通过FTIR与机器学习方法相结合,对酒中的挥发性化合物进行区分和定量
Yi-Fang Gao1,2, Xiao-Yan Li1,2, Qin-Ling Wang1,2
1Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, PR China.
Food chemistry: X
|April 4, 2024
概括
这项研究使用GC-MS和PCA识别了九种酒类型中的关键挥发性化合物,并根据口味配置文件区分酒. 一种基于FTIR的新方法准确地预测了风味化合物水平,为GC-MS提供了一致的替代方案.
科学领域:
- 食品化学 食品化学
- 分析化学 分析化学
- 感官科学 感官科学
背景情况:
- 挥发性化合物显著影响酒的风味和质量.
- 了解酒的挥发性特征对于质量控制和产品开发至关重要.
- 对挥发性化合物的现有分析方法可能耗时.
研究的目的:
- 为了识别和量化各种酒类型中的挥发性化合物.
- 建立一种基于挥发性概况的酒类型区分方法.
- 开发和验证一个基于FTIR的新型预测模型,用于关键的风味化合物.
主要方法:
- 气色谱-质谱 (GC-MS) 用于挥发性化合物的识别.
- 主要成分分析 (PCA) 用于区分酒类型.
- 福里埃变换红外光谱 (FTIR) 与部分最小平方 (PLS) 回归相结合,用于定量分析.
主要成果:
- 在9种酒类型中确定了23种挥发性化合物 (,醇,酸,).
- 基于挥发性成分,PCA有效地区分了不同类型的酒.
- 关键的区分因素包括乙基酸盐,乙基酸盐和乙醇.
- 对于六种关键的挥发性化合物,FTIR-PLS模型实现了高预测精度 (R2:0.9398-0.9994).
- FTIR-PLS方法与GC-MS结果显示出良好的一致性.
结论:
- 挥发性化合物分析是了解酒质量和多样性的关键.
- PCA是一种强大的工具,可以根据其口味化学成分对酒进行分类.
- 与PLS回归相结合的FTIR光谱学提供了一种快速可靠的方法来预测酒风味化合物,补充GC-MS.
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