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Mutunga T, Sinanovic S, Harrison CS. Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater. SENSORS (BASEL, SWITZERLAND) 2024; 24:3191. [PMID: 38794044 PMCID: PMC11125874 DOI: 10.3390/s24103191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.
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Affiliation(s)
- Titus Mutunga
- School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, Scotland, UK; (S.S.); (C.S.H.)
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Ji B, Yang L, Ren C, Xu X, Zhao W, Yang Y, Xu G, Zhao D, Bai Y. A modified QuEChERS method based on a reduced graphene oxide-coated melamine sponge for multiresidue analysis of veterinary drugs in mutton by UPLC-MS/MS. Food Chem 2024; 433:137376. [PMID: 37683470 DOI: 10.1016/j.foodchem.2023.137376] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/10/2023]
Abstract
The development of different matrix adsorbents is a research focus in the multiresidue analysis of veterinary drugs in foods. In this study, a novel elastic matrix adsorbent based on a reduced graphene oxide-coated melamine sponge was prepared and applied in matrix purification for the simultaneous determination of 52 veterinary drugs in mutton by UPLC-MS/MS. Efficient and convenient matrix removal was achieved through simple soaking and squeezing. Good linearities with determination coefficients ≥0.999 and low matrix effects ≤±13% were obtained in the range of 10-500 μg·kg-1. The obtained recoveries ranged from 63.7% to 109.5% at three spiked levels (10, 50, and 100 μg·kg-1), with relative standard deviations ≤8.1%. Low LODs and LOQs were obtained in ranges of 0.02-2.0 μg·kg-1 and 0.05-5.0 μg·kg-1, respectively. Based on the comprehensive results, our developed method showed good applicability in the analysis of multiresidues in various foods.
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Affiliation(s)
- Baocheng Ji
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China; Henan Key Laboratory of Cold Chain Quality and Safety Control, Zhengzhou, PR China; Collaborative Innovation Center of Food Production and Safety, Henan Province, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou, PR China
| | - Lanrui Yang
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China; Henan Key Laboratory of Cold Chain Quality and Safety Control, Zhengzhou, PR China; Collaborative Innovation Center of Food Production and Safety, Henan Province, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou, PR China
| | - Chengyu Ren
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China
| | - Xu Xu
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China; Henan Key Laboratory of Cold Chain Quality and Safety Control, Zhengzhou, PR China; Collaborative Innovation Center of Food Production and Safety, Henan Province, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou, PR China
| | - Wenhao Zhao
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China; Henan Key Laboratory of Cold Chain Quality and Safety Control, Zhengzhou, PR China; Collaborative Innovation Center of Food Production and Safety, Henan Province, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou, PR China
| | - Yike Yang
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China
| | - Gaigai Xu
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China
| | - Dianbo Zhao
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China; Henan Key Laboratory of Cold Chain Quality and Safety Control, Zhengzhou, PR China; Collaborative Innovation Center of Food Production and Safety, Henan Province, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou, PR China
| | - Yanhong Bai
- School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, PR China; Henan Key Laboratory of Cold Chain Quality and Safety Control, Zhengzhou, PR China; Collaborative Innovation Center of Food Production and Safety, Henan Province, PR China; Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou, PR China.
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Weng S, Tang L, Qiu M, Wang J, Wu Y, Zhu R, Wang C, Li P, Sha W, Liang D. Surface-enhanced Raman spectroscopy charged probes under inverted superhydrophobic platform for detection of agricultural chemicals residues in rice combined with lightweight deep learning network. Anal Chim Acta 2023; 1262:341264. [PMID: 37179059 DOI: 10.1016/j.aca.2023.341264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/18/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023]
Abstract
In this study, surface-enhanced Raman spectroscopy (SERS) charged probes and an inverted superhydrophobic platform were used to develop a detection method for agricultural chemicals residues (ACRs) in rice combined with lightweight deep learning network. First, positively and negatively charged probes were prepared to adsorb ACRs molecules to SERS substrate. An inverted superhydrophobic platform was prepared to alleviate the coffee ring effect and induce tight self-assembly of nanoparticles for high sensitivity. Chlormequat chloride of 15.5-0.05 mg/L and acephate of 100.2-0.2 mg/L in rice were measured with the relative standard deviation of 4.15% and 6.25%. SqueezeNet were used to develop regression models for the analysis of chlormequat chloride and acephate. And the excellent performances were obtained with the coefficients of determination of prediction of 0.9836 and 0.9826 and root-mean-square errors of prediction of 0.49 and 4.08. Therefore, the proposed method can realize sensitive and accurate detection of ACRs in rice.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China.
| | - Le Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China
| | - Mengqing Qiu
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, 230031, People's Republic of China.
