Rapid and Sensitive Detection of Polycyclic Aromatic Hydrocarbons in Tea Leaves Using Magnetic Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Real Sample
2.2. Chemicals and Standards
2.3. Preparation and Characterization of C18/MNPs
2.3.1. Synthesis of C18/MNPs
2.3.2. Characterization of C18/MNPs
2.4. C18/MNPs-Based Extraction Procedure for the Preconcentration and Detection of Five Kinds of PAHs in Tea
2.4.1. Configuration of Standard Solution and Preparation of Spike Samples
2.4.2. Preconcentration Procedures
2.5. Recycling of C18/MNPs
2.6. UPLC Analysis
2.7. Statistical Analysis
3. Results and Discussion
3.1. Characterizing of C18/MNPs
3.2. Optimizing MSPE Conditions
3.2.1. Amount of Absorbents
3.2.2. Extraction Time
3.2.3. Type of the Eluent
3.2.4. Elution Time
3.2.5. Volume of Eluent
3.3. Evaluation of the Detection Method Performance
3.4. Reproducibility and Reusability of the C18/MNPs
3.5. Method Comparison
3.6. Real Sample Analysis
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Chemical Name | Abbreviation | Molecular Structure | Molecular Formula | Molecular Weight | Ring | Type |
---|---|---|---|---|---|---|
Fluorene | FLU | C13H10 | 166.22 | 3 | Light PAHs | |
Anthracene | ANT | C14H10 | 178.23 | 3 | Light PAHs | |
Pyrene | PYR | C16H10 | 202.25 | 4 | Light PAHs | |
Benzo[a]anthracene | BaA | C18H12 | 228.3 | 4 | Light PAHs | |
Benzo[b]fluoranthene | BbF | C20H12 | 252.3 | 5 | Heavy PAHs |
Analyte | Equation of Calibration Curve | R2 | LOD (ng g−1) | LOQ (ng g−1) |
---|---|---|---|---|
Fluorene | y = 48627x + 71642 | 0.998 | 7.93 | 24.03 |
Anthracene | y = 106034x + 4318855 | 0.999 | 5.32 | 16.12 |
Pyrene | y = 22396x + 216848 | 0.997 | 9.97 | 30.21 |
Benzo[a]anthracene | y = 56517x + 357848 | 0.998 | 1.69 | 5.12 |
Benzo[b]fluoranthene | y = 51622x + 661315 | 0.998 | 5.43 | 16.45 |
Analyte | Concentration (ng g−1) | Intra-Day Precision | Inter-Day Precision | ||
---|---|---|---|---|---|
Recovery ± SD (%) | RSD (%) | Recovery ± SD (%) | RSD (%) | ||
Fluorene | 50 | 94.8 ± 2.8 | 3.4 | 84.8 ± 2.9 | 6.8 |
100 | 95.2 ± 0.7 | 1.3 | 91.6 ± 0.3 | 3.1 | |
200 | 88.9 ± 1.9 | 1.4 | 93.6 ± 6.1 | 6.6 | |
Anthracene | 50 | 91.7 ± 0.8 | 2.9 | 88.1 ± 0.9 | 5.9 |
100 | 100.2 ± 1.6 | 1.3 | 94.6 ± 0.7 | 3.7 | |
200 | 93.0 ± 0.2 | 6.7 | 99.0 ± 2.3 | 2.3 | |
Pyrene | 50 | 89.9 ± 1.2 | 5.9 | 99.3 ± 0.7 | 2.8 |
100 | 95.8 ± 1.4 | 2.3 | 100.5 ± 1.7 | 4.7 | |
200 | 102.1 ± 0.7 | 11.9 | 105.4 ± 3.0 | 2.9 | |
Benzo[a] anthracene | 50 | 99.1 ± 1.1 | 2.1 | 93.2 ± 1.8 | 5.1 |
100 | 97.4 ± 0.9 | 2.4 | 96.7 ± 0.6 | 6.2 | |
