Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes

C. Nansen, K. Singh, Ajmal Mian, B.J. Allison, C.W. Simmons

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)


    © 2016 Elsevier Ltd
    The uniqueness and consistency of commercial food and beverage brands are critically important for their marketability. Thus, it is important to develop quality control tools and measures, so that both companies and consumers can monitor whether a given food product or beverage meets certain quality expectations and/or is consistent when purchased at different times or at different locations. In this study, we characterized the consistency (levels of extractable protein and reducing sugars) of 15 brands of roasted coffee beans, which were obtained from a supermarket at two dates about six months apart. Coffee brands varied markedly in extractable protein and reducing sugar contents between dates, and also within and among roasting classes (light, medium, medium-dark, and dark roasts). We acquired hyperspectral imaging data (selected bands out of 220 narrow spectral bands from 408 nm to 1008 nm) from ground samples of the roasted coffee beans, and reflectance-based classification of roasting classes was associated with fairly low accuracy. We provide evidence that the combination of hyperspectral imaging and a general quality indicator (such as extractable protein content) can be used to monitor brand consistency and quality control. We demonstrated that a non-destructive method, potentially real-time and automated, and quantitative method can be used to monitor the consistency of a highly complex beverage product. We believe the results from this study of brand consistency are not only of relevance to the coffee industry but to a wide range of commercial food and beverage brands.
    Original languageEnglish
    Pages (from-to)34-39
    JournalJournal of Food Engineering
    Early online date18 Jun 2016
    Publication statusPublished - Dec 2016


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