Kelis Cardoso Victor Gustavo, Balog Julia, Zsellér Viktor, Karancsi Tamas, Sabin Guilherme Post, Hantao Leandro Wang
Institute of Chemistry, University of Campinas, Campinas, Brazil; National Institute of Science and Technology in Bioanalytics (INCTBio), Campinas, Brazil.
Waters Research Center, Budapest, Hungary.
Food Res Int. 2025 Feb;203:115773. doi: 10.1016/j.foodres.2025.115773. Epub 2025 Jan 23.
Coffee is an important commodity in the worldwide economy and smart technologies are important for accurate quality control and consumer-oriented product development. Sensory perception is probably the most important information since it is directly related to product acceptance. However, sensory analysis is imprecise and present large deviation related to subjectivity and relying exclusively on the sensory panel. Thus, practical technologies may be developed to assist in making accurate decisions.
This study presents a new method applying laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) coupled with high-resolution mass spectrometry to fingerprint coffee samples. Predictive models have estimated sensory properties with accuracies between 87 and 96 % for test samples. The complex relationship between the MS profiles and modelled properties, artificial neural networks (ANN) outperformed partial least square-discriminant analysis (PLS-DA) on estimation of coffee properties. Tentatively identified compounds such as sugars, chlorogenic, and fatty acids were the ones that most affected coffee sensory properties according to a novel approach to evaluate ANN weights.
The proposed method could analyse coffee samples with minimal sample preparation using an automated device. Predictive models can be applied to assist sensory panel on making decision due to accuracies up to 96 % Additionally, a novel algorithm for evaluate m/z importance in ANN models were presented, paving the way for a higher-level of interpretation by using this algorithm.
咖啡是全球经济中的一种重要商品，智能技术对于精确的质量控制和以消费者为导向的产品开发至关重要。感官感知可能是最重要的信息，因为它与产品接受度直接相关。然而，感官分析不精确，且由于主观性以及完全依赖感官评价小组而存在很大偏差。因此，可能需要开发实用技术来辅助做出准确决策。
本研究提出了一种新方法，即应用激光辅助快速蒸发电离质谱（LA-REIMS）结合高分辨率质谱对咖啡样品进行指纹图谱分析。预测模型对测试样品感官特性的估计准确率在87%至96%之间。在MS图谱与建模特性之间的复杂关系方面，人工神经网络（ANN）在咖啡特性估计方面优于偏最小二乘判别分析（PLS-DA）。根据一种评估ANN权重的新方法，初步鉴定出的化合物如糖类、绿原酸和脂肪酸是对咖啡感官特性影响最大的物质。
所提出的方法可以使用自动化设备以最少的样品制备来分析咖啡样品。由于准确率高达96%，预测模型可用于辅助感官评价小组做出决策。此外，还提出了一种在ANN模型中评估m/z重要性的新算法，为使用该算法进行更高层次的解释铺平了道路。
