Nanomaterial-Based Fluorescent Biosensor for Food Safety Analysis
Abstract
:1. Introduction
2. Biomaterials Used in Food Safety Analysis
2.1. Aptamers
2.2. Antibodies
2.3. Enzymes
2.4. Nucleic Acids
2.5. Others
3. Nanomaterials in Food Safety Analysis
3.1. Nanoparticles
3.2. Nanoclusters
3.3. Two-Dimensional Materials
3.4. Metal-Organic Frameworks
3.5. Others
4. Application of Fluorescent Biosensors for Food Safety Analysis
4.1. Mycotoxins
4.2. Heavy Metals
4.3. Antibiotics
4.4. Foodborne Pathogens
4.5. Other Illegal Additives
5. Conclusions and Perspectives
- (1)
- With the development of bio- and nanomaterials, the development of inexpensive, easy-to-synthesize, and eco-friendly materials remains a topic for future research.
- (2)
- Many nanomaterial-based biosensors have been successfully developed; however, their detection performance must be improved. Some suffer from limitations, such as low stability, poor repeatability, and a weak anti-interference ability. Therefore, there is an urgent need to develop efficient methods for fluorescence biosensing.
- (3)
- High-performance aptamers have been screened. Aptamer screening has accelerated the development of new aptasensors. Although numerous aptasensors have been developed, not all types of analyte have been investigated. Biomaterial analogs should be developed and integrated into biosensors to increase the number of aptamers.
- (4)
- Currently, newly developed biosensors are still in their early stages. Future research in this area should focus on real-time monitoring or on-site analysis, for example, the investigation of portable devices for food safety analysis.
- (5)
- Although nano- and biomaterials have advantages in terms of performance, their synthesis conditions must be optimized. In addition to full purification and the removal of impurities, multiple fluorescences should be used instead of a single fluorescence.
- (6)
- Increased practical applications. Currently, most fluorescent systems are in the experimental stage. The practical applications of nanomaterials-based fluorescent biosensors in complex matrices remain a great challenge. Adopting machine learning and microfluidic systems into fluorescence biosensors may meet the criteria of cheap real-time detection in complex matrices.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Analytes | Nanomaterials | Linear Range | LOD | Food Matrix | Ref. |
---|---|---|---|---|---|
Mycotoxins | |||||
T-2 toxin | AgNCs | 0.005–500 ng/mL | 0.93 pg/mL | maize and wheat | [86] |
DON | QDs | 1–10 ng/ml | 28 μg/kg | wheat | [87] |
FB1 | AuNPs | 7.3–22.6 ng/mL | 1.1 ng/mL | wheat | [89] |
FB1, ZEN and OTA | QDs/QD | ------ | 0.25 ng/mL 3.0 ng/mL 0.5 ng/mL | ------ | [90] |
OTA and AFB1 | hydrogel particles | 0.1–500 ng/mL, 0.1–200 ng/mL | 0.1 ng/mL | corn flour | [91] |
OTA, AFB1, and FB1 | TiO2-Si | 0.1–10 ng/mL, 0.01–10 ng/mL, 0.001–10 ng/mL | 15.4 pg/mL 1.48 pg/mL 0.21 pg/mL | rice, corn, and wheat | [92] |
Heavy metals | |||||
Hg2+ | CdTe QDs | 0.7–900 nM | 2.5 nM | water | [94] |
