Modelling sensorial and nutritional changes to better define quality and shelf life of fresh-cut melons - Maria Luisa Amodio; Antonio Derossi; Giancarlo Colelli (Journal of Agricultural Engineering, 2013)
The shelf life of fresh-cut produce is mostly determined by evaluating the external appearance since this is the major factor affecting consumer choice at the moment of purchase. The aim of this study was to investigate the degradation kinetics of the major quality attributes in order to better define the shelf life of fresh-cut melons. Melon pieces were stored for eight days in air at 5°C. Sensorial and physical attributes including colour, external appearance, aroma, translucency, firmness, and chemical constituents, such as soluble solids, fructose, vitamin C, and phenolic content, along with antioxidant activity were monitored. Attributes showing significant changes over time were used to test conventional kinetic models of zero and first order, and Weibullian models. The Weibullian model was the most accurate to describe changes in appearance score, translucency, aroma, firmness and vitamin C (with a regression coefficient always higher than 0.956), while the other parameters could not be predicted with such accuracy by any of the tested models. Vitamin C showed the lowest kinetic rate among the model parameters, even though at the limit of marketability (appearance score 3), estimated at five days, a loss of 37% of its initial content was observed compared to the fresh-cut product, indicating a much lower nutritional value. After five days, the aroma score was already 2.2, suggesting that this quality attribute, together with the vitamin C content, should be taken into account when assessing shelf life of fresh-cut melons. In addition, logistical models were used to fit the percentage of rejected samples on the basis of non-marketability and non-edibility (appearance score <3 and <2, respectively). For both parameters, correlations higher than 0.999 were found at P<0.0001; for each mean score this model helps to understand the distribution of the samples among marketable, nonmarketable, and non-edible products.