Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres
Abstract
:1. Introduction
- Investigate the influence of MAP, temperature, and storage days on the shelf life and quality parameters of dates.
- Evaluate the quality parameters and shelf life of dates during storage under storge conditions.
- Develop a low-cost and fast inference multispectral sensor-based detector for reflectance spectroscopy data acquisition in dates during storage.
- Develop predictive models, using Edge Impulse to predict the quality and shelf life of packaged dates, based on reflectance spectroscopy.
- Validate the performance of the developed TinyML models for predicting packaged fruits’ shelf life and quality under different storage conditions.
2. Materials and Methods
2.1. Collection and Preparation of the Samples
- Unsealed trays in the natural atmosphere (Control).
- Vacuum-sealed bags (VSBs).
- Modified atmosphere packing trays with 20% CO2 and N balance (MAP1).
- Modified atmosphere packing trays with 10% O2, 20% CO2, and N balance (MAP2).
2.2. Quality Parameter Measurments
2.3. Multispectral Sensor Description
2.4. TinyML Prediction Models
2.5. Statistical Analysis and TinyMl Models Evaluation
3. Results
3.1. Influence of Storage Conditions on Fresh Dates
3.2. Quality Parameters of Dates
3.3. Spectral Reflectance Data
3.4. Correlation between Quality and Spectroscopy Data
3.5. TinyML Prediction Model Evaluation
4. Discussion
4.1. Influence of Storage Conditions on Fresh Dates
4.2. Non-Destructive Estimation of Shelf Life and Quality of Dates
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Specifications | |
---|---|---|
Prediction of the Parameters under Each Packaging and Temperature Condition | Prediction of the Quality Parameters of the Packaged Fruits | |
Model type | Sequential | |
Input layer | 18 inputs: As72651, As72652, and AS72653 sensors data (A, B, C, D, E, F, G, H, I, J, K, L, R, S, T, U, V, and W) | 20 inputs: As72651, As72652, and AS72653 sensors data (18) + Packaging types (1) + Storage temperatures (1) |
Hidden layer 1 | 18 nodes | 20 nodes |
Hidden layer 2 | 10 nodes | 12 nodes |
Dropout rate | 0.2 | |
Output layer | 1 node (Y-Predicted shelf life or one target quality parameter under each treatment) | 1 node (Y-Predicted one target quality parameter or overall shelf life) |
Activation function | ReLu | |
Size of the Batch | 32 | |
Epochs numbers | 10 | |
Optimization | Adam | |
Loss function | MSE | |
Cycles of training | 2000 | |
Learning rate | 0.005 | |
Valid Dataset | 70 | 168 |
Dataset—Training (80%) | 56 | 135 |
Dataset—Testing (20%) | 14 | 33 |
Validation dataset | 168 | 168 |
Ripening Stage | Quality Attributes | ||||
---|---|---|---|---|---|
pH | TSSs (Brix) | Sugar (%) | MC (%) | Tannin (%) | |
Fresh (Khalal) | 5.46 ± 0.43 C | 36.45 ± 5.23 C | 34.95 ± 5.56 C | 59.23 ± 6.42 A | 1.88 ± 0.83 A |
