通过基于挤出的固体模型系统,在咖啡类型中生成来自梅拉德反应的风味化合物
Bei Wang1, Marjanne J Verhoeven1, Vincenzo Fogliano1
1Food Quality and Design Group, Wageningen University & Research, Wageningen, The Netherlands.
Food chemistry
|July 18, 2025
概括
这项研究开发了一种固体食物矩阵模型,以通过梅拉德反应 (MR) 了解咖啡风味的产生. 像氨基酸,糖和酸这样的关键前体显著影响了风味化合物生成.
科学领域:
- 食品化学 食品化学
- 食品加工 食品加工 食品加工
- 感官科学 感官科学
背景情况:
- 梅拉德反应 (MR) 对于加工食品,特别是咖啡的风味开发至关重要.
- 了解前体和反应条件的影响对于控制风味配置文件至关重要.
- 现有的模型可能无法完全捕捉干燥食品加工环境的复杂性.
研究的目的:
- 建立固体食品矩阵 (SFM) 模型,模拟干食品加工,用于咖啡风味研究.
- 调查各种前体和反应参数对梅拉德反应中介风味生成的影响.
- 分析关键风味化合物的形成,如pyrazines和furans.
主要方法:
- 使用小粉,油,酸,氨基酸和糖的SFM开发.
- 通过高湿度挤出,干燥和流化床烤来处理SFM.
- 使用气体染色学/质谱学 (GC/MS) 分析挥发性化合物.
主要成果:
- 油含量改变了SFM的物理性质 (散装密度,膨胀比,颜色).
- 增加的氨基酸 (高达5%) 增加了pyrazine的形成;增加的糖糖 (高达6%) 增加了的产生.
- 酸显著影响了 furan 和 pyrazine 的形成.
- 酸性条件抑制了pyrazines,但偏好了 furan (例如,furfural,2-furanmethanol). 酸性条件抑制了pyrazines,但偏好了 furan (例如,furfural,2-furanmethanol).
结论:
- 开发的SFM是一种有效的工具,用于研究咖啡类似物中由梅拉德反应驱动的风味发展.
- 前体类型和度以及pH值是控制咖啡风味化合物生成的关键因素.
- 这个模型提供了对优化加工条件的洞察力,以获得所需的风味结果.
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