Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration
Abstract
:1. Introduction
2. Materials and Methods
2.1. Determination of Optimal Dilution
2.2. Determination of Optimal Extraction Method
2.2.1. Raw Meat Extraction with Distilled Water
2.2.2. Meat Extraction by Cooking with Distilled Water
2.2.3. Frozen Meat Extraction with Distilled Water
2.3. Electronic Tongue Measurements
2.4. Data Analysis
2.4.1. Classification of Meat Mixtures with Linear Discriminant Analysis
2.4.2. Partial Least Squares Regression
3. Results
3.1. Determination of Optimal Dilution
3.1.1. Linear Discriminant Analysis (LDA) Models Developed for the Determination of Optimal Dilution
3.1.2. Partial Least Squares (PLS) Regression Models Developed for the Determination of Optimal Dilution
3.2. Optimal Extraction Method
3.2.1. Results of Sensor Optimization for LDA Analysis in the Optimal Extraction Method
3.2.2. LDA Analysis for Raw Meat Extraction with Distilled Water in the Determination of Optimal Extraction
3.2.3. LDA Analysis for Meat Extraction by Cooking with Distilled Water for the Determination of Optimal Extraction
3.2.4. LDA Analysis for Frozen Meat Extraction with Distilled Water for the Determination of Optimal Extraction
3.2.5. Partial Least Squares (PLS) Models to Regress on the Concentrations of Adulterated Poultry and Red Meat for the Determination of Optimal Extraction
3.2.6. PLS Models to Regress on the Concentrations of Chicken in Turkey Using all the Extraction Methods
3.2.7. PLS Models to Regress on the Concentrations of Pork in Beef Using all the Extraction Methods
4. Discussion
4.1. Determination of Optimal Optimal Dilution
4.2. Determination of Optimal Extraction Method
4.2.1. LDA Sensor Optimization for the Determination of Optimal Extraction Method
4.2.2. Raw Meat Extraction with Distilled Water
4.2.3. Meat Extraction by Cooking with Distilled Water
4.2.4. Frozen Meat Extraction with Distilled Water
4.2.5. PLS Sensor Optimization for the Determination of Optimal Extraction Method
4.2.6. PLS Regression Models for Predicting Concentrations of Chicken in Turkey and Pork in Beef Using all the Three Extraction Methods
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sample ID | Turkey (% w/w) | Chicken (% w/w) | Turkey (g) | Chicken (g) | % w/v of Meat Mixture |
---|---|---|---|---|---|
05_100 | 100 | 0 | 20.00 | 0.00 | 0.50 |
05_097 | 97 | 3 | 19.40 | 0.60 | 0.50 |
05_095 | 95 | 5 | 19.00 | 1.00 | 0.50 |
05_090 | 90 | 10 | 18.00 | 2.00 | 0.50 |
10_100 | 100 | 0 | 20.00 | 0.00 | 1.00 |
10_097 | 97 | 3 | 19.40 | 0.60 | 1.00 |
10_095 | 95 | 5 | 19.00 | 1.00 | 1.00 |
10_090 | 90 | 10 | 18.00 | 2.00 | 1.00 |
20_100 | 100 | 0 | 20.00 | 0.00 | 2.00 |
