Volume 18, Issue 116 (2021)                   FSCT 2021, 18(116): 143-159 | Back to browse issues page

‎ 10.52547/fsct.18.116.143

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Ganjloo A, Zandi M, Bimakr M, Monajem S. Visual grading of cherry tomatoes coated with aloe vera gel containing hemp seed oil using principal component analysis, artificial neural network and adaptive neuro-fuzzy inference system methods. FSCT 2021; 18 (116) :143-159
URL: http://fsct.modares.ac.ir/article-7-45273-en.html
Visual grading of cherry tomatoes coated with aloe vera gel containing hemp seed oil using principal component analysis, artificial neural network and adaptive neuro-fuzzy inference system methods
Ali Ganjloo 1, Mohsen Zandi2 , Mandana Bimakr2 , Samaneh Monajem2
1- Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan 45371-38791, Iran , aganjloo@znu.ac.ir
2- Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan 45371-38791, Iran
Abstract:   (2421 Views)
In the present study, in the first step, the effect of Aloe vera gel (75% v/v) coating containing different concentrations of hemp seed oil (1-5% v/v) on some physicochemical properties of cherry tomatoes during storage at room temperature was investigated. The results revealed the ability of hemp seed oil to improve the physicochemical properties of cherry tomatoes during storage, although no significant difference was observed between 3 and 5% levels of hemp seed oil (p> 0.05). Slope change in the ripening index trend occurred for A. vera gel (75% v/v) coated sample on day 12 and for A. vera gel containing 3% hemp seed oil coated sample on day 16. Using an image processing system, the changes of the coated samples were evaluated based on the color statistical and color texture features extracted from the images and were graded through different procedures. The results showed that the principal component analysis (PCA) and artificial neural network (ANN) methods were able to divide the cherry tomatoes into intact and blemished grades which the ANN method was graded samples using color texture features with higher accuracy (97.41%). The adaptive neuro-fuzzy inference system (ANFIS) method had higher diagnostic power than the other two methods and was able to grade the samples into three grades including intact, grade 2 and unusable with accuracy of 98.96%.
Keywords: Image processing, Cherry tomato, Ripening index, Fuzzy logic.
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Article Type: Original Research | Subject: food industry engineering
Received: 2020/08/17 | Accepted: 2021/04/25 | Published: 2021/10/2
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