烹饪器具烹饪加水量与成熟重量损失分析数据

该数据可应用于烹饪设备制造企业，用于优化烹饪设备的设计。通过分析不同种类食物烹饪加水量和成熟时的重量损失的关系，得到任意加水量时对应的合理成熟重量损失，烹饪设备可以根据用户的任意加水量进行功率、时间调控，使其成熟时达到合理的重量损失。精准应对烹饪痛点，例如红烧汤汁太多、收干等，以及用户个性化的成熟菜品需求，例如在合理范围内更喜欢汤汁偏多的，为用户提供更精准的智能烹饪服务。1.数据来源 记录烹饪方式、分类、菜谱名称、食材量（g）、加水量（g）、功率1、时间1、功率2、时间2、成熟菜品重量（g）、成熟重量损失（g）、汤汁量（偏少、适中、偏多）、感官评价评分（满分5分）等数据。 2.数据处理 评估模型构建，模型构建针对不同的测试对象，加水量（X）作为自变量，成熟重量损失（Y）为因变量进行线性回归得到相应的线性模型 ， 例如红烧肉，当加入不同的加水量（x）时，这道红烧菜品成熟且收汁达到适中的汤汁量时，需要损失的重量按照下述公式计算：y = 0.9795x - 25.139；若用户偏好偏少的汤汁量时，需要损失的重量按照下述公式计算：y = 0.9889x - 14.164；若用户偏好偏多的汤汁量时，需要损失的重量按照下述公式计算：y = 0.9921x - 62.358 3.数据分析 计算决定系数 R²，评估模型的拟合度。R² 大于0.9，模型合格，说明加水量与成熟时重量损失水平强相关，可以根据加水量来较准确地预测成熟时菜品的重量损失，烹饪设备生产商可以根据此规律对菜品烹饪过程进行控制，使其达到合理的成熟重量损失，保证用户的使用效果。 补充说明：其中拟合度R²是根据每种菜谱的数据样本量及测量数据，用excel建模后生成公式，基于得到的公式与该组菜谱的数据情况，由excel自动计算得来，该处理过程常见的数据处理软件excel、spss均可完成。This dataset is applicable to cooking appliance manufacturing enterprises for optimizing the design of cooking equipment. By analyzing the relationship between water addition amount and weight loss at maturity for different types of food, the reasonable mature weight loss corresponding to any given water addition amount can be derived. Cooking equipment can adjust its power and cooking time based on the water addition amount set by the user, so that the cooked dish reaches the target reasonable mature weight loss. This accurately addresses common cooking pain points, such as overly thick or insufficiently reduced sauce for braised dishes, as well as users' personalized preferences for the final cooked dish—for example, preferring a slightly higher sauce volume within a reasonable range—thereby providing users with more precise intelligent cooking services. 1. Data Source The dataset records various information including cooking method, food category, recipe name, ingredient quantity (g), water addition amount (g), power 1, time 1, power 2, time 2, mature dish weight (g), mature weight loss (g), sauce quantity (classified as too little, moderate, too much), and sensory evaluation score (full score of 5 points), among other relevant data. 2. Data Processing An evaluation model is constructed. For different test recipes, a linear regression model is established with water addition amount (X) as the independent variable and mature weight loss (Y) as the dependent variable. Take braised pork as an example: when different amounts of water are added, the weight loss required for the dish to reach maturity with a moderate sauce volume is calculated by the formula: y = 0.9795x - 25.139; if the user prefers a lower sauce volume, the required weight loss is calculated by: y = 0.9889x - 14.164; if the user prefers a higher sauce volume, the required weight loss is calculated by: y = 0.9921x - 62.358. 3. Data Analysis The coefficient of determination R² is calculated to evaluate the goodness of fit of the model. If R² is greater than 0.9, the model is considered qualified, indicating a strong correlation between water addition amount and mature weight loss level, allowing accurate prediction of the dish's mature weight loss based on the water addition amount. Cooking equipment manufacturers can use this rule to control the cooking process of target dishes, ensuring that the final product reaches the reasonable mature weight loss and delivering a consistent user experience. Supplementary Note: The goodness of fit R² is generated through Excel modeling based on the data sample size and measured data of each individual recipe, and automatically calculated by Excel using the derived formula and the dataset of the corresponding recipe group. This processing workflow can be completed using common data processing software such as Excel and SPSS.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于分析不同烹饪器具在烹饪过程中加水量与成熟食材重量损失的关系，通过表格形式展示关键变量，旨在帮助用户理解烹饪参数对食材变化的影响，适用于烹饪优化或相关研究领域。