Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.1.1. Tuna Processing
2.1.2. Freezing and Thawing Process
2.1.3. Additives and Reagents
2.2. Destructive Analysis: Physicochemical Characterization
2.3. Data Acquisition
2.3.1. Bioelectrical Impedance Analysis (BIA)
2.3.2. Near-Infrared Spectroscopy (NIR)
2.3.3. Time Domain Reflectometry (TDR)
2.4. Data Analysis
2.4.1. Data Cleaning, Data Preprocessing and Principal Component Analysis (PCA)
2.4.2. Comparison between Unfrozen and Frozen-Thawed Samples
2.4.3. Creation and Descriptive Statistics of Calibration and Validation Datasets
2.4.4. Classification Model Building
3. Results
3.1. Differences between Unfrozen and Frozen-Thawed Samples
3.2. Descriptive Statistics of Calibration and Validation Samples
3.3. Models Building
- BIA (Figure 2a,b). Loadings of the first two LVs explain the higher amount of variance (50.46% for latent variable 1 (LV1) and 17.06% for latent variable 2 (LV2)). The loadings of these two LVs reveal that two variables have the higher influence in the model: Pa and Xc.
- NIR (Figure 3). In this case, the LV that retains the higher amount of information is LV2, with an 80.0% explained variance. In this case, an alternating positive and negative pattern is found. Three positive groups of wavelengths are contributing to the model at 980–1100 nm, 1200–1280 nm and 1460–1650 nm, with maximum peaks at 1057 nm, 1224 nm and 1540 nm. The spectral ranges contributing with negative signs are at 1100–1200 nm and 1280–1460 nm, with maximum peaks at 1143 nm and 1388 nm.
- TDR (Figure 4). The loadings of the first LV explain 96.02% of the variance, showing a relevant peak in the region between 0.61 ns and 1.17 ns and a maximum with negative sign at 0.76 ns.
4. Discussion
4.1. Changes during Frozen-Thawed Process
4.2. BIA
4.3. NIR
4.4. TDR
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Unfrozen | Thawed | |||
---|---|---|---|---|
Non-Injected | Injected | Non-Injected | Injected | |
BIA | 120 | 90 | 20 | 70 |
NIR | 960 | 720 | 160 | 560 |
TDR | 30 | 90 | 20 | 70 |
Pre-Processing | LV | Error-Rate | Accuracy | Sensitivity | Specificity | Precision | ||
---|---|---|---|---|---|---|---|---|
Calibration | Unfrozen | Autoscaling | 2 | 0.08 | 0.91 | 0.90 | 0.93 | 0.97 |
Thawed | 0.93 | 0.90 | 0.81 | |||||
CV | Unfrozen | 0.10 | 0.90 | 0.90 | 0.90 | 0.95 | ||
Thawed | 0.90 | 0.90 | 0.80 | |||||
Validation | Unfrozen | 0.14 | 0.88 | 0.91 | 0.81 | 0.93 | ||
Thawed | 0.81 | 0.91 | 0.76 |
Pre-Processing | LV | Error-Rate | Accuracy | Sensitivity | Specificity | Precision | ||
---|---|---|---|---|---|---|---|---|
Calibration | Unfrozen | 1st derivative (order 2, window 5) + Mean Center | 9 | 0.08 | 0.94 | 0.96 | 0.88 | 0.95 |
Thawed | 0.88 | 0.96 | 0.91 | |||||
CV | Unfrozen | 0.08 | 0.94 | 0.96 | 0.88 | 0.95 | ||
Thawed | 0.88 | 0.96 | 0.90 | |||||
Validation | Unfrozen | 0.10 | 0.91 | 0.94 | 0.86 | 0.92 | ||
Thawed | 0.86 | 0.94 | 0.89 |
Pre-Processing | LV | Error-Rate | Accuracy | Sensitivity | Specificity | Precision | ||
---|---|---|---|---|---|---|---|---|
Calibration | Unfrozen | SNV + Mean Center | 8 | 0.04 | 0.96 | 0.97 | 0.96 | 0.97 |
Thawed | 0.96 | 0.97 | 0.96 | |||||
CV | Unfrozen | 0.13 | 0.87 | 0.83 | 0.92 | 0.93 | ||
Thawed | 0.92 | 0.83 | 0.81 | |||||
Validation | Unfrozen | 0.15 | 0.86 | 0.88 | 0.82 | 0.88 | ||
Thawed | 0.82 | 0.88 | 0.82 |
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Nieto-Ortega, S.; Melado-Herreros, Á.; Foti, G.; Olabarrieta, I.; Ramilo-Fernández, G.; Gonzalez Sotelo, C.; Teixeira, B.; Velasco, A.; Mendes, R. Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods 2022, 11, 55. https://doi.org/10.3390/foods11010055
Nieto-Ortega S, Melado-Herreros Á, Foti G, Olabarrieta I, Ramilo-Fernández G, Gonzalez Sotelo C, Teixeira B, Velasco A, Mendes R. Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods. 2022; 11(1):55. https://doi.org/10.3390/foods11010055
Chicago/Turabian StyleNieto-Ortega, Sonia, Ángela Melado-Herreros, Giuseppe Foti, Idoia Olabarrieta, Graciela Ramilo-Fernández, Carmen Gonzalez Sotelo, Bárbara Teixeira, Amaya Velasco, and Rogério Mendes. 2022. "Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)" Foods 11, no. 1: 55. https://doi.org/10.3390/foods11010055
APA StyleNieto-Ortega, S., Melado-Herreros, Á., Foti, G., Olabarrieta, I., Ramilo-Fernández, G., Gonzalez Sotelo, C., Teixeira, B., Velasco, A., & Mendes, R. (2022). Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR). Foods, 11(1), 55. https://doi.org/10.3390/foods11010055