Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review
AbstractAn overview is given regarding the most recent use of non-destructive techniques during drying used to monitor quality changes in fruits and vegetables. Quality changes were commonly investigated in order to improve the sensory properties (i.e., appearance, texture, flavor and aroma), nutritive values, chemical constituents and mechanical properties of drying products. The application of single-point spectroscopy coupled with drying was discussed by virtue of its potentiality to improve the overall efficiency of the process. With a similar purpose, the implementation of a machine vision (MV) system used to inspect foods during drying was investigated; MV, indeed, can easily monitor physical changes (e.g., color, size, texture and shape) in fruits and vegetables during the drying process. Hyperspectral imaging spectroscopy is a sophisticated technology since it is able to combine the advantages of spectroscopy and machine vision. As a consequence, its application to drying of fruits and vegetables was reviewed. Finally, attention was focused on the implementation of sensors in an on-line process based on the technologies mentioned above. This is a necessary step in order to turn the conventional dryer into a smart dryer, which is a more sustainable way to produce high quality dried fruits and vegetables. View Full-Text
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Raponi, F.; Moscetti, R.; Monarca, D.; Colantoni, A.; Massantini, R. Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review. Sustainability 2017, 9, 2009.
Raponi F, Moscetti R, Monarca D, Colantoni A, Massantini R. Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review. Sustainability. 2017; 9(11):2009.Chicago/Turabian Style
Raponi, Flavio; Moscetti, Roberto; Monarca, Danilo; Colantoni, Andrea; Massantini, Riccardo. 2017. "Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review." Sustainability 9, no. 11: 2009.
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