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咖啡的高分辨率分子特征:深入了解其烘焙化学和全面真实性特征的途径。

The high-resolution molecular portrait of coffee: A gateway to insights into its roasting chemistry and comprehensive authenticity profiles.

机构信息

Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Analytical BioGeoChemistry, Helmholtz Association, Helmholtz Munich, Neuherberg, Germany.

Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Analytical Chemistry, Department of Applied Sciences and Mechatronics, Munich University of Applied Sciences, Munich, Germany.

出版信息

Food Chem. 2025 Jan 15;463(Pt 4):141432. doi: 10.1016/j.foodchem.2024.141432. Epub 2024 Sep 24.

DOI:10.1016/j.foodchem.2024.141432
PMID:39378723
Abstract

The direct-infusion of 130 coffee samples into a Fourier-transform ion cyclotron mass spectrometer (FT-ICR-MS) provided an ultra-high resolution perspective on the molecular complexity of coffee: The exceptional resolving power and mass accuracy (± 0.2 ppm) facilitated the annotation of unambiguous molecular formulas to 11,500 mass signals. Utilizing this molecular diversity, we extracted hundreds of compound signals linked to the roasting process through guided Orthogonal Partial Least Squares (OPLS) analysis. Visualizations such as van Krevelen diagrams and Kendrick mass defect analysis provided deeper insights into the intrinsic compositional nature of these compounds and the complex chemistry underlying coffee roasting. Predictive OPLS-DA models established universal molecular profiles for rapid authentication of Coffea arabica versus Coffea canephora (Robusta) coffees. Compositional analysis revealed Robusta specific signals, indicative of tryptophan-conjugates of hydroxycinnamic acids. Complementary LC-ToF-MS confirmed their compound class, building blocks and structures. Their water-soluble nature allows for application across raw and roasted beans, as well as in ready-made coffee products.

摘要

将 130 个咖啡样品直接注入傅里叶变换离子回旋共振质谱仪(FT-ICR-MS),为咖啡的分子复杂性提供了超高分辨率的视角:卓越的分辨率和质量精度(±0.2 ppm)有助于将明确的分子式注释到 11500 个质量信号上。利用这种分子多样性,我们通过引导正交偏最小二乘(OPLS)分析提取了数百个与烘焙过程相关的化合物信号。诸如范·克里夫伦图和肯德里克质量缺陷分析等可视化方法深入了解了这些化合物的内在组成性质以及咖啡烘焙背后的复杂化学。预测性 OPLS-DA 模型为快速鉴定阿拉比卡咖啡和罗布斯塔咖啡(卡内弗拉)建立了通用的分子特征。成分分析揭示了罗布斯塔特有的信号,表明色氨酸与羟基肉桂酸的缀合物。互补的 LC-ToF-MS 证实了它们的化合物类别、构建块和结构。它们的水溶性使其能够应用于生豆和烘焙豆,以及即饮咖啡产品。

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