Aquatic and Crop Resources Development Research Center, National Research Council of Canada, 1411 Oxford Street, Halifax, Nova Scotia B3H 3Z1, Canada.
NHP Research Alliance, College of Biological Sciences, University of Guelph, Guelph, Ontario N1G 4T2, Canada.
J Agric Food Chem. 2020 Dec 9;68(49):14643-14651. doi: 10.1021/acs.jafc.0c06239. Epub 2020 Nov 30.
In response to the need from the food industry for new analytical solutions, a fit-for-purpose quantitative H NMR methodology was developed to authenticate pure coffee (100% arabica or robusta) as well as predict the percentage of robusta in blends through the study of 292 roasted coffee samples in triplicate. Methanol was chosen as the extraction solvent, which led to the quantitation of 12 coffee constituents: caffeine, trigonelline, 3- and 5-caffeoylquinic acid, lipids, cafestol, nicotinic acid, -methylpyridinium, formic acid, acetic acid, kahweol, and 16--methylcafestol. To overcome the chemical complexity of the methanolic extract, quantitative analysis was performed using a combination of traditional integration and spectral deconvolution methods. As a result, the proposed methodology provides a systematic methodology and a linear regression model to support the classification of known and unknown roasted coffees and their blends.
针对食品行业对新型分析解决方案的需求,开发了一种适用的定量 H NMR 方法,通过对 292 个重复烤咖啡样本的研究,对纯咖啡(100%阿拉比卡或罗布斯塔)进行了验证,并对混合物中罗布斯塔的百分比进行了预测。选择甲醇作为提取溶剂,定量分析了 12 种咖啡成分:咖啡因、葫芦巴碱、3-和 5-咖啡酰奎宁酸、脂类、卡法醇、烟酸、-甲基吡啶、甲酸、乙酸、卡瓦醇和 16--甲基卡法醇。为了克服甲醇提取物的化学复杂性,使用传统积分和光谱解卷积方法的组合进行定量分析。因此,所提出的方法提供了一种系统的方法和线性回归模型,以支持已知和未知烤咖啡及其混合物的分类。