20_097 | 97 | 3 | 19.40 | 0.60 | 2.00 |
20_095 | 95 | 5 | 19.00 | 1.00 | 2.00 |
20_090 | 90 | 10 | 18.00 | 2.00 | 2.00 |
Sample ID | Turkey (%) | Chicken (%) | Turkey (g) | Chicken (g) |
---|---|---|---|---|
T100 | 100 | 0 | 20.00 | 0.00 |
T099 | 99 | 1 | 19.80 | 0.20 |
T097 | 97 | 3 | 19.40 | 0.60 |
T095 | 95 | 5 | 19.00 | 1.00 |
T090 | 90 | 10 | 18.00 | 2.00 |
T080 | 80 | 20 | 16.00 | 4.00 |
Sample ID | Beef (%) | Pork (%) | Beef (g) | Pork (g) |
---|---|---|---|---|
B100 | 100 | 0 | 20.00 | 0.00 |
B099 | 99 | 1 | 19.80 | 0.20 |
B097 | 97 | 3 | 19.40 | 0.60 |
B095 | 95 | 5 | 19.00 | 1.00 |
B090 | 90 | 10 | 18.00 | 2.00 |
B080 | 80 | 20 | 16.00 | 4.00 |
Dilution Level | LV | R2 | RMSEC (w/v Meat Mixture) | R2CV | RMSECV (w/v Meat Mixture) |
---|---|---|---|---|---|
Dilution level 1 (2% w/v turkey) | 3 | 0.88 | 1.26 | 0.81 | 1.57 |
Dilution level 2 (1% w/v turkey) | 3 | 0.97 | 0.59 | 0.95 | 0.80 |
Dilution level 3 (0.5% w/v turkey) | 1 | 0.71 | 1.96 | 0.65 | 2.14 |
Meat Combination | Sample Preparation | Selected Sensors | Omitted Sensors | Initial Cross-Validation Accuracies (%) | Optimized Cross-Validation Accuracies (%) |
---|---|---|---|---|---|
Chicken and turkey adulteration | Raw meat extraction with distilled water | HA, BB, ZZ, GA | JE, CA, JB | 47.99 | 58.35 |
Meat extraction by cooking with distilled water | BB, ZZ, GA, JB | HA, JE, CA | 54.14 | 64.72 | |
Frozen meat extraction with distilled water | All: HA, BB, ZZ, GA, JE, JB, CA | None | 62.55 | 62.55 | |
Pork and beef adulteration | Raw meat extraction with distilled water | HA, ZZ, GA, JB | BB, CA, JE | 45.90 | 54.25 |
Meat extraction by cooking with distilled water | HA, ZZ, GA CA, JE, JB | BB | 58.37 | 68.77 | |
Frozen meat extraction with distilled water | HA, ZZ, BB, GA, JE, JB | CA | 52.11 | 56.41 |
Average Accuracies | T080 | T090 | T095 | T097 | T099 | T100 | |
---|---|---|---|---|---|---|---|
Recognition 81.28% | T080 | 100 | 0 | 0 | 0 | 0 | 0 |
T090 | 0 | 93.81 | 18.73 | 24.95 | 0 | 0 | |
T095 | 0 | 0 | 56.18 | 0 | 12.57 | 6.19 | |
T097 | 0 | 6.19 | 0 | 68.86 | 6.19 | 0 | |
T099 | 0 | 0 | 12.55 | 6.19 | 75.05 | 0 | |
T100 | 0 | 0 | 12.55 | 0 | 6.19 | 93.81 | |
Cross-validation 58.35% | T080 | 100 | 0 | 0 | 0 | 0 | 0 |
T090 | 0 | 87.59 | 0 | 49.81 | 0 | 0 | |
T095 | 0 | 0 | 37.45 | 0 | 25.09 | 12.41 | |
T097 | 0 | 12.41 | 12.36 | 25.09 | 37.45 | 0 | |
T099 | 0 | 0 | 25.09 | 25.09 | 12.36 | 0 | |
T100 | 0 | 0 | 25.09 | 0 | 25.09 | 87.59 |
Average Accuracies | B080 | B090 | B095 | B097 | B099 | B100 | |
---|---|---|---|---|---|---|---|
Recognition 67.73% | B080 | 75.05 | 6.19 | 0 | 24.95 | 12.57 | 0 |
B090 | 6.19 | 75.05 | 0 | 0 | 0 | 0 | |
