Institute of Chemistry, University of Campinas, 270 Monteiro Lobato, Campinas, SP 13083-862, Brazil; National Institute of Science and Technology in Bioanalytics (INCTBio), SP, Campinas, 13083-862 Brazil.
Institute of Chemistry, University of Campinas, 270 Monteiro Lobato, Campinas, SP 13083-862, Brazil; National Institute of Science and Technology in Bioanalytics (INCTBio), SP, Campinas, 13083-862 Brazil.
J Chromatogr A. 2024 Aug 30;1731:465171. doi: 10.1016/j.chroma.2024.465171. Epub 2024 Jul 19.
This paper presents a study that assesses the application of chemometrics for classifying coffee samples in a quality control context. High-resolution and accurate mass measurements were utilized as input for pixel-based orthogonal partial least squares discriminant analysis (OPLS-DA) models. The compositional data were acquired through a fully automated workflow combining headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry (GC-HRMS) using an FT-Orbitrap® mass analyzer. A workflow centered on accurate mass measurements was successfully utilized for group-type analysis, offering an alternative to methods relying solely on MS similarity searches. The predictive models underwent thorough evaluation, demonstrating robust multivariate classification performance. Five key coffee attributes, bitterness, acidity, body, intensity, and roasting level were successfully predicted using GC-HRMS data. The results revealed strong predictive accuracy across all models, ranging from 88.9 % (bitterness) to 94.4 % (roasting level). This study represents a significant advancement in automating methods for coffee quality control, notably increasing the predictive ability of the models compared to existing literature.
本文提出了一项研究，评估了化学计量学在质量控制背景下对咖啡样品进行分类的应用。高分辨率和精确质量测量被用作基于像素的正交偏最小二乘判别分析（OPLS-DA）模型的输入。通过结合顶空固相微萃取和气相色谱-高分辨率质谱（GC-HRMS）的全自动工作流程，获得了组成数据，使用 FT-Orbitrap®质量分析仪。成功地围绕精确质量测量的工作流程用于组类型分析，为仅依赖 MS 相似性搜索的方法提供了替代方法。预测模型经过了彻底的评估，证明了强大的多元分类性能。使用 GC-HRMS 数据成功预测了苦味、酸度、口感、强度和烘焙度等五个关键咖啡属性。结果表明，所有模型的预测准确性都很强，范围从 88.9%（苦味）到 94.4%（烘焙度）。这项研究代表了自动化咖啡质量控制方法的重大进展，与现有文献相比，显著提高了模型的预测能力。
