners are unable to verify whether the actual nutritional components meet the established standards and requirements. This dataset for basic nutritional component analysis of e-commerce platform cat foods is developed by collecting sampling inspection and laboratory testing data of pet cat foods from major e-commerce platforms, and analyzing the basic nutritional indicator data of each brand across different platforms. E-commerce platforms can generate targeted recommendation rankings based on the multi-dimensional basic nutritional component analysis data provided by this dataset, allowing consumers to compare and analyze different brands of cat foods, assisting them in making informed purchasing decisions and improving order placement efficiency.
1. Data Collection: Through sampling inspection of pet cat foods sold on e-commerce platforms, actual measured values of multiple nutritional indicators including crude protein, crude fat, crude ash, crude fiber, and moisture, as well as corresponding test conclusion data, are collected.
2. Data Processing: The collected data is classified, merged, aggregated, and otherwise processed according to e-commerce platforms and pet cat food brands.
3. Algorithm Processing:
1) Calculate the actual measured values of various nutritional indicators $x_n$ (where $n=1, 2, dots$ represents different types of nutritional indicators) and their test conclusions $Y_n$ (where $n=1, 2, dots$ represents the test results of each nutritional indicator) for branded pet cat foods sold on e-commerce platforms;
2) Calculate the average value $Z_n$ of individual nutritional indicators for all qualified pet cat foods of the current brand sold on its associated e-commerce platform (where $n=1, 2, dots$ represents different types of nutritional indicators);
3) If the nutritional index $x_n$ of a single cat food is greater than or equal to the average value $Z_n$ of the corresponding nutritional index of all cat foods sold on its sales platform, assign a value of 1 to the recommendation flag for this nutritional component, indicating it meets the high-quality standard, otherwise assign a value of 0;
4) If the nutritional indicator of a single cat food has unqualified records in the historical sampling inspection and testing data across all platforms, assign a value of 1 to the risk warning field, otherwise assign a value of 0.

搜集汇总

数据集介绍

特点

该数据集提供了电商平台宠物猫粮的基础营养成分分析数据，包含934条记录，涉及多个品牌的猫粮在粗蛋白、粗脂肪等营养成分的检测结果及其是否符合标准的信息。数据通过抽检和检测获得，并经过分类、合并等处理，最终用于生成推荐榜单，帮助消费者进行选择和决策。