Rutab | 6.15 ± 0.35 B | 48.06 ± 5.81 B | 46.83 ± 5.74 B | 42.3 ± 8.26 B | 0.41 ± 0.51 B |
Tamr | 6.59 ± 0.21 A | 62.27 ± 6.42 A | 61.28 ± 4.58 A | 22.48 ± 3.61 C | 0.08 ± 0.11 C |
Total | 5.87 ± 0.54 | 43.93 ± 9.74 | 42.6 ± 9.82 | 48.39 ± 13.51 | 1.06 ± 1.13 |
Sensors | Wavelength (λ) | Ripening Stage | ||
---|---|---|---|---|
Fresh | Rutab | Tamr | ||
AS72653 | A: 410 nm | 1158.5 ± 99.5 a | 981.6 ± 80.8 b | 810 ± 40.1 c |
B: 435 nm | 595.2 ± 76.1 a | 435.9 ± 77.5 b | 308.8 ± 15.2 c | |
C: 460 nm | 1354.3 ± 205 a | 981.6 ± 208.5 b | 715.2 ± 60.5 c | |
D: 485 nm | 584.7 ± 95.6 a | 392.9 ± 88.5 b | 263.4 ± 91.8 bc | |
E: 510 nm | 1474.3 ± 280.2 a | 883.9 ± 339.2 b | 370.6 ± 18.5 c | |
F: 535 nm | 3348.5 ± 555.9 a | 1956 ± 833.6 b | 563.6 ± 49.6 c | |
AS72652 | G: 560 nm | 2162.4 ± 326.9 a | 1419.8 ± 463.6 b | 683.8 ± 136.4 c |
H: 585 nm | 2644.1 ± 354.3 a | 1715.1 ± 561.2 b | 684.7 ± 65.5 c | |
I: 645 nm | 1944.1 ± 215.1 a | 1282.6 ± 396.6 b | 479.6 ± 41.4 c | |
J: 705 nm | 731.6 ± 60.2 a | 516.5 ± 155.9 b | 197.2 ± 20.8 c | |
K: 900 nm | 1139.3 ± 122.5 a | 779.4 ± 219.6 b | 347.8 ± 42.1 c | |
L: 940 nm | 1089.8 ± 86.1 a | 758.1 ± 143.3 b | 379.4 ± 47.1 c | |
AS72651 | R: 610 nm | 4407.7 ± 799.6 a | 2937.6 ± 131.1 b | 1012.4 ± 170.7 c |
S: 680 nm | 1115.5 ± 119.3 a | 865.3 ± 193.5 b | 473.4 ± 55.6 c | |
T: 730 nm | 528.3 ± 55.5 a | 388.4 ± 111.1 b | 170.3 ± 107.3 bc | |
U: 760 nm | 308.7 ± 63.5 a | 239.2 ± 61.9 a | 124.2 ± 22.33 c | |
V: 810 nm | 862.7 ± 93.5 a | 678.5 ± 187.7 b | 345.2 ± 56.56 c | |
W: 860 nm | 1167.7 ± 114.4 a | 1003.1 ± 199.1 b | 650.2 ± 87.5 c |
Wavelength (λ) | SL | pH | TSSs | SC | MC | TC |
---|---|---|---|---|---|---|
SL | 1 | −0.517 ** | −0.680 ** | −0.704 ** | 0.710 ** | 0.857 ** |
pH | −0.517 ** | 1 | 0.820 ** | 0.819 ** | −0.400 ** | −0.527 ** |
TSSs | −0.680 ** | 0.820 ** | 1 | 0.995 ** | −0.764 ** | −0.767 ** |
SC | −0.704 ** | 0.819 ** | 0.995 ** | 1 | −0.783 ** | −0.784 ** |
MC | 0.710 ** | −0.400 ** | −0.764 ** | −0.783 ** | 1 | 0.826 ** |
TC | 0.857 ** | −0.527 ** | −0.767 ** | −0.784 ** | 0.826 ** | 1 |
A: 410 nm | 0.718 ** | −0.166 * | −0.553 ** | −0.578 ** | 0.891 ** | 0.773 ** |
B: 435 nm | 0.743 ** | −0.361 ** | −0.682 ** | −0.701 ** | 0.904 ** | 0.802 ** |
C: 460 nm | 0.747 ** | −0.359 ** | −0.653 ** | −0.671 ** | 0.865 ** | 0.791 ** |
D: 485 nm | 0.799 ** | −0.409 ** | −0.704 ** | −0.728 ** | 0.895 ** | 0.832 ** |
E: 510 nm | 0.740 ** | −0.454 ** | −0.723 ** | −0.746 ** | 0.885 ** | 0.789 ** |
F: 535 nm | 0.663 ** | −0.453 ** | −0.728 ** | −0.748 ** | 0.875 ** | 0.741 ** |
G: 560 nm | 0.672 ** | −0.397 ** | −0.680 ** | −0.699 ** | 0.872 ** | 0.733 ** |
H: 585 nm | 0.680 ** | −0.410 ** | −0.707 ** | −0.727 ** | 0.899 ** | 0.747 ** |
I: 645 nm | 0.678 ** | −0.408 ** | −0.721 ** | −0.742 ** | 0.916 ** | 0.751 ** |
J: 705 nm | 0.583 ** | −0.357 ** | −0.668 ** | −0.689 ** | 0.883 ** | 0.671 ** |
