APPROACHES TO PREDICTION OF STORAGE OUT-TURN FOR UNITS OF FRESH PRODUCE
The field of postharvest horticulture has traditionally focused on establishing the optimal combination of harvest timing, postharvest treatments and storage technologies which results in the greatest longevity of fresh products. While much has been achieved in reducing crop losses and facilitating global trade of fresh produce, product losses in the postharvest environment which impact on industry profitability still occur, including in developed industries. Many of these losses in established industries are a result of the inherent variability which is observed between batches of the same product. An improved ability to predict storage out-turn would allow improved inventory management so particular batches of product could be targeted to the most appropriate market and maximize industry profitability. Irrespective of whether the mode of product failure is decay, chilling injury or development of advanced senescence, the ability to understand the physiology underlying batch variability and predict the behaviour of each batch would be a powerful tool in stock management. A number of different postharvest approaches including metabolomics, mathematical modeling, non-destructive testing and accelerated libraries have the potential to contribute to improved prediction of storage out-turn. This paper provides examples of each of these approaches and suggests the potential for synergies in ideology and data handling methodologies which may apply across all horticultural industries.
East, A.R., Jabbar, A. and Heyes, J.A. (2013). APPROACHES TO PREDICTION OF STORAGE OUT-TURN FOR UNITS OF FRESH PRODUCE. Acta Hortic. 1012, 1303-1309
inventory management, postharvest, quality, modelling, supply chain