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.

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In today’s global economy, the origin of the food we eat is becoming more and more difficult to identify. Verifying the authenticity of products to protect supply chains is a complicated problem that requires a multidisciplinary approach. Chemical authentication of foodstuffs often includes a mass spectrometer detector; however, they are costly and require advanced operating skills. As an alternative, simple and robust post-column derivatization processes undertaken with HPLC may potentially provide chemical signatures of samples based upon the antioxidant profiles of the coffee samples. In this experiment, students employed a HPLC-PCD-VIS hyphenated separation protocol to produce a high-resolution chromatogram with minimal sample preparation, and the analysis was tuned to antioxidants in the coffee. Students encountered common analytical instruments and the commercially relevant CUPRAC assay. Using chromatographically derived data, students constructed MS Excel spreadsheets to normalize data and apply information theory to make statements about the similarity between coffee samples. Students were introduced to analytical protocols and chemometric techniques typical of 21st-century solutions to complex problems while utilizing common analytical instruments and reagents. Students were able to gain real-world laboratory skills, including running HPLC columns for sample analysis, method hyphenation, and signal retrieval. Students then utilized software to plot data for interpretation and to simplify data via preprocessing and chemometric operations to solve real-world problems.