- Author: Kathy Keatley Garvey
And that's grounds for concern, researchers say.
Agricultural entomologist Christian Nansen of the UC Davis Department of Entomology and Nematology and four colleagues analyzed 15 brands of roasted coffee beans, purchased at an area supermarket on two dates about six months apart, and using hyperspectral imaging technology, found “they were all over the board.”
“There was no consistency in the protein/sugar content and within the roasting classes of light, medium, medium dark, and dark or between sampling dates,” said Nansen, who specializes in insect ecology and remote sensing and uses imaging technology to quantify variability and identify trends and patterns in biological systems. “I thought this would be interesting to apply my hyperspectral imaging technology to a commercial system rather than a biological system.”
The research, “Using Hyperspectral Imaging to Characterize the Consistency of Coffee Brands and Their Respective Roasting Classes" is published in the current edition of the Journal of Food Engineering. Hyperspectral imaging involves collecting and processing information from across the electromagnetic spectrum.
Co-authors of the paper are postdoctoral research Keshav Singh of the Nansen lab; assistant professor Christopher Simmons and doctoral candidate Brittany Allison, both in the UC Davis Department of Food Science and Technology; and Ajmal Mian of the University of Western Australia's Computer Science and Software Engineering.
The study is not only relevant to the coffee industry and consumers but to a wide range of commercial food and beverage brands, Nansen said. Statistics show that Americans, the leading consumers of coffee in the world, consume 400 million cups of coffee per day. They spend an average of $21 per week on coffee.
Nansen, a coffee drinker, came by the topic naturally and also out of curiosity. “I got interested in this topic because I like coffee but also because I am certain that many food and beverage products vary markedly in quality. I thought this would be interesting to apply my hyperspectral imaging technology to a commercial system rather than a biological system.”
“The uniqueness and consistency of commercial food and beverage brands are critically important for their marketability,” the researchers wrote in the abstract. “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.”
“We acquired hyperspectral imaging data (selected bands out of 220 narrow spectral bands from 408 nanometers to 1008 nanometers from ground samples of the roasted coffee beans, and reflectance-based classification of roasting classes was associated with fairly low accuracy.”
Their research provides 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,” the scientists wrote. “We demonstrated that a non-destructive method, potentially real-time and automated, and quantitative method can be used to monitor the consistence of a highly complex beverage product.”
The research was funded in part by Mian's ARC Fellowship.