This study aimed to assess the feasibility of a multi-sensor approach for predicting shrinkage of papaya during drying using computer vision methods in combination with optical scattering analysis of light at 650 nm. The top-side area and total surface area derived from computer vision were analyzed, while the illuminated area and light intensity from optical scattering images were used to interpret photon migration in the fruit tissue. The relationship between moisture content and shrinkage in terms of volume and area reduction during drying was satisfactorily explained by a linear model. The results demonstrated that the prediction of papaya shrinkage during drying from top and total surface areas of the sample was possible, but can potentially be improved. Multivariate correlations of computer vision parameters and optical scattering properties showed the enhanced performance for shrinkage prediction. This multi-sensor approach could possibly be applied as a fast, accurate and non-invasive technique for in-line quality control to monitor shrinkage in the production of dried fruits.

https://dx.doi.org/10.1016/j.jfoodeng.2016.05.014

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https://dx.doi.org/10.1016/j.jfoodeng.2016.05.014

Post-Harvesting Technology

Comptuer Vision; Postharvest Technology; Dehydration; Carica

Southeast Asia

Germany; Thailand

Journal of Food Engineering