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Malta Researchers to Develop a Non-Contact Hyperspectral Imaging Method to Detect Microbial Contamination in Food

Image created by Dr. Michael J. Miller

A project financed by the Malta Council for Science and Technology has developed a non-contact and non-destructive approach for the early detection of microbial contaminants that are responsible for food spoilage, with a focus on slow-growing fungi in dairy products.

The MCST awarded €195,000 to the project, which involved a collaboration between the Centre for Biomedical Cybernetics, the Department of Food Sciences and Nutrition at the University of Malta, and Farm Fresh Ltd.

Every year, the European dairy industry processes approximately 152 million tons of raw milk, for consumption or for the production of food, feed and pharmaceutical products. The raw milk delivered by the EU-25’s 1.6 million dairy farmers, processed by the dairy industry, plays a vital role in rural areas, and the dairy industry represents approximately 15 per cent of the turnover of the food and drinks industry in Europe, employing about 13 per cent of the total workforce.

Typical tests currently in use for the analysis of milk products rely on lengthy procedures that can last from 24 to 36 hours for bacterial analysis, and seven to eight days for fungal analysis. Alternative methods such as rapid genomic subtyping may be faster but are very costly for SMEs not running their own research and development department, while the efficacy of methods such as infrared spectroscopy can be limited if the presence of water is above specific thresholds.

Owen Falzon, senior lecturer at the University’s Centre for Biomedical Cybernetics, said: “The FIHI project consortium investigated the use of a hyperspectral imaging to assess the characteristics of food products at different spectral bands. These images can be considered as a fingerprint that characterises the composition of the object being analysed.

“Through the automated processing and analysis of the hyperspectral data, this system can help identify contaminated products while reducing time and effort for food sample inspection.”

In light of recent food-borne illness outbreaks, the early detection of contaminated products in the processing chain can allow for immediate action to prevent contaminated batches from moving further down the production and distribution line and reaching the end customer, leading to a significant social as well as economic impact especially in regions at greater risk.

The Maltese ġbejna (cheeselet) is made from sheep or goat milk curds and aged for several months to develop its distinctive taste. During the ageing process, the cheese can become spoiled by fungi and unsafe for human consumption.

This is a significant public health risk and a financial liability for producers. Conventional microbiology techniques may involve lengthy analysis procedures for the detection of these slow-growing unpigmented fungi, allowing occasional distribution of contaminated products.

To test this hypothesis, a model cheeselet was produced with the involvement of Farm Fresh Ltd to conduct compatibility and stability studies, through measurements of colony forming units, water activity, moisture levels, pH, protein and sugar content. The ġbejna model was then challenged with fungal strains isolated from commercial ġbejna and imaged using a hyperspectral camera.

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