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| Moscetti, Roberto; Raponi, Flavio; Sturm, Barbara; Nallan Chakravatula, Swathi Sirisha; Massantini, Riccardo. |
| Usage of computer vision (CV) as Process Analytical Technology tool in drying of apple slices was tested. Samples were subjected to various anti-browning treatments at sub- and atmospheric pressures, and dried at 60°C up to a moisture content (dry basis) of 0.18 g/g. CV-based prediction models of changes in moisture content (wet basis) were developed and promising results were obtained (R2P > 0.99, RMSEP = 0.01÷0.06 and BIASP < 0.06 in absolute value), regardless of the anti-browning treatment. |
| Tipo: Conference paper, poster, etc. |
Palavras-chave: Food security; Food quality and human health Processing; Packaging and transportation. |
| Ano: 2019 |
URL: http://orgprints.org/36551/1/Eurodrying2019_full_paper_42_revision.pdf |
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| Moscetti, Roberto; Massaro, Simone; Chillemi, Giovanni; Sanna, Nico; Sturm, Barbara; Nallan Chakravatula, Swathi Sirisha; Massantini, Riccardo. |
| Deep learning was tested for its feasibility as CV tool for the analysis of inlet wet food into the drying process. In detail, convolutional neural networks (CNNs) were successfully applied for addressing the following tasks: (i) the semantic image segmentation of the inlet product; (ii) the inlet product classification for automatic selection of drying parameters. As a result, CNNs have been shown to be used for the development of smart dryers able to monitor and control the process. |
| Tipo: Conference paper, poster, etc. |
Palavras-chave: Food security; Food quality and human health Processing; Packaging and transportation. |
| Ano: 2019 |
URL: http://orgprints.org/36552/1/Eurodrying2019_full_paper_167_revision.pdf |
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