Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparation of Samples
2.2. Convection Drying
2.3. Acoustic Signal Acquisition
2.4. Texture Analysis of Dried Strawberry Fruit
2.5. Structure of Training Sets
2.6. Preparation of Artificial Neural Networks
3. Results and Discussion
3.1. Classification
3.2. Validating the Artificial Neural Networks
3.3. Texture Analysis
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Quality Ratio | 1 Point | 2 Points | 3 Points | 4 Points | 5 Points |
---|---|---|---|---|---|
Looks | Very soft fruit with numerous stewed areas on the surface | Soft fruit with visible stewed areas on the surface | Firm fruit without deformations with light stewed areas on the surface | Firm fruit without deformations, lack of visible stewed areas on the surface | Hard fruit |
Color | Red, numerous dark red marks | Red with dark red marks | Dark red | Red | Light red |
Taste | Strongly perceptible alcohol aftertaste | Sweet with detectable alcohol aftertaste | Characteristic of strawberry, intense sweet | Characteristic of strawberry, sweet | Characteristic of strawberry, sweet and sour |
Name | Z1 | Z2 | Z3 |
---|---|---|---|
Model ANN | MLP 2:2-6-1:1 | MLP 2:2-14-1:1 | MLP 2:2-4-1:1 |
Training error | 0.14 | 0.01 | 0.32 |
Validation error | 0.04 | 0.09 | 0.29 |
Testing error | 0.32 | 0.18 | 0.45 |
Quality of learning | 0.98 | 0.98 | 0.85 |
Quality of validation | 0.99 | 0.99 | 0.86 |
Quality of testing | 0.90 | 0.96 | 0.76 |
Learning cases | 120 | 40 | 40 |
Training algorithm | BP50, CG134b | BP50, CG19b | BP06 |
Name | Model ANN | MSE | RMSE | MAD | MAPE |
---|---|---|---|---|---|
Z1 | MLP 2:2-6-1:1 | 0.03 | 0.16 | 0.36 | 20.55 |
Z2 | MLP 2:2-14-1:1 | 0.01 | 0.09 | 0.19 | 10.39 |
Z3 | MLP 2:2-4-1:1 | 0.12 | 0.35 | 0.71 | 5.57 |
No. | Ripe Class (N) | Over-Ripe Class (N) |
---|---|---|
1 | 13.1 | 10.1 |
2 | 15.6 | 12.4 |
3 | 16 | 9.9 |
4 | 13.6 | 8.8 |
5 | 18.2 | 7.3 |
6 | 15.6 | 7.6 |
7 | 12.3 | 9.1 |
8 | 15.4 | 8.7 |
9 | 12.3 | 8.3 |
10 | 15.4 | 6.9 |
11 | 17.8 | 7.1 |
12 | 12.9 | 7.1 |
13 | 14.4 | 10.1 |
14 | 14.6 | 7.3 |
15 | 12.5 | 8.6 |
16 | 14.6 | 6.8 |
17 | 18.3 | 6.9 |
18 | 14.8 | 10.1 |
19 | 12.9 | 10.9 |
20 | 14.6 | 9.3 |
Mean | 15 | 8.7 |
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Przybył, K.; Duda, A.; Koszela, K.; Stangierski, J.; Polarczyk, M.; Gierz, Ł. Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks. Sensors 2020, 20, 499. https://doi.org/10.3390/s20020499
Przybył K, Duda A, Koszela K, Stangierski J, Polarczyk M, Gierz Ł. Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks. Sensors. 2020; 20(2):499. https://doi.org/10.3390/s20020499
Chicago/Turabian StylePrzybył, Krzysztof, Adamina Duda, Krzysztof Koszela, Jerzy Stangierski, Mariusz Polarczyk, and Łukasz Gierz. 2020. "Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks" Sensors 20, no. 2: 499. https://doi.org/10.3390/s20020499
APA StylePrzybył, K., Duda, A., Koszela, K., Stangierski, J., Polarczyk, M., & Gierz, Ł. (2020). Classification of Dried Strawberry by the Analysis of the Acoustic Sound with Artificial Neural Networks. Sensors, 20(2), 499. https://doi.org/10.3390/s20020499