Using HPLC with In-Column Derivatization to Authenticate Coffee Samples

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.

Automated summary

Coffee is a popular beverage, with a growing market worth over USD 4 billion. Coffee fraud is a concerning issue, with up to one in five coffees being contaminated with cheaper blends. Climate change and the recent Brazil frost have further impacted coffee production, leading to a potential increase in coffee fraud. In this study, a novel approach using postcolumn derivatization is proposed to authenticate coffee samples in real-time, along with three mathematical similarity metrics.