精确识别甲醇和乙醇汽油类型,并使用有效的化学信息快速检测酒精含量
Ke Li1, Chaomin Ding2, Jin Zhang2
1Center for Environmental Metrology, National Institute of Metrology, Beijing, 100029, China.
Talanta
|March 31, 2024
概括
开发了有效的化学信息 (ECI) 模型,使用近红外 (NIR) 频谱用于甲醇和乙醇汽油. 该ECI-部分最小平方差分分析 (PLS-DA) 模型在识别和量化燃料中的酒精含量方面实现了100%的准确性.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 燃料科学 燃料科学 燃料科学
背景情况:
- 甲醇和乙醇汽油正在成为新兴的清洁能源替代品.
- 准确识别和量化汽油中酒精含量至关重要.
- 现有的方法可能缺乏用于区分甲醇和乙醇汽油的特异性.
研究的目的:
- 开发有效的化学信息 (ECI) 模型,用于对汽油中的甲醇和乙醇进行定性识别和定量分析.
- 为了比较ECI模型与传统光谱分析方法的性能.
- 建立一种快速而准确的燃料分析方法.
主要方法:
- 使用近红外 (NIR) 光谱法获取光谱数据.
- 开发了ECI模型,使用部分最小平方区分分析 (PLS-DA) 和部分最小平方 (PLS) 算法.
- 将ECI模型与基于全频谱,预测变量重要性 (VIP) 和蒙特卡洛无信息变量消除 (MC-UVE) 的模型进行比较.
主要成果:
- 该ECI-PLS-DA模型实现了100%的灵敏度,特异性和准确性,用于定性识别.
- 量化分析的ECI-PLS模型显示,低根平均平方预测误差 (RMSEP) 为甲醇的0.18% (v/v) 和乙醇的0.21% (v/v).
- 与标准方法 (F-test和T-test) 相比,使用ECI-PLS的NIR方法没有显著差异.
结论:
- 与其他光谱模型相比,ECI模型显示出更高的识别成功率和确定精度.
- 这项研究建立了基于化学结构的燃料组成的定性和定量分析的高度准确和快速确定模型.
- 开发的ECI模型有可能扩展到使用NIR光谱分析燃料的其他物理化学性质.
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