Peanut contamination could be easier to detect with infrared hyperspectral imaging
Posted: 20 April 2015 | Victoria White | No comments yet
New research has revealed that peanut contamination in food products could soon become much easier to detect using NIR hyperspectral imaging (HSI)…
New research has revealed that peanut contamination in food products could soon become much easier to detect.
Peanut contamination, even at trace levels, can be a major problem for individuals who are allergic to peanuts, potentially triggering a life-threatening reaction. Any food product may contain traces of peanut if it is made with powdered foodstuffs like wheat flour that were ground up in a facility that also processes peanuts, as it can be impossible to prevent contamination from occurring.
Although there are several techniques for detecting peanut contamination, they tend to be time-consuming and only work with small samples, which may not be representative of the foodstuff as a whole. New research published in JNIRS – Journal of Near Infrared Spectroscopy shows that a novel form of near infrared (NIR) spectroscopy known as NIR hyperspectral imaging (HSI) could be used for faster and more accurate detection of peanut contamination.
NIR spectroscopy is an analytical technique that detects specific molecules based on their absorption and reflection of light at near infrared wavelengths. Scientists have already shown that peanut powder generates different NIR spectra to various other powdered foodstuffs, including wheat flour, milk and cocoa, allowing any contamination to be detected.
NIR HSI can be used to detect trace levels of peanut over a large area
Conventional NIR spectroscopy collects an average NIR spectrum over a large area, meaning that trace peanut contamination may be missed, but a team of scientists at the LPF-TAGRALIA, Universidad Politécnica de Madrid (UPM) and the Institute National de Recherche en Sciences et Technologies pour L’environnement et L’agriculture (IRSTEA) decided to try to solve this problem using NIR HSI, which produces images in which every pixel contains spectral data.
Each pixel can contain information about peanut contamination, making NIR HSI much more sensitive than conventional NIR spectroscopy and allowing it to detect trace levels of peanut over a large area. As a first test, the team of scientists confirmed that peanut powder generates different NIR spectra to wheat flour when analysed by NIR HSI, allowing the two powders to be distinguished from each other.
Next, they developed a scoring system that could determine whether or not specific pixels in an image of wheat flour contained peanut powder from their NIR spectra. Using this scoring system, they could then estimate the level of contamination by simply determining the percentage of pixels that contained peanut powder.
NIR HSI was able to detect peanut contamination at 0.01%
They tested this system on samples of wheat flour spiked with powder from four different types of peanut, including raw, blanched and roasted, at concentrations varying between 0.01% and 10%. The system was able to detect peanut contamination even at 0.01%, although it could only accurately determine the level of contamination at between 0.1% and 10%.
“These results show the feasibility of using HSI systems for detecting traces of peanut and similar products that are present in low percentages in powder foods with contrasting spectra,” says lead researcher Puneet Mishra at UPM.
Mishra and his colleagues are now looking to apply the same technique to detecting contamination by other nuts, which can also cause serious allergic reactions.
“Although peanut is the most common cause of nut allergy, peanut allergic patients are frequently also sensitive to tree nuts,” Mishra explained. “We are presently sampling different tree nut mixtures of almond, walnut and hazelnut to check the feasibility of HSI for detecting them.”