School of Science, Western Sydney University, South Parramatta, NSW 2150, Australia.
Ubiquinox, Parramatta, NSW 2151, Australia.
Molecules. 2023 Feb 8;28(4):1651. doi: 10.3390/molecules28041651.
Coffee is one of the world's most popular beverages, with the global coffee capsule market worth over USD 4 billion and growing. The incidence of coffee fraud is estimated to be up to one in five coffees being contaminated with cheaper blends of coffee. Given the worsening extent of climate change, coffee crop yields are harder to maintain, while demand is increasing. The 2021 Brazil frost delaying or destroying many coffee crops is an example. Hence, the incidence of coffee fraud is expected to increase, and as the market becomes more complex, there needs to be faster, easier, and more robust means of real-time coffee authentication. In this study, we propose the use of novel approaches to postcolumn derivatization (termed herein as in-column derivatization) to visualize the antioxidant profiles of coffee samples, to be later used as indicators for authentication purposes. We propose three simple mathematical similarity metrics for the real-time identification of unknown coffee samples from a sample library. Using the CUPRAC assay, and these metrics, we demonstrate the capabilities of the technique to identify unknown coffee samples from within our library of thirty.
咖啡是世界上最受欢迎的饮料之一，全球咖啡胶囊市场价值超过 40 亿美元，且仍在不断增长。据估计，咖啡掺假的发生率高达五分之一，即每五杯咖啡中就有一杯受到更廉价咖啡混合物的污染。鉴于气候变化的程度不断恶化，咖啡作物的产量更难维持，而需求却在增加。2021 年巴西的霜冻延迟或摧毁了许多咖啡作物就是一个例子。因此，咖啡掺假的发生率预计将会增加，而且随着市场变得更加复杂，需要更快、更容易、更强大的实时咖啡认证手段。在本研究中，我们提出了使用新的柱后衍生化方法（在此称为柱内衍生化）来可视化咖啡样品的抗氧化特性，以便以后用作认证目的的指标。我们提出了三种简单的数学相似性度量标准，用于实时识别样品库中未知的咖啡样品。使用 CUPRAC 测定法和这些度量标准，我们展示了该技术从我们的三十个样品库中识别未知咖啡样品的能力。
