Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
Abstract
1. Introduction
2. Nutritional and Health Challenges of Edible Oils
2.1. Olive Oil: Nutritional and Functional Profile
2.2. Sunflower Seed Oil: Nutritional and Functional Profile
2.3. Tomato Seed Oil: Nutritional and Functional Profile
2.4. Pumpkin Seed Oil: Nutritional and Functional Profile
3. Materials and Methods: Thermographic Signal Acquisition System
4. Post-Processing of IR Signals: Proposed Supervised Learning Model
4.1. Data Assessment
4.2. Proposed Model Architecture
5. Results and Performance Evaluation
5.1. Classification Performance
5.2. Ablation Studies
5.3. Implications and Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Component | Per 100 g | % RDA/AI |
---|---|---|
Energy | 884 kcal | 44% (2000 kcal) |
Total Fat | 100 g | 129% (77 g) |
Saturated Fat | 13.8 g | 69% (20 g max) |
Monounsaturated Fats | 73.0 g | - |
Polyunsaturated Fats | 10.5 g | - |
Sodium | 2 mg | <0.1% (2300 mg) |
Vitamin E | 14 mg | 93% (15 mg) |
Vitamin K | 60.2 μg | 75% (80 μg) |
Polyphenols (approx.) | Variable (50–500 mg) | No RDA |
Component | Per 100 g | % RDA/AI |
---|---|---|
Energy | 884 kcal | 44.2% (2000 kcal) |
Total Fat | 100 g | 153.8% (based on 65 g/day) |
Saturated Fat | 10 g | 50% (20 g/day max) |
Monounsaturated Fats | 20.0 g | - |
Polyunsaturated Fats | 70 g | - |
Sodium | 2 mg | <0.1% (2300 mg) |
Vitamin E | 41 mg | 273% (15 mg RDA) |
Vitamin K | 0.6 mg | 0.5% (120 μg RDA) |
Component | Per 100 g | % RDA/AI |
---|---|---|
Energy | 900 kcal | 45% (2000 kcal) |
Total Fat | 100 g | 154% (based on 65 g/day) |
Saturated Fat | 12 g | 60% |
Monounsaturated Fats | 13 g | - |
Polyunsaturated Fats | 70 g | - |
Omega-3 Fatty Acids | 6 g | 375% |
Omega-6 Fatty Acids | 67 g | 558% |
Phytosterols (total) | 180 mg | 60% |
Lycopene | 1.2 mg | - |
Β-carotene | 0.8 mg | 10% (as vitamin A equivalent) |
Vitamin E (α-tocopherol) | 24 mg | 160% |
Component | Per 100 g | % RDA/AI |
---|---|---|
Energy | 814 kcal | 40.7% |
Total Fat | 90 g | 138.5% |
Saturated Fat | 18 g | 90% |
Monounsaturated Fats | 30 g | - |
Polyunsaturated Fats | 42 g | - |
Phytosterols | 230 mg | - |
Vitamin E | 10 mg | 66.7% |
Sample | Room Temperature (°C) | High Temperature (°C) | Low Temperature (°C) |
---|---|---|---|
Olive oil | 20–24 | 28–73 | −17–16 |
Sunflower seed oil | 22–26 | 28–71 | −14–16.5 |
Tomato seed oil | 22–26 | 30–65 | −14–15.3 |
Pumpkin seed oil | 22–26 | 30–51 | −13.5–15.7 |
Layer Number | Layer Name | Input Shape | Output Shape | Kernel Size | Stride |
---|---|---|---|---|---|
1 | Conv2D + ReLU | (128, 128, 1) | (126, 126, 32) | (3, 3) | (1, 1) |
2 | MaxPooling2D | (126, 126, 32) | (63, 63, 32) | (2, 2) | (2, 2) |
3 | Conv2D + ReLU | (63, 63, 32) | (61, 61, 64) | (3, 3) | (1, 1) |
4 | MaxPooling2D | (61, 61, 64) | (30, 30, 64) | (2, 2) | (2, 2) |
5 | Flatten | (30, 30, 64) | (57,600) | – 1 | – |
Layer Number | Layer Name | Input Shape | Output Shape | Notes |
---|---|---|---|---|
6 | LSTM | (Num frames, 57,600) | (64) | Captures temporal dynamics |
7 | Dense + ReLU | (64) | (128) | Fully connected |
8 | Dropout (rate = 0.3) | (128) | (128) | Regularisation |
9 | Dense + SoftMax | (128) | (4) | 4-class oil classification |
Metric | Mean (%) | Std. Dev (%) |
---|---|---|
Accuracy | 93.25 | 1.45 |
Precision | 94.55 | 1.22 |
Recall | 92.80 | 1.78 |
F1-score | 93.66 | 1.60 |
Model Variant | Accuracy | Precision | Recall |
---|---|---|---|
CNN-only (spatial features only) | 85.23% | 86.14% | 84.03% |
LSTM-only (temporal only) | 70.11% | 70.24% | 70.48% |
CNN-LSTM (proposed model) | 93.25% | 94.55% | 92.80% |
CNN-LSTM w/o dropout | 90.22% | 90.14% | 88.78% |
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Pratticò, D.; Laganà, F. Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment. Signals 2025, 6, 38. https://doi.org/10.3390/signals6030038
Pratticò D, Laganà F. Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment. Signals. 2025; 6(3):38. https://doi.org/10.3390/signals6030038
Chicago/Turabian StylePratticò, Danilo, and Filippo Laganà. 2025. "Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment" Signals 6, no. 3: 38. https://doi.org/10.3390/signals6030038
APA StylePratticò, D., & Laganà, F. (2025). Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment. Signals, 6(3), 38. https://doi.org/10.3390/signals6030038