Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. General Chemical Analysis
2.3. E-Nose Based on Colorimetric Sensors
- (1)
- manganese(3+),10,12,13,23-tetraphenyl-21H-porphyrin,trichloride;
- (2)
- zinc-2,3,9,10,16,17,23,24-octakis-(octyloxy)-29H,31H-phthalocyanine;
- (3)
- manganese(3+),2,12,13,15,17,18,20,23-octaethyl-21H-porphyrin,trichloride;
- (4)
- 5,10,15,20-Tetraphenyl-21H,23H-porphinecopper;
- (5)
- 5,10,15,20-tetraphenyl-21,22-dihydroporphyrin,zinc;
- (6)
- [5,10,15,20-tetrakis(C6F5)-21H,23H-porphine]Fe(III) chloride;
- (7)
- 5,10,15,20-tetraphenyl-21h,23h-porphine iron(iii) chloride;
- (8)
- 5,10,15,20-Tetraphenyl-21H,23H-porphine Cobalt(II);
- (9)
- 5,10,15,20-tetraphenyl-21H,23H-porphine;
- (10)
- 2-[4-(dimethylamino)phenylazo]benzoic acid;
- (11)
- m-Cresol, 4,4′-(3H-2,1-benzoxathiol-3-ylidene)bis(2,6-dibromo-, S,S-dioxide (8CI);
- (12)
- 9-(Diethylamino)-5H-benzo[a]phenoxazin-5-one.
2.4. Operating Procedure and Feature Extraction
ΔG= |Ga− Gb|
ΔB = |Ba− Bb|
2.5. Multivariate Analysis and Software
2.6. Software
3. Results
3.1. Chemical Analysis Results
3.2. Sensor Responses and Preprocessing
3.3. Identification of the Beef Adulterated with Pork
3.4. Prediction of Adulteration Levels
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Ethical Approval
References
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Crude Protein | Total Lipid | Total Ash | |
---|---|---|---|
beef | 14 ± 0.85a | 34 ± 4.0a | 0.62 ± 0.10a |
pork | 18 ± 3.0b | 4.3 ± 0.47b | 1.0 ± 0.19b |
Adulteration Level | 20% | 40% | 60% | 80% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 in Figure 3b | C2 in Figure 3b | C3 in Figure 3b | C1 in Figure 3c | C2 in Figure 3c | C3 in Figure 3c | C1 in Figure 3d | C2 in Figure 3d | C3 in Figure 3d | C1 in Figure 3e | C2 in Figure 3e | C3 in Figure 3e | |
Red | 7.2 | 4.8 | 0.49 | 12 | 5.1 | 0.55 | 7.9 | 2.0 | 3.4 | 8.1 | 6.8 | 0.031 |
0.25 | 0.21 | 0.54 | 1.1 | 0.50 | 0.053 | 5.4 | 1.7 | 4.8 | 0.089 | 0.40 | 1.7 | |
1.2 | 0.70 | 0.31 | 2.0 | 0.79 | 0.65 | 1.7 | 2.4 | 0.37 | 1.4 | 0.87 | 0.068 | |
4.2 | 0.15 | 0.22 | 8.4 | 0.62 | 0.31 | 8.2 | 0.35 | 1.8 | 9.3 | 1.4 | 1.8 | |
Green | 7.3 | 0.49 | 0.30 | 11 | 0.024 | 0.53 | 9.3 | 7.4 | 3.0 | 6.7 | 6.1 | 0.33 |
0.2 | 0.53 | 0.34 | 2.2 | 1.3 | 0.52 | 5.4 | 4.2 | 4.7 | 0.40 | 0.66 | 0.19 | |
1.9 | 0.53 | 0.04 | 1.5 | 0.22 | 0.20 | 0.86 | 3.1 | 5.8 | 1.2 | 1.2 | 0.090 | |
1.3 | 0.47 | 5.4 | 2.6 | 0.18 | 8.6 | 0.74 | 0.25 | 10 | 1.5 | 1.6 | 111 | |
Blue | 0.18 | 0.56 | 0.44 | 1.7 | 0.11 | 1.1 | 3.7 | 7.6 | 5.8 | 2.3 | 6.8 | 1.1 |
0.34 | 0.41 | 0.60 | 2.6 | 0.89 | 1.8 | 0.98 | 4.9 | 12 | 0.86 | 1.1 | 1.4 | |
1.3 | 0.05 | 0.64 | 6.3 | 1.0 | 3.0 | 4.7 | 4.0 | 5.5 | 5.1 | 0.049 | 1.8 | |
0.36 | 0.51 | 0.25 | 0.022 | 1.1 | 4.4 | 0.024 | 1.0 | 4.2 | 0.30 | 1.8 | 0.31 |
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Han, F.; Huang, X.; H. Aheto, J.; Zhang, D.; Feng, F. Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors. Foods 2020, 9, 193. https://doi.org/10.3390/foods9020193
Han F, Huang X, H. Aheto J, Zhang D, Feng F. Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors. Foods. 2020; 9(2):193. https://doi.org/10.3390/foods9020193
Chicago/Turabian StyleHan, Fangkai, Xingyi Huang, Joshua H. Aheto, Dongjing Zhang, and Fan Feng. 2020. "Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors" Foods 9, no. 2: 193. https://doi.org/10.3390/foods9020193