200 | 81.1 ± 2.2 | 2.2 | 86.3 ± 3.6 | 4.2 | |
Benzo[b] fluoranthene | 50 | 102.1 ± 3.1 | 7.1 | 96.2 ± 2.1 | 4.9 |
100 | 98.2 ± 1.7 | 2.9 | 98.5 ± 5.2 | 5.2 | |
200 | 93.4 ± 3.5 | 5.2 | 91.4 ± 3.6 | 4.0 |
Method | Detection Technique | Sample | Linear Range (ng g−1) | LOD (ng g−1) | Recovery (%) | RSD (%) | References |
---|---|---|---|---|---|---|---|
SPE | GC-MS | Dry tea | - | 0.09–0.32 | 37.00–96.10 | - | Ciemniak et al. (2019) [38] |
SPE | HPLC-FLD | Tea | - | 16.00 | 90.00–95.00 | <10.00 | Stuppner et al. (2020) [39] |
LLE | GC-MS | Roasted coffee | 0.25–4.00 | 0.04–0.18 | 87.08–111.28 | 3.26–23.75 | Pissinatti et al. (2015) [40] |
GE | UPLC-Q-TOF-MS | Tea | - | 2.00–100.00 | 78.40–109.20 | 1.33–9.72 | Li et al. (2022) [41] |
UAE | GC-FID | Dry tea | - | 0.30 | 90.24–108.92 | 77.02–100.60 | Benson et al. (2018) [42] |
MSPE | UPLC-UV | Tea | 0.50–300.00 | 1.69–9.97 | 84.80–105.40 | 2.30–6.80 | This work |
Tea Type | Fluorene (ng g−1) | Anthracene (ng g−1) | Pyrene (ng g−1) | Benzo[a]anthracene (ng g−1) | Benzo[b]fluoranthene (ng g−1) | |
---|---|---|---|---|---|---|
Green (10 tea brands) | Mean | 132.3 | 41.9 | 109.5 | N.D. | N.D. |
Min | 31.2 | N.D. | N.D. | N.D. | N.D. | |
Max | N.D. | 196.5 | N.D. | 8.4 | N.D. | |
Black (3 tea brands) | Mean | 109.8 | 21.7 | 94.6 | N.D. | N.D. |
Min | 81.9 | N.D. | 70.3 | N.D. | N.D. | |
Max | 141.5 | 31.2 | 113.4 | 5.3 | N.D. |
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Zhao, X.; Feng, X.; Chen, J.; Zhang, L.; Zhai, L.; Lv, S.; Ye, Y.; Chen, Y.; Zhong, T.; Yu, X.; et al. Rapid and Sensitive Detection of Polycyclic Aromatic Hydrocarbons in Tea Leaves Using Magnetic Approach. Foods 2023, 12, 2270. https://doi.org/10.3390/foods12112270
Zhao X, Feng X, Chen J, Zhang L, Zhai L, Lv S, Ye Y, Chen Y, Zhong T, Yu X, et al. Rapid and Sensitive Detection of Polycyclic Aromatic Hydrocarbons in Tea Leaves Using Magnetic Approach. Foods. 2023; 12(11):2270. https://doi.org/10.3390/foods12112270
Chicago/Turabian StyleZhao, Xiaohan, Xiao Feng, Jingwen Chen, Lanxin Zhang, Lingzi Zhai, Sizhe Lv, Yonghao Ye, Yongqi Chen, Tian Zhong, Xi Yu, and et al. 2023. "Rapid and Sensitive Detection of Polycyclic Aromatic Hydrocarbons in Tea Leaves Using Magnetic Approach" Foods 12, no. 11: 2270. https://doi.org/10.3390/foods12112270
APA StyleZhao, X., Feng, X., Chen, J., Zhang, L., Zhai, L., Lv, S., Ye, Y., Chen, Y., Zhong, T., Yu, X., & Xiao, Y. (2023). Rapid and Sensitive Detection of Polycyclic Aromatic Hydrocarbons in Tea Leaves Using Magnetic Approach. Foods, 12(11), 2270. https://doi.org/10.3390/foods12112270