Hg2+ | DNA-hydrogel | ------ | 10 nM | water | [95] |
Pb2+ | NH2-MIL-125(Ti) MOF | 0–11 nM | 7.7 pM | ------ | [96] |
Pb2+ | AuNCs | 0–190 nM | 10 nM | pond water and river water | [97] |
Pb2+ | ZIF-8 | 0.01–10.0 nM | 7.1 pM | water and fish | [98] |
Cd2+ and Pb2+ | V6O13 nanobelts | 5–200 μg/L, 5–100 μg/L | 1.89 μg/L, 2.11 μg/L | water | [100] |
Cr3+ and Pb2+ | CDs | 0.1–6.0 μM, 0.1–5.0 μM | 27 nM 34 nM | water | [101] |
Cd2+, Hg2+ and Pb2+ | DNAzymes | ------ | 4.8 nM 2.0 nM 0.1 nM | ------ | [102] |
Hg2+ and Ag+ | MPDA frame-works | 0–2 nM 1–3 nM | 1.3 pM 34 pM | water | [103] |
Antibiotics | |||||
TET | Apt-tet MBs | 0.001–10 ng/mL | 0.724 pg/mL | fish and honey | [108] |
TET OTC DOX | BNQD-Eu3+ | ------ | 0.019 μM 0.104 μM 0.104 μM | milk and beef | [109] |
TET | BSA-BMNCs | ------ | ------ | water and milk | [110] |
KAN | aptamer probes | 0.1–75 nM | 0.097 nM | aquatic products | [112] |
tobramycin | magnetic beads | 0.3–50 μM | 17.37 nM | ------ | [113] |
AMP | UCNPs | 10–100 ng/mL | 3.9 ng/mL | milk | [115] |
penicillin | CDs | 1–32 nM | 0.34 nM | milk | [116] |
CAP | DNA four-arm junctions | 1.0 pg/mL–10 ng/mL | 0.72 pg/mL | milk and honey | [119] |
AMP, CPFX, KAN, SMZ, TET, and TMP | carbon nanoparticle | ------ | ------ | poultry feeds | [120] |
TET, AMP, and sulfacetamide | nanocluster | 50–5000 ng/mL 5–5000 ng/mL 50–5000 ng/mL | 3.5 ng/mL 1.4 ng/mL 7.6 ng/mL | milk | [121] |
Foodborne pathogens | |||||
Escherichia coli | CDs | 2.4 × 102–2.4 × 107 CFU/mL | 2.4 × 102 CFU/mL | milk | [124] |
S. aureus | GOQDs | 1 × 10−17–1 × 10−11 mol/L | 0.98 × 10−17 mol/L | ------ | [125] |
Escherichia coli, Salmonella typhimurium | apt-FMNPs | 40–108 CFU/mL 63–108 CFU/mL | 16 CFU/mL 25 CFU/mL | ------ | [126] |
Other illegal additives | |||||
Triazophos, parathion, chlorpyrifos | AuNPs | 0.01–20 μg/L, 0.05–50 μg/L, 0.5–1000 μg/L | 0.007 μg/L, 0.009 μg/L, 0.087 μg/L | rice, wheat, cucumber, cabbage, and apple | [127] |
paraquat, carbendazim pesticides | UCNPs and BPNS | 1.0–1.0 × 105 ng/mL | 0.18 ng/mL, 0.45 ng/mL | ------ | [128] |
CAP, 17β-estradiol | MOF-MoS2 | 0.9917–5 nM, 0–5 nM | 200 pM, 180 pM | milk, honey, and water | [129] |
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Zhou, J.; Gui, Y.; Lv, X.; He, J.; Xie, F.; Li, J.; Cai, J. Nanomaterial-Based Fluorescent Biosensor for Food Safety Analysis. Biosensors 2022, 12, 1072. https://doi.org/10.3390/bios12121072
Zhou J, Gui Y, Lv X, He J, Xie F, Li J, Cai J. Nanomaterial-Based Fluorescent Biosensor for Food Safety Analysis. Biosensors. 2022; 12(12):1072. https://doi.org/10.3390/bios12121072
Chicago/Turabian StyleZhou, Jiaojiao, Yue Gui, Xuqin Lv, Jiangling He, Fang Xie, Jinjie Li, and Jie Cai. 2022. "Nanomaterial-Based Fluorescent Biosensor for Food Safety Analysis" Biosensors 12, no. 12: 1072. https://doi.org/10.3390/bios12121072
APA StyleZhou, J., Gui, Y., Lv, X., He, J., Xie, F., Li, J., & Cai, J. (2022). Nanomaterial-Based Fluorescent Biosensor for Food Safety Analysis. Biosensors, 12(12), 1072. https://doi.org/10.3390/bios12121072