Scientists at the University of Western Australia are developing innovative new ways to assess food quality by using infra-red technology.
The technology is similar to that of infra-red thermometers which are used to detect fever in humans by converting information of the colour of the skin into an estimate of internal body temperature. Associate professor, Christian Nansen believes that the same technology can be used to assess food quality.
"With this technology, food items moving down a conveyor belt can easily be ‘tagged' by an infra-red scanner, and fast computers can quickly analyse the imaging data and determine whether or not a given food item needs to be rejected, or whether it needs to be diverted to the cargo bin for lower-grade food items,” said Nansen.
"It is similar to the baggage handling system at an airport: the infra-red scan taken along the conveyor belt represents the ‘tag' which ensures that each item of luggage – or fruit – gets to the right cargo bin and airplane."
Nansen says that although the prevalence of imaging technology systems for unprocessed and processed food items is growing, most detect and quantify defects in grains, fruit and vegetables and meat quality and that one of the biggest challenges – the fact that fresh produce varies greatly in size and in surface texture – colour classification based on surface colour via imaging technology is generally associated with low classification accuracy.
Nansen also adds that his latest research is looking to explore whether the technology can be used to detect weevil infestation inside field peas.
"The research question was whether field peas infested with beetles reflected light differently compared to field peas without internal beetle infestations," said Nansen.
Nansen’s research which is a collaboration between Associate Professor Guijun Yan, Dr Nader Aryamanesh and Masters student Xuechen Zhang at UWA has been published in the Journal of Food Engineering.
The researchers compared different classification methods and found that Nansen’s outperformed the more conventional classification methods. Nansen believes that the new approach could pave the way for accurate large-scale, commercially viable classification of food items that can be performed under tight time constraints.