Volume 17, Issue 105 (2020)
FSCT 2020, 17(105): 135-149 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ganjloo A, Zandi M, Bimakr M, Monajem S. Ripening Stages Control of Cherry Tomato Coated with Aloe Vera Gel using Artificial Vision System. FSCT 2020; 17 (105) :135-149
URL: http://fsct.modares.ac.ir/article-7-41047-en.html
URL: http://fsct.modares.ac.ir/article-7-41047-en.html
1- Department of Food Science and Engineering, University of Zanjan , aganjloo@znu.ac.ir
2- Department of Food Science and Engineering, University of Zanjan
3- MSc Student of Food Technology,Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
2- Department of Food Science and Engineering, University of Zanjan
3- MSc Student of Food Technology,Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
Abstract: (2909 Views)
It is important to control the ripening stages of agricultural products during storage and their quality grading based on their ripening stage. Edible coatings can prolong the storage life of agricultural products and protect them through the handling, storage, processing and marketing. The purpose of the current study was to develop an artificial vision system for quality control and segregation of cherry tomatoes in two different conditions including with and without Aloe vera gel coating. For this purpose, physicochemical properties including titrable acidity, total soluble solids and firmness of cherry tomatoes were measured in both conditions. Based on these properties, the ripening index (RPI) was determined and the samples were classified to MS1 and MS2 according to the ripening stage. Subsequently, the samples were classified using color features, color texture features separately and their combination through principal component analysis (PCA) and back propagation neural network (BPNN). Classification results showed that the use of color and color texture features combination made the classification more accurate; PCA and BPNN methods were able to segregate the samples with high accuracy (85.72 and 98.21, respectively) using the 21 color and color texture features. The higher accuracy of the BPNN method is due to its nonlinear performance. The results of this study indicate that Aloe vera gel is promising in delaying the ripening process of cherry tomatoes and artificial vision system can be used as a non-destructive method for evaluation of cherry tomato ripening level based on the color and color texture features.
Keywords: Cherry tomato, Degree of maturity, Image processing, Multivariate analysis, Back propagation artificial neural network.
Article Type: Original Research |
Subject:
food industry engineering
Received: 2020/02/28 | Accepted: 2020/06/9 | Published: 2020/10/31
Received: 2020/02/28 | Accepted: 2020/06/9 | Published: 2020/10/31
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |