It is estimated that one third of the EU's total agricultural production is discarded as "ugly" food, although of perfectly good quality and fit for human consumption. To avoid this waste, ugly food needs to be identified in advance through an effective assessment that guarantees quality and provides accurate shelf-life information.

This Living Lab aims to classify tomato production at an early stage by quality and usefulness/shelf life by applying non-destructive and multi-attribute analysis techniques by VIS-NIR to single fruits. Moreover, the viability of the line identification of organic tomato by a novel technology using mass spectrometry on a small sample will be tested for future full-scale implementation.

The problem of identification of ugly food: A useful life analysis

• Identification of ‘ugly food’ or non-compliance to retail requirements at a late stage reduces the possibilities for alternative valorisation.
• Marketing and logistics decisions towards reducing FLW are difficult, as reliable information on food shelf-life losses and alternative opportunities are missing.
• Shelflife estimations/approaches are not accurate and it takes an excessive amount of time to avoid food waste.
• Analysis to certify that samples are organic take too long, and it may happen that an order identified as organic is not, and has been sold as organic, resulting in a claim by the customer and food waste.

Recovery strategies with an impact

The technology tested allows “ugly food” selection to be performed at a very early stage in an efficient manner. The main innovation is the combination of artificial intelligence (AI) and real-time data processing techniques with further hardware development which would facilitate non-destructive sensor deployment in industrial environments.

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