B095 | 0 | 12.57 | 75.05 | 0 | 18.76 | 0 | |
B097 | 18.76 | 6.19 | 6.19 | 56.29 | 6.19 | 0 | |
B099 | 0 | 0 | 18.76 | 12.57 | 43.71 | 18.76 | |
B100 | 0 | 0 | 0 | 6.19 | 18.76 | 81.24 | |
Cross-validation 54.25% | B080 | 62.78 | 12.41 | 0 | 25.19 | 25.09 | 0 |
B090 | 12.41 | 62.78 | 0 | 12.41 | 0 | 0 | |
B095 | 0 | 12.41 | 50 | 0 | 12.36 | 0 | |
B097 | 12.41 | 12.41 | 12.41 | 50 | 0 | 0 | |
B099 | 12.41 | 0 | 37.59 | 0 | 37.45 | 37.45 | |
B100 | 0 | 0 | 0 | 12.41 | 25.09 | 62.55 |
Average Accuracies | T080 | T090 | T095 | T097 | T099 | T100 | |
---|---|---|---|---|---|---|---|
Recognition 78.13% | T080 | 74.91 | 0 | 0 | 0 | 6.19 | 0 |
T090 | 12.55 | 87.62 | 0 | 0 | 6.19 | 12.55 | |
T095 | 0 | 0 | 75.05 | 0 | 0 | 12.55 | |
T097 | 0 | 0 | 0 | 100 | 0 | 0 | |
T099 | 12.55 | 6.19 | 0 | 0 | 68.86 | 12.55 | |
T100 | 0 | 6.19 | 24.95 | 0 | 18.76 | 62.36 | |
Cross-validation 64.72% | T080 | 75.19 | 0 | 0 | 0 | 12.36 | 0 |
T090 | 12.41 | 75.19 | 0 | 0 | 25.09 | 12.41 | |
T095 | 0 | 0 | 62.55 | 12.41 | 0 | 12.41 | |
T097 | 0 | 0 | 12.36 | 87.59 | 0 | 0 | |
T099 | 12.41 | 12.41 | 0 | 0 | 25.09 | 12.41 | |
T100 | 0 | 12.41 | 25.09 | 0 | 37.45 | 62.78 |
Average Accuracies | B080 | B090 | B095 | B097 | B099 | B100 | |
---|---|---|---|---|---|---|---|
Recognition 89.62% | B080 | 100 | 0 | 0 | 0 | 0 | 0 |
B090 | 0 | 87.62 | 18.73 | 6.19 | 0 | 0 | |
B095 | 0 | 6.19 | 68.73 | 6.19 | 6.19 | 0 | |
B097 | 0 | 6.19 | 0 | 87.62 | 0 | 0 | |
B099 | 0 | 0 | 12.55 | 0 | 93.81 | 0 | |
B100 | 0 | 0 | 0 | 0 | 0 | 100 | |
Cross-validation 68.77% | B080 | 87.59 | 0 | 0 | 0 | 12.41 | 0 |
B090 | 12.41 | 87.59 | 25.09 | 12.41 | 0 | 0 | |
B095 | 0 | 12.41 | 37.45 | 25.19 | 25.19 | 0 | |
B097 | 0 | 0 | 0 | 50 | 12.41 | 0 | |
B099 | 0 | 0 | 37.45 | 12.41 | 50 | 0 | |
B100 | 0 | 0 | 0 | 0 | 0 | 100 |
Average Accuracies | T080 | T090 | T095 | T097 | T099 | T100 | |
---|---|---|---|---|---|---|---|
Recognition 80.52% | T080 | 100 | 0 | 0 | 0 | 0 | 0 |
T090 | 0 | 81.39 | 6.19 | 6.19 | 0 | 0 | |
T095 | 0 | 6.2 | 87.62 | 0 | 18.76 | 0 | |
T097 | 0 | 6.2 | 0 | 93.81 | 0 | 0 | |
T099 | 0 | 6.2 | 6.19 | 0 | 81.24 | 12.55 | |
T100 | 0 | 0 | 0 | 0 | 0 | 87.45 | |
Cross-validation 62.55% | T080 | 100 | 0 | 0 | 0 | 0 | 0 |
T090 | 0 | 37.45 | 0 | 25.09 | 0 | 0 | |
T095 | 0 | 25.09 | 62.55 | 12.36 | 50 | 12.41 | |
T097 | 0 | 25.09 | 0 | 62.55 | 12.41 | 0 | |
T099 | 0 | 12.36 | 25.09 | 0 | 37.59 | 12.41 | |
T100 | 0 | 0 | 12.36 | 0 | 0 | 75.19 |
Average Accuracies | B080 | B090 | B095 | B097 | B099 | B100 | |
---|---|---|---|---|---|---|---|
Recognition 85.51% | B080 | 100 | 0 | 0 | 0 | 0 | 0 |
B090 | 0 | 93.81 | 12.55 | 0 | 6.19 | 0 | |
B095 | 0 | 0 | 68.73 | 0 | 6.19 | 12.55 | |
