Infrared technology key to non-destructive food quality testing

Non-destructive food quality testing could soon be a reality, thanks to researchers from the University of Western Australia (UWA).

The technique is similar to using infrared thermometers to detect body temperature by converting information about the colour of skin into a prediction of internal body temperature. Associate Professor Christian Nansen from UWA’s Institute of Agriculture and School of Animal Biology is applying this technology to class food products.

“With this technology, food items moving down a conveyor belt can easily be ‘tagged’ by an infrared 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,” Associate Professor Nansen said.

“It is similar to the baggage handling system at an airport: the infrared 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 aeroplane.”

The technology can be applied to develop quality control systems for foods in order to detect and quantify defects in grains, fruits and vegetables, as well as pesticide residues and meat quality.

Historically, classification accuracy has been difficult to achieve due to the differences in size, surface textures and colours of fruits and vegetables.

In his latest research published in the Journal of Food Engineering, Associate Professor Nansen has collaborated with Associate Professor Guijun Yan, Dr Nader Aryamanesh and Masters student Xuechen Zhang at UWA to explore whether this technology could also be used to detect weevil infestation inside field pea.

“The research question was whether field peas infested with beetles reflected light differently compared to field peas without internal beetle infestations,” Associate Professor Nansen said.

The team behind this project used 12 varieties of field peas with and without pea weevil infestation with pea varieties encompassing a wide range of background colours.

The researchers compared different classification methods and found that one developed by Associate Professor Nansen’s group outperformed more conventional classification methods, paving the way for accurate, large-scale, commercially viable classification of food items that can be performed under significant time constraints.

The project has been funded by an Australian Research Council grant. Associate Professor Nansen is also supported by the Grains Research and Development Corporation.