Using machine learning for food quality and safety assurance

The latest research published by eResearch Office’s Dr Frederic Isingizwe on detecting defects in fresh agri-food products dealt with detecting soft damage to apple fruit while they are still invisible to the naked eye.

Damage to fresh agri-food products due to brute impact or compression force can occur during handling and transport, can be invisible at an early stage but becomes more pronounced with time, either in the consumer’s hands or on a retailer’s shelf. Such damage to fresh produce accelerates the deterioration of fruit and vegetables and can facilitate infections by micro-organisms, which makes products unsafe to consume.

The research was conducted to aid with sorting and grading fresh products, either at an industrial or smaller scale. We demonstrated that these invisible defects can be detected using shortwave hyperspectral imaging techniques and by using machine learning algorithms, we established the degree to which the differentiation of defective from sound apple fruits is feasible.

Read more about this work here.

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