NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”
Abstract
:1. Introduction
2. Materials and Methods
2.1. Samples
2.2. Sensory Analysis
2.3. Near Infrared (NIR) Spectroscopy
2.4. Discriminant Analysis
2.5. Artificial Neural Network for Predicting Sensory Parameters
3. Results and Discussion
3.1. Sensory Data
3.2. Discrimination of the Samples According to Protected Geographical Indication (PGI) Quality Label
3.3. Prediction of the Sensory Parameters of Cecina
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Mean | Minimum | Maximum | SD | |
---|---|---|---|---|
Appearance | ||||
Veined | 4.11 | 1.86 | 8.71 | 1.68 |
Fat color | 5.97 | 4.43 | 7.50 | 0.59 |
Color intensity | 5.78 | 3.57 | 8.00 | 1.09 |
Exudate | 3.11 | 1.29 | 7.71 | 1.15 |
White spots | 1.43 | 1.00 | 7.57 | 1.14 |
Flavor | ||||
Odor intensity | 5.83 | 4.14 | 7.00 | 0.56 |
Cured odor | 5.39 | 3.67 | 6.71 | 0.67 |
Smoked odor | 4.90 | 3.14 | 7.00 | 0.72 |
Rancid odor | 1.36 | 1.00 | 3.00 | 0.40 |
Moldy odor | 1.11 | 1.00 | 2.33 | 0.23 |
Flavor intensity | 6.14 | 4.00 | 7.14 | 0.62 |
Cured flavor | 5.56 | 3.67 | 6.86 | 0.74 |
Saltiness | 4.35 | 3.33 | 5.14 | 0.42 |
Sweetness | 1.54 | 1.00 | 2.29 | 0.30 |
Smoked flavor | 4.49 | 2.33 | 5.86 | 0.71 |
Rancidity | 1.61 | 1.00 | 3.83 | 0.57 |
Pungency | 1.38 | 1.00 | 2.00 | 0.25 |
Aftertaste | 5.42 | 3.29 | 6.71 | 0.58 |
Texture | ||||
Hardness | 3.88 | 2.33 | 6.43 | 0.94 |
Juiciness | 4.49 | 2.33 | 6.00 | 0.81 |
Fatness | 3.17 | 1.50 | 6.29 | 0.96 |
Fibrousness | 3.11 | 1.71 | 5.29 | 0.78 |
Chewiness | 3.60 | 2.29 | 5.43 | 0.84 |
Gumminess | 2.63 | 1.67 | 4.50 | 0.70 |
None 2,4,4,1 | None 2,10,10,1 | SNV 1,4,4,1 | Detrend 1,4,4,1 | Detrend 2,10,10,1 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
PGI | Not PGI | PGI | Not PGI | PGI | Not PGI | PGI | Non-PGI | PGI | Not PGI | |
PGI | 18 | 7 | 21 | 4 | 22 | 3 | 18 | 7 | 21 | 4 |
Non-PGI | 0 | 25 | 0 | 25 | 3 | 22 | 2 | 23 | 0 | 25 |
Hit rate | 72% | 100% | 84% | 100% | 88% | 88% | 72% | 92% | 84% | 100% |
Neurons | Percentage of Samples Correctly Classified | ||||
---|---|---|---|---|---|
Training Set | Validation Set | Test Set | Total | ||
Gradient Descent | 27 | 95.6 | 85.7 | 85.7 | 92.7 |
Gradient Descent with Adaptive Learning Rate | 30 | 98.5 | 85.7 | 85.7 | 94.8 |
Gradient Descent with Momentum | 9 | 89.7 | 100 | 85.7 | 90.6 |
Gradient Descent with Momentum and Adaptive Learning Rate | 19 | 98.5 | 85.7 | 100 | 96.9 |
Scaled Conjugate Gradient | 29 | 98.5 | 100 | 100 | 98.9 |
Conjugate Gradient with Powell-Beale | 10 | 100 | 100 | 92.8 | 98.9 |
Conjugate Gradient with Fletcher-Reeves | 18 | 98.5 | 100 | 85.7 | 96.9 |
Conjugate Gradient with Polak-Ribiere | 7 | 98.5 | 100 | 100 | 98.9 |
Levenberg-Marquardt | 13 | 100 | 100 | 100 | 100 |
Neurons | RSQ | MSEP | |
---|---|---|---|
Appearance | |||
Veined | 15 | 0.90 | 0.293 |
Fat color | 18 | 0.84 | 0.054 |
Color intensity | 8 | 0.89 | 0.135 |
Exudate | 13 | 0.87 | 0.190 |
White dots | 1 | 0.99 | 0.008 |
Flavor | |||
Odor intensity | 9 | 0.65 | 0.133 |
Cured odor | 14 | 0.87 | 0.066 |
Smoked odor | 25 | 0.73 | 0.183 |
Rancid odor | 9 | 0.84 | 0.025 |
Moldy odor | 6 | 0.91 | 0.005 |
Flavor intensity | 22 | 0.80 | 0.097 |
Cured flavor | 14 | 0.81 | 0.108 |
Saltiness | 7 | 0.83 | 0.037 |
Sweetness | 6 | 0.83 | 0.014 |
Smoked flavor | 8 | 0.81 | 0.101 |
Rancidity | 25 | 0.87 | 0.044 |
Pungency | 25 | 0.79 | 0.013 |
Aftertaste | 12 | 0.88 | 0.042 |
Texture | |||
Hardness | 13 | 0.90 | 0.090 |
Juiciness | 24 | 0.95 | 0.036 |
Fatness | 19 | 0.90 | 0.101 |
Fibrousness | 9 | 0.88 | 0.067 |
Chewiness | 18 | 0.92 | 0.050 |
Gumminess | 15 | 0.93 | 0.033 |
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Revilla, I.; Vivar-Quintana, A.M.; González-Martín, M.I.; Hernández-Jiménez, M.; Martínez-Martín, I.; Hernández-Ramos, P. NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”. Sensors 2020, 20, 6892. https://doi.org/10.3390/s20236892
Revilla I, Vivar-Quintana AM, González-Martín MI, Hernández-Jiménez M, Martínez-Martín I, Hernández-Ramos P. NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”. Sensors. 2020; 20(23):6892. https://doi.org/10.3390/s20236892
Chicago/Turabian StyleRevilla, Isabel, Ana M. Vivar-Quintana, María Inmaculada González-Martín, Miriam Hernández-Jiménez, Iván Martínez-Martín, and Pedro Hernández-Ramos. 2020. "NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef “Cecina”" Sensors 20, no. 23: 6892. https://doi.org/10.3390/s20236892