B097 | 0 | 0 | 6.18 | 100 | 0 | 0 | |
B099 | 0 | 6.19 | 12.55 | 0 | 75.05 | 12.55 | |
B100 | 0 | 0 | 0 | 0 | 12.57 | 74.91 | |
Cross-validation 56.41% | B080 | 50 | 0 | 0 | 0 | 0 | 0 |
B090 | 37.59 | 100 | 12.41 | 0 | 12.41 | 0 | |
B095 | 12.41 | 0 | 62.78 | 12.41 | 25.19 | 37.45 | |
B097 | 0 | 0 | 0 | 87.59 | 0 | 0 | |
B099 | 0 | 0 | 12.41 | 0 | 12.41 | 37.45 | |
B100 | 0 | 0 | 12.41 | 0 | 50 | 25.09 |
Meat Combination | Sample Preparation | Selected Sensors | Omitted Sensors | Initial RMSECV (% w/v Meat Mixture) | Optimized RMSECV (% w/v Meat Mixture) |
---|---|---|---|---|---|
Turkey and chicken adulteration | Raw meat extraction with distilled water | HA, BB, ZZ, GA, | JE, JB, CA | 3.68 | 3.34 |
Meat extraction by cooking with distilled water | HA, BB, ZZ, CA, JB | JE, GA | 5.19 | 4.93 | |
Frozen meat extraction with distilled water | HA, BB, ZZ, GA, JE | JB, CA | 3.04 | 2.89 | |
Beef and beef pork adulteration | Raw meat extraction with distilled water | HA, BB, CA, GA | JE, JB, ZZ | 5.91 | 5.51 |
Meat extraction by cooking with distilled water | HA, ZZ, JB | JE, GA, BB, CA | 4.44 | 3.83 | |
Frozen meat extraction with distilled water | HA, BB, ZZ JB, JE | GA, CA | 5.81 | 5.16 |
Sample Preparation Method | LV | R2 | RMSEC (w/v Meat Mixture) | R2CV | RMSECV (w/v Meat Mixture) |
---|---|---|---|---|---|
Raw meat extraction with distilled water | 3 | 0.82 | 2.91 | 0.76 | 3.34 |
Meat extraction by cooking with distilled water | 5 | 0.67 | 3.92 | 0.47 | 4.93 |
Frozen meat extraction with distilled water | 4 | 0.86 | 2.57 | 0.81 | 2.89 |
Sample Preparation Method | LV | R2 | RMSEC (w/v Meat Mixture) | R2CV | RMSECV (w/v Meat Mixture) |
---|---|---|---|---|---|
Raw meat extraction with distilled water | 4 | 0.51 | 4.78 | 0.34 | 5.51 |
Meat extraction by cooking with distilled water | 3 | 0.76 | 3.35 | 0.72 | 3.83 |
Frozen meat extraction with distilled water | 4 | 0.65 | 4.05 | 0.43 | 5.16 |
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Zaukuu, J.-L.Z.; Gillay, Z.; Kovacs, Z. Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration. Sensors 2021, 21, 481. https://doi.org/10.3390/s21020481
Zaukuu J-LZ, Gillay Z, Kovacs Z. Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration. Sensors. 2021; 21(2):481. https://doi.org/10.3390/s21020481
Chicago/Turabian StyleZaukuu, John-Lewis Zinia, Zoltan Gillay, and Zoltan Kovacs. 2021. "Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration" Sensors 21, no. 2: 481. https://doi.org/10.3390/s21020481
APA StyleZaukuu, J.-L. Z., Gillay, Z., & Kovacs, Z. (2021). Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration. Sensors, 21(2), 481. https://doi.org/10.3390/s21020481