Postharvest losses in the European fruit & vegetable chain can be reduced. We identified new technological solutions with regard to preserving quality and reducing losses.
Postharvest losses in the European fruit & vegetable chain are difficult to quantify. Data are not easily shared and have a large variance. Our analysis indicates that apples, bananas, stone fruits and strawberries are among the main products with losses in terms of value. Some of the main causes are ripening, decay and chilling injury. More information is provided in the table.
|Product||Main causes of losses in storage, packaging and retail phase|
|Apple and pear||Ripening (firmness loss), scald, decay, internal disorders|
|Banana||Chilling injury, rots, ripening (senescence)|
|Peach, nectarine, plum, apricot||Decay, shrivelling, bruising, internal breakdown, ripening|
|Avocado||Unequal and overripening, internal defects, chilling injury|
|Strawberry||Decay and senescence|
|Kiwifruit||Internal breakdown/ripening, decay, ethylene sensitive|
Besides quantity losses, also quality loss (price) is typical for the postharvest chain. In individual cases, the % loss can be very high with consequences such as claims and loss of markets. Also losses in the consumer phase are partly initiated already during previous steps in the chain. This adds up to the need to invest in new solutions to reduce postharvest losses.
This project provides an overview of existing postharvest treatments, technologies and services with respect to food loss reduction. Subsequently gaps in the market were identified for which potential new solutions are proposed. These include non-destructive quality measurements, new sensors for gaseous compounds, additional postharvest treatments, and chain monitoring combined with intelligence.
Decision Support System
Chain monitoring combined with intelligence may provide a market opportunity which was further assessed.
Routine evaluation of the quality of perishables already takes place throughout the chain. Also storage and transport conditions are often monitored. However the interpretation of these data and its translation into actions could significantly be improved. Players in the postharvest chain are seeking indeed for intelligent interpretation of data to support daily decisions about their flow of goods.
Scientific knowledge is available as a basis to develop predictive quality models (e.g. for expire date) and quality controlled logistic models. These models can be combined in a Decision Support System as a practical tool.