The potential of camera vision, optical backscattered properties and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea Batatas L.) during drying

Onwude, Daniel I. and Hashim, Norhashila and Abdan, Khalina and Janius, Rimfiel and Chen, Guangnan (2017) The potential of camera vision, optical backscattered properties and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea Batatas L.) during drying. Journal of the Science of Food and Agriculture. ISSN 0022-5142

Abstract

BACKGROUND: Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50–70 ∘C and samples thicknesses of 2–6 mm.The volume and surface area obtained fromcamera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying. RESULTS: The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. Amultilayer perceptron (MLP) artificial neural networkwith input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer,was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95. CONCLUSION: Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 30 Jan 2018 02:01
Last Modified: 02 May 2018 03:47
Uncontrolled Keywords: drying; shrinkage; computer vision; laser backscattering; process control; sweet potato
Fields of Research : 07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070307 Crop and Pasture Post Harvest Technologies (incl. Transportation and Storage)
Socio-Economic Objective: B Economic Development > 82 Plant Production and Plant Primary Products > 8206 Harvesting and Packing of Plant Products > 820605 Unprocessed Grains
Identification Number or DOI: 10.1002/jsfa.8595
URI: http://eprints.usq.edu.au/id/eprint/33241

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