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Bhardwaj SK, Deep A, Bhardwaj N, Wangoo N. Recent advancements in nanomaterial based optical detection of food additives: a review. Analyst 2023; 148:5322-5339. [PMID: 37750046 DOI: 10.1039/d3an01317k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Food additives have become a critical component in the food industry. They are employed as preservatives to decelerate the negative effects of environmental and microbial factors on food quality. Currently, food additives are used for a variety of purposes, including colorants, flavor enhancers, nutritional supplements, etc., owing to improvements in the food industry. Since the usage of food additives has increased dramatically, the efficient monitoring of their acceptable levels in food products is quite necessary to mitigate the problems associated with their inappropriate use. The traditional methods used for detecting food additives are generally based on standard spectroscopic and chromatographic techniques. However, these analytical techniques are limited by their high instrumentation cost and time-consuming procedures. The emerging field of nanotechnology has enabled the development of highly sensitive and specific sensors to analyze food additives in a rapid manner. The current article emphasizes the need to detect various food additives owing to their potential negative effects on humans, animals, and the environment. In this article, the role of nanomaterials in the optical sensing of food additives has been discussed owing to their high accuracy, ease-of-use, and excellent sensitivity. The applications of nanosensors for the detection of various food additives have been elaborated with examples. The current article will assist policymakers in developing new rules and regulations to mitigate the adverse effects of toxic food additives on humans and the environment. In addition, the prospects of nanosensors for the optical detection of food additives at a commercial scale have been discussed to combat their irrational use in the food industry.
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Affiliation(s)
- Sanjeev K Bhardwaj
- Department of Applied Sciences, University Institute of Engineering Technology (UIET), Panjab University, Chandigarh, India.
| | - Akash Deep
- Energy and Environment unit, Institute of Nanoscience and Technology, Mohali, India.
| | - Neha Bhardwaj
- Energy and Environment unit, Institute of Nanoscience and Technology, Mohali, India.
| | - Nishima Wangoo
- Department of Applied Sciences, University Institute of Engineering Technology (UIET), Panjab University, Chandigarh, India.
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Huang L, Qin S, Xu Y, Cheng S, Yang J, Wang Y. Enzyme-free colorimetric detection of uric acid on the basis of MnO2 nanosheets - mediated oxidation of 3, 3', 5, 5'- tetramethylbenzidine. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Guo L, Zhao DM, Chen S, Yu YL, Wang JH. Smartphone-Integrated Photoacoustic Analytical Device for Point-of-Care Testing of Food Contaminant Azodicarbonamide. Anal Chem 2022; 94:14004-14011. [PMID: 36166592 DOI: 10.1021/acs.analchem.2c03319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Azodicarbonamide (ADA) is widely used as a flour additive due to its oxidizing and bleaching properties, but it reacts with wet flour during heat processing and is easily decomposed into semicarbazide with genotoxicity and carcinogenicity. In order to improve the efficiency of food safety supervision and expand the scope of food safety control, it is of great significance to develop a facile method for point-of-care testing (POCT) of ADA. Herein, a field-portable and universal smartphone-based photoacoustic (PA) integration device is constructed for quantitative POCT of ADA in flour. The recognition probe Prussian blue with favorable stability is loaded on a flexible substrate for fabricating a portable test strip. In the presence of target ADA, the PA signal changes driven by a modulated 808 nm laser beam can be conveniently collected through the recording application (Audio Lab) of the smartphone. By combining the economic test strip and portable PA device with smartphone readout, it not only greatly simplifies the operation steps but also dramatically reduces the size and cost of the instrument. There is a favorable linear relationship between the PA signal and ADA concentration in the range of 10-200 μmol L-1 (R2 = 0.9928), and a detection limit of 5 μmol L-1 obtained is much lower than the maximum allowable ADA level in the extract of flour (388 μmol L-1). The present miniature PA device with strong POCT ability holds enormous public health significance and economic value in the field of food safety, especially in resource-limited settings.
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Affiliation(s)
- Lan Guo
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
| | - Dong-Mei Zhao
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
| | - Shuai Chen
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
| | - Yong-Liang Yu
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
| | - Jian-Hua Wang
- Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Box 332, Shenyang 110819, China
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Liu T, Chen S, Ruan K, Zhang S, He K, Li J, Chen M, Yin J, Sun M, Wang X, Wang Y, Lu Z, Rao H. A handheld multifunctional smartphone platform integrated with 3D printing portable device: On-site evaluation for glutathione and azodicarbonamide with machine learning. JOURNAL OF HAZARDOUS MATERIALS 2022; 426:128091. [PMID: 34952493 DOI: 10.1016/j.jhazmat.2021.128091] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 12/01/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Azodicarbonamide (ADA) in flour can be easily decomposed to semi-carbazide and biuret, exhibiting strong genotoxicity in vitro and carcinogenicity. Glutathione (GSH) can be conjugated with some ketone-containing compounds and unsaturated aldehydes to form toxic metabolites. Here, a novel ratio fluorescence probe based on blue emitting biomass-derived carbon dots (BCDs) and yellow emitting 2,3-diaminophenazine (OxOPD) was prepared for the bifunctional determination of glutathione (GSH) and ADA. This strategy includes three processes: (1) Ag+ oxidizes o-phenylenediamine (OPD) to produce OxOPD. The peak at 562 nm was enhanced, and the peak at 442 nm was reduced due to fluorescence resonance energy transfer (FRET), (2) glutathione binds Ag+ and inhibits the production of OxOPD, (3) ADA oxidizes GSH to form GSSG, resulting in the release of Ag+ by GSH. Therefore, the newly designed ratio fluorescence probe can be based on the intensity ratio (I442/I562) changes and significant fluorescent color changes to detect GSH and ADA. Moreover, a smartphone WeChat applet and a yolov3-assisted deep learning classification model have been developed to quickly detect GSH and ADA on-site based on an image processing algorithm. These results indicate that smartphone ratiometric fluorescence sensing combined with machine learning has broad prospects for biomedical analysis.
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Affiliation(s)
- Tao Liu
- College of Information Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Suru Chen
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Kun Ruan
- College of Information Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Shuxin Zhang
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Keqiao He
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Jian Li
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Maoting Chen
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Jiajian Yin
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Mengmeng Sun
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Xianxiang Wang
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Yanying Wang
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Zhiwei Lu
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China.
| | - Hanbing Rao
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China.
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