Food allergies are a common problem with possible life-threatening consequences. Even trace amounts of allergens such as peanuts, milk, or eggs can trigger an allergic reaction. While food labeling is mandatory in many countries, cross-contamination, e.g., in restaurants, can occur easily and pose a danger. Analyzing food to detect allergens usually requires a laboratory. The available on-site tests are often slow and not sensitive enough to provide safety for users.
Ralph Weissleder, Hakho Lee, and colleagues, Harvard Medical School, Boston, MA, USA, have developed a portable device that can detect antigens for the common food allergens peanuts, hazelnuts, wheat, milk, and egg quickly and with high sensitivity. The system consists of a disposable kit to extract the antigens from food and a keychain reader that acts as a sensor and can send the results to a smartphone.
The extraction kit captures antibodies from food allergens on a magnet, which is inserted into a mixture of the food sample and so-called immunomagnetic beads. These magnetic beads have coatings that specifically bind the targeted biomolecules. The magnetically captured antibodies are bound to the enzyme horseradish peroxidase, mixed with 3,3′,5,5′-tetramethylbenzidine (TMB), and placed on an electrode. The electrode is then inserted into the reader. The horseradish peroxidase catalyzes the oxidation of TMB, which generates an electric current that the reader can detect.
The team tested the device on consumer food products and in several cases found allergens not listed on the packaging. Each test takes about 10 minutes. Currently, the device costs about 40 USD, with each test costing an additional 4 USD. The researchers expect these prices to decrease with scale-up of the method. According to the team, the system could be modified to detect other allergens, as well as toxins or other food contaminants.
- Integrated Magneto-Chemical Sensor For On-Site Food Allergen Detection,
Hsing-Ying Lin, Chen-Han Huang, Jongmin Park, Divya Pathania, Cesar M. Castro, Alessio Fasano, Ralph Weissleder, Hakho Lee,
ACS Nano 2017.
DOI: 10.1021/acsnano.7b04318