K: 900 nm | 0.681 ** | −0.391 ** | −0.703 ** | −0.725 ** | 0.911 ** | 0.744 ** |
L: 940 nm | 0.707 ** | −0.377 ** | −0.698 ** | −0.721 ** | 0.921 ** | 0.767 ** |
R: 610 nm | 0.630 ** | −0.390 ** | −0.686 ** | −0.711 ** | 0.862 ** | 0.717 ** |
S: 680 nm | 0.575 ** | −0.296 ** | −0.626 ** | −0.650 ** | 0.844 ** | 0.675 ** |
T: 730 nm | 0.597 ** | −0.344 ** | −0.662 ** | −0.684 ** | 0.874 ** | 0.685 ** |
U: 760 nm | 0.576 ** | −0.301 ** | −0.622 ** | −0.645 ** | 0.849 ** | 0.662 ** |
V: 810 nm | 0.542 ** | −0.276 ** | −0.588 ** | −0.614 ** | 0.818 ** | 0.620 ** |
W: 860 nm | 0.487 ** | −0.158 * | −0.478 ** | −0.508 ** | 0.742 ** | 0.546 ** |
Packaging Type | Temperature | Evaluation Metrics | Parameters | |||||
---|---|---|---|---|---|---|---|---|
Shelf Life Period | pH | TSS | Sugar | Moisture | Tannin | |||
Control | 5 °C | R2 | 0.964 | 0.886 | 0.909 | 0.933 | 0.933 | 0.952 |
MAE | 0.911 | 0.151 | 3.291 | 2.543 | 2.733 | 0.255 | ||
RMSE | 1.336 | 0.166 | 3.925 | 3.381 | 4.121 | 0.349 | ||
22 °C | R2 | 0.925 | 0.741 | 0.914 | 0.912 | 0.973 | 0.922 | |
MAE | 1.076 | 0.171 | 2.962 | 3.142 | 1.842 | 0.219 | ||
RMSE | 1.487 | 0.232 | 3.803 | 3.841 | 2.157 | 0.305 | ||
VSBs | 5 °C | R2 | 0.975 | 0.782 | 0.798 | 0.705 | 0.884 | 0.956 |
MAE | 2.054 | 0.172 | 2.771 | 3.418 | 2.048 | 0.209 | ||
RMSE | 2.587 | 0.208 | 3.718 | 4.575 | 3.267 | 0.252 | ||
22 °C | R2 | 0.978 | 0.908 | 0.922 | 0.754 | 0.939 | 0.925 | |
MAE | 1.622 | 0.125 | 2.172 | 3.146 | 2.127 | 0.259 | ||
RMSE | 1.942 | 0.155 | 2.691 | 4.026 | 2.707 | 0.334 | ||
MAP1 | 5 °C | R2 | 0.976 | 0.851 | 0.719 | 0.723 | 0.934 | 0.869 |
MAE | 1.259 | 0.135 | 2.863 | 3.476 | 1.902 | 0.285 | ||
RMSE | 1.571 | 0.182 | 4.044 | 3.997 | 2.842 | 0.383 | ||
22 °C | R2 | 0.972 | 0.685 | 0.883 | 0.848 | 0.869 | 0.929 | |
MAE | 1.512 | 0.235 | 2.383 | 2.511 | 3.573 | 0.251 | ||
RMSE | 2.187 | 0.275 | 2.944 | 3.349 | 4.979 | 0.323 | ||
MAP2 | 5 °C | R2 | 0.979 | 0.965 | 0.975 | 0.957 | 0.973 | 0.903 |
MAE | 1.348 | 0.088 | 1.281 | 1.863 | 1.351 | 0.257 | ||
RMSE | 1.555 | 0.115 | 1.621 | 2.092 | 1.773 | 0.323 | ||
22 °C | R2 | 0.966 | 0.751 | 0.909 | 0.946 | 0.961 | 0.937 | |
MAE | 1.371 | 0.168 | 2.726 | 1.944 | 2.124 | 0.221 | ||
RMSE | 1.785 | 0.192 | 3.403 | 2.798 | 2.879 | 0.286 |
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Mohammed, M.; Srinivasagan, R.; Alzahrani, A.; Alqahtani, N.K. Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres. Sustainability 2023, 15, 12871. https://doi.org/10.3390/su151712871
Mohammed M, Srinivasagan R, Alzahrani A, Alqahtani NK. Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres. Sustainability. 2023; 15(17):12871. https://doi.org/10.3390/su151712871
Chicago/Turabian StyleMohammed, Maged, Ramasamy Srinivasagan, Ali Alzahrani, and Nashi K. Alqahtani. 2023. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres" Sustainability 15, no. 17: 12871. https://doi.org/10.3390/su151712871