Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.1.1. Destructive Analyses
2.1.2. Imaging System
2.2. Data Processing
2.2.1. Stage 1: Determining Landmarks
- The central axis is detected to take into account orientation changes.
- The center of the eye is located by the previously explained techniques.
- An imaginary profile AB, perpendicular to the central axis, is calculated that crosses the fish, as depicted in Figure 4.
- The AC profile is found because AC is shorter than CB.
- Highest point H is found.
- The AC profile is projected in the direction of the central axis as a distance defined by H to obtain the A’C’ profile.
- A region around the A’C’ profile is the opercular spine region.
2.2.2. Stage 2: Freshness Model
2.3. Statistical Validation
3. Results and Discussion
3.1. Chemical and Microbiological Results
3.2. Image Analysis
3.2.1. Stage 1: Landmarks Determination
3.2.2. Stage 2: Freshness Evaluation
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Storage Time (Days) | RI | TVB-N | pH | Enterobacteriaceae (log cfu) | Mesophilic (log cfu) |
---|---|---|---|---|---|
0 | 1.3348 ± 0.0003 a | 18.14 ± 2.25 a | 6.28 ± 0.18 a | <1 | 0.37 ± 0.74 |
1 | 1.3350 ± 0.0002 a | 18.57 ± 0.61 a | 6.11 ± 0.04 ab | ||
3 | 1.3352 ± 0.0006 a | 19.47 ± 1.79 a | 6.22 ± 0.108 ab | ||
6 | 1.3371 ± 0.0010 b | 28.06 ± 1.29 b | 6.23 ± 0.09 b | 3.30 ± 0.22 | 5.02 ± 0.24 |
Iris | Pupil | Pupil & Iris | Opercular Spine | |||
---|---|---|---|---|---|---|
Whole Spectrum | I-PLS Selection | Whole Spectrum | I-PLS Selection | Whole Spectrum | Whole Spectrum | |
Num. LVs | 7 | 3 | 7 | 3 | 5 | 6 |
RMSEC (days) | 0.805 | 1.041 | 0.604 | 0.908 | 1.174 | 0.662 |
RMSECV (days) | 0.968 | 1.071 | 0.712 | 0.941 | 1.212 | 0.803 |
RMSEPred (days) | 0.882 | 0.971 | 0.651 | 0.846 | 1.253 | 0.783 |
R2 Cal | 0.87 | 0.79 | 0.93 | 0.84 | 0.74 | 0.566 |
R2 CV | 0.82 | 0.77 | 0.90 | 0.82 | 0.72 | 0.391 |
R2 Pred | 0.86 | 0.83 | 0.92 | 0.87 | 0.7 | 0.413 |
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Ivorra, E.; Verdu, S.; Sánchez, A.J.; Grau, R.; Barat, J.M. Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis. Sensors 2016, 16, 1735. https://doi.org/10.3390/s16101735
Ivorra E, Verdu S, Sánchez AJ, Grau R, Barat JM. Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis. Sensors. 2016; 16(10):1735. https://doi.org/10.3390/s16101735
Chicago/Turabian StyleIvorra, Eugenio, Samuel Verdu, Antonio J. Sánchez, Raúl Grau, and José M. Barat. 2016. "Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis" Sensors 16, no. 10: 1735. https://doi.org/10.3390/s16101735
APA StyleIvorra, E., Verdu, S., Sánchez, A. J., Grau, R., & Barat, J. M. (2016). Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis. Sensors, 16(10), 1735. https://doi.org/10.3390/s16101735