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. 2019 Dec 26;8(2):884-893.
doi: 10.1002/fsn3.1365. eCollection 2020 Feb.

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Mahsa Sadat Razavi  1 , Abdollah Golmohammadi  2 , Reza Sedghi  3 , Ali Asghari  4
Affiliations

Prediction of bruise volume propagation of pear during the storage using soft computing methods

Mahsa Sadat Razavi et al. Food Sci Nutr. .

Abstract

Bruises occur under both static and dynamic loadings when the imposed stress on fruit goes over the failure stress of the fruit tissue. Bruise damage is the main reason for fruit quality loss. In this study, the potential of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression (MR) techniques to predict bruise volume propagation of pears during the storage time was evaluated. For this purpose, at first, the radius of curvature at loading region was obtained. Samples were divided into five groups and subjected to five force levels. Then, they were kept under storage conditions and at 7-time intervals after loading tests, bruise volume was calculated using magnetic resonance imaging (MRI) and image processing techniques. Force, storage time, and radius of curvature at loading region were employed as input variables, and bruise volume (BV) was considered as output in the developed models. Multilayer perceptron (MLP) artificial neural network with three layers that includes an input layer (three neurons), two hidden layers (two and nine neurons), and one output layer was used. For the evaluation of models, three criteria (RMSE, VAF, and R 2) were calculated. ANN and MR gave the highest and lowest correlation between predicted and actual values, respectively. These results indicate that the ANN techniques can be used to predict pear bruising propagation in storage time.

Keywords: adaptive neuro‐fuzzy inference system; artificial neural network; bruise; image processing; magnetic resonance imaging; multiple regression; storage.

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Conflict of interest statement

The authors declare that they do not have any conflict of interest.

Figures

Figure 1
Figure 1
R 2 values for different number of neurons in second hidden layer and fixed 2 neurons in first hidden layer
Figure 2
Figure 2
R 2 values for 10‐fold cross‐validation method
Figure 3
Figure 3
Cross‐correlation of observed and predicted values of BV for MR model
Figure 4
Figure 4
Cross‐correlation of observed and predicted values of BV for ANN model
Figure 5
Figure 5
Cross‐correlation of predicted and observed values of BV for ANFIS model
Figure 6
Figure 6
The variation of the values predicted by MR, ANN, and ANFIS model from the observed values
See this image and copyright information in PMC

References

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