Quality Evaluation of Chilli during Drying Using Image Analysis and Artificial Neural Networks 1
Rattapon Saengrayap*, 1Ampawan Tansakul and 2Gauri S. Mittal
1
Department of Food Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, 10140 Thailand 2 School of Engineering, University of Guelph, Ontario, N1G 2W1 Canada * Corresponding author:
[email protected]
Results & discussion
Introduction The changes of product’s qualities were dependent on several factors. Artificial neural network (ANN) modeling is a computational method that can be used to handle complicated relationships between physical properties of foods and process parameters. The objectives of this study were to develop the image analysis procedure for quality assessment and to propose the guideline for developing ANN algorithm of online-control system.
Materials & methods Chilli was dried in a far-infrared assisted microwave-vacuum dryer with different drying conditions. In the first part, dried chilli was evaluated in terms of color, hardness, rehydration ability and shrinkage.
The developed method showed a good efficiency to measure all properties. The obtained properties from the developed method were not significant different from those of conventional method. Table 2: The correlations between actual values and values that obtained from image analysis Parameters
R2
Parameters
R2
Width
0.990
L*
0.965
Length
0.991
a*
0.979
Area
0.990
b*
0.947
Perimeter
0.996
E
0.970
Volume
0.964
According to the prediction results and training time, the 10-10-10 hidden node WN3 was chosen for further analysis. The proper values of learning rate and momentum will improve the ANN performance. The effects of learning rate and momentum on prediction errors are given in Figure 3. Fig. 1: Schematic diagram of the combination FIR with microwave-vacuum dryer
In the second part, dried chilli was taken the image and the passed through image processing step for determining dimensions and color. The results from image analysis method were compared with those of conventional method. In the third part, during drying, chilli was taken from the dryer for taking a photo. Chilli’s images were analysed to determine the changes of dimensions and color. Fig. 3: The effect of learning rate and momentum on the performance of the ANN
The results showed that the learning rate = 0.3 and momentum = 0.3 provided the best ANN performance and minimum prediction errors when compared with other combinations (Table 3). Table 3: Prediction error and performance of the developed ANN Performance indices R2 MRE SD of MRE MAE SD of MAE
Outputs L*/L0*
a*/a0*
b*/b0*
E*
HN
RR
SC
0.998 0.001 0.001 0.001 0.001
0.999 0.004 0.005 0.002 0.002
0.999 0.003 0.004 0.003 0.003
0.999 0.002 0.002 0.003 0.005
0.995 0.001 0.004 0.003 0.003
0.963 0.001 0.001 0.002 0.001
0.903 0.005 0.005 0.002 0.002
Average 0.980 -
Conclusion
Fig. 2: Determination of color and physical properties using image analysis
Artificial neural network development
The properties of chilli could be assessed using image analysis and were comparable with those of the conventional methods. In addition, the suitable model for predicting the quality changes of chilli was 10-10-10 hidden neuron – 3 slabs - ‘Wardnets’ and the activation functions were Gaussian, Gaussian complement and tanh; the learning rate and momentum were 0.3 and 0.3, respectively.
ANNs were developed using NeuroShell® 2 Release 4.2 (Ward System Group., Acknowledgement Inc., Frederick, MD). Four different ANN architecture were tested. Moreover, The author Saengrayap would like to thank the Royal Golden Jubilee (RGJ) numbers of hidden neuron, activation functions, the learning rate and Ph.D. Program of the Thailand Research Fund (TRF) and King Mongkut’s University of Technology Thonburi for supporting his doctoral study. This Table 1: Inputs and outputs for developing ANN model work was also supported by the Higher Education Research Promotion and National Research University (NRU) Project of Thailand, Office of the Higher Input Value range Output Value range Education Commission. MW 100-300 -0.1429 – -0.0618 L*/L0* P
21.33-34.66
a*/a0*
0.0196 – 0.1374
FIR
100-300
b*/b0*
0.0476 – 0.2209
t
30-197
E
3.80 – 13.11
R
43.86-50-59
HH
8.36 – 10.39
G
55.85-69.14
RR
1.14 – 1.51
B
35.80-47.79
SC
0.0185 – 0.0503
Dimension
N/A
Selected reference Mittal GS, Zhang J (2003) Artificial Neural Network-based Psychrometric Predictor. Biosys Eng 85(3): 283-289. Saengrayap R, Tansakul A, Mittal GS. 2015. Effect of far-infrared radiation assisted microwave-vacuum drying on drying characteristics and quality of red chilli. J Food Sci Technol 52(5): 2610-2621.
