香精挥发性等级对香氛留香时间的影响分析数据

本数据聚焦于分析不同香精挥发性等级对香氛产品留香时间的影响，揭示了香精分子特性与挥发速率、香气持久度之间的量化关系。为公司（作为经销商）及外部相关方提供了关键决策依据，具有重要的应用价值。具体体现在以下方面：
1.优化香氛产品采购策略​​：公司可根据挥发性等级-留香时间曲线，建立科学的香精评估标准，优先选择挥发性等级搭配合理的香氛产品系列。
2.推动行业技术创新​​：本数据可为制造商提供香精复配优化依据，推动其开发精准挥发性调控技术，实现各调性香精的最佳配比组合。1.数据采集：实时记录不同香精挥发性等级下的香氛留香时间测试数据，包括测试样品编号、测试时间、香精挥发性等级、香氛留香时间/h等字段。
2.数据预处理：（1）对采集的数据进行去噪处理，确保数据准确性。（2）将历史采集的数据（包含本次采集）进行聚合，形成数据集X，并针对数据集X中的香氛留香时间字段，计算出其平均值。
3.计算线性回归斜率a和截距b:基于数据集X（以香精挥发性等级为自变量、香氛留香时间为因变量），运用SLOPE函数，基于最小二乘法原理确定斜率a，运用INTERCEPT函数确定截距b。斜率a表示单位香精挥发性等级变化对香氛留香时间的影响程度，截距b表示基准香精挥发性等级下香氛的留香时间值。
4.结果运用：（1）计算比例系数k：k=|a/香氛留香时间平均值|×100%；（2）若k≥10%，则判定为“高影响”，若5%≤k＜10%，则判定为“中影响”，若k＜5%，则判定为“低影响”。

This dataset focuses on analyzing the impact of different fragrance volatility grades on the longevity of scented products, and reveals the quantitative relationship between fragrance molecular characteristics, volatilization rate and aroma persistence. It provides key decision-making basis for the company (as a distributor) and external stakeholders, and has important application value, which is specifically reflected in the following aspects:
1. Optimize the procurement strategy of scented products: The company can establish a scientific fragrance evaluation standard based on the volatility grade-longevity curve, and prioritize selecting scented product series with a reasonable volatility grade mix.
2. Promote industry technological innovation: This dataset can provide a basis for manufacturers to optimize fragrance compounding, promote the development of precise volatility regulation technologies, and achieve the optimal proportion combination of fragrances with different notes.

Specific data processing steps are as follows:
1. Data collection: Real-time record the test data of scented product longevity under different fragrance volatility grades, including fields such as test sample number, test time, fragrance volatility grade, and scented product longevity/h.
2. Data preprocessing: (1) Denoise the collected data to ensure data accuracy. (2) Aggregate the historically collected data (including this collection) to form dataset X, and calculate the average value of the scented product longevity field in dataset X.
3. Calculate the linear regression slope a and intercept b: Based on dataset X (with fragrance volatility grade as the independent variable and scented product longevity as the dependent variable), use the SLOPE function to determine the slope a based on the principle of least squares, and use the INTERCEPT function to determine the intercept b. Slope a represents the degree of influence of unit change in fragrance volatility grade on scented product longevity, and intercept b represents the scented product longevity value under the reference fragrance volatility grade.
4. Result application: (1) Calculate the proportional coefficient k: k = |a / average scented product longevity| × 100%; (2) If k ≥ 10%, it is judged as "high impact"; if 5% ≤ k < 10%, it is judged as "medium impact"; if k < 5%, it is judged as "low impact".