Next Article in Journal
A Hybrid RES Distributed Generation System for Autonomous Islands: A DER-CAM and Storage-Based Economic and Optimal Dispatch Analysis
Next Article in Special Issue
Thermal and Fluid Dynamic Analysis within a Batch Micro-Reactor for Biodiesel Production from Waste Vegetable Oil
Previous Article in Journal
Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessReview
Sustainability 2017, 9(11), 2009; doi:10.3390/su9112009

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

1
Department for Innovation in Biological, Agro-food and Forest system (DIBAF), Tuscia University, 01100 Viterbo, Italy
2
Department of Agricultural and Forestry Sciences, (DAFNE) Tuscia University, Via San Camillo de Lellis snc, 01100 Viterbo, Italy
*
Author to whom correspondence should be addressed.
Received: 27 September 2017 / Revised: 18 October 2017 / Accepted: 28 October 2017 / Published: 2 November 2017
View Full-Text   |   Download PDF [1705 KB, uploaded 2 November 2017]   |  

Abstract

An 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
Keywords: non-destructive technique; visible-near infrared spectroscopy (Vis-NIR); chemometrics; hyper-/multi-spectral imaging spectroscopy; drying process optimization; quality changes during drying non-destructive technique; visible-near infrared spectroscopy (Vis-NIR); chemometrics; hyper-/multi-spectral imaging spectroscopy; drying process optimization; quality changes during drying
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top