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China
| | - Yehang Wu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China
| | - Rui Zhu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China
| | - Cong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China
| | - Pan Li
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, 230031, People's Republic of China
| | - Wen Sha
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei, People's Republic of China.
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In Situ Collection and Rapid Detection of Pathogenic Bacteria Using a Flexible SERS Platform Combined with a Portable Raman Spectrometer. Int J Mol Sci 2022; 23:ijms23137340. [PMID: 35806345 PMCID: PMC9267095 DOI: 10.3390/ijms23137340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/25/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022] Open
Abstract
This study aims to develop a simple, sensitive, low-cost, environmentally friendly and flexible surface-enhanced Raman scattering (SERS) platform, combined with a portable Raman spectrometer, for the rapid and on-site SERS detection of bacteria. Commercial tobacco packaging paper (TPP) with little background interference was used as a loading medium that effectively adsorbed Au nanoparticles and provided sufficient “hot spots”. This Au-tobacco packaging paper (Au-TPP) substrate used as a flexible SERS platform can maximize sample collection by wiping irregular surfaces, and was successfully applied to the on-site and rapid detection of pathogenic bacteria. Raman fingerprints of pathogenic bacteria can be obtained by SERS detection of spiked pork using wipeable Au-TPP, which verifies its value in practical applications. The results collected by SERS were further verified by polymerase chain reaction (PCR) results. It showed several advantages in on-site SERS detection, including accurate discrimination, simple preparation, easy operation, good sensitivity, accuracy and reproducibility. This study indicates that the established flexible SERS platform has good practical applications in pathogenic bacterial identification and other rapid detections.
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Nilghaz A, Mahdi Mousavi S, Amiri A, Tian J, Cao R, Wang X. Surface-Enhanced Raman Spectroscopy Substrates for Food Safety and Quality Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:5463-5476. [PMID: 35471937 DOI: 10.1021/acs.jafc.2c00089] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has been identified as a fundamental surface-sensitive technique that boosts Raman scattering by adsorbing target molecules on specific surfaces. The application of SERS highly relies on the development of smart SERS substrates, and thus the fabrication of SERS substrates has been constantly improved. Herein, we investigate the impacts of different substrates on SERS technology including plasmonic metal nanoparticles, semiconductors, and hybrid systems in quantitative food safety and quality analysis. We first discuss the fundamentals, substrate designs, and applications of SERS. We then provide a critical review of the recent progress of SERS in its usage for screening and detecting chemical and biological contaminants including fungicides, herbicides, insecticides, hazardous colorants, and biohazards in food samples to assess the analytical capabilities of this technology. Finally, we investigate the future trends and provide practical techniques that could be used to fulfill the requirements for rapid analysis of food at a low cost.
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Affiliation(s)
- Azadeh Nilghaz
- Institute for Frontier Materials, Deakin University, Waurn Ponds, VIC 3216, Australia
| | | | - Amir Amiri
- Department of Food Science and Technology, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Junfei Tian
- State Key Laboratory of Pulp & Paper Engineering, South China University of Technology, Guangzhou 510640, China
| | - Rong Cao
- Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou 571199, China
| | - Xungai Wang
- Institute for Frontier Materials, Deakin University, Waurn Ponds, VIC 3216, Australia
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Rahman MM, Lee DJ, Jo A, Yun SH, Eun JB, Im MH, Shim JH, Abd El-Aty AM. Onsite/on-field analysis of pesticide and veterinary drug residues by a state-of-art technology: A review. J Sep Sci 2021; 44:2310-2327. [PMID: 33773036 DOI: 10.1002/jssc.202001105] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/12/2021] [Accepted: 03/13/2021] [Indexed: 11/08/2022]
Abstract
Pesticides and veterinary drugs are generally employed to control pests and insects in crop and livestock farming. However, remaining residues are considered potentially hazardous to human health and the environment. Therefore, regular monitoring is required for assessing and legislation of pesticides and veterinary drugs. Various approaches to determining residues in various agricultural and animal food products have been reported. Most analytical methods involve sample extraction, purification (cleanup), and detection. Traditional sample preparation is time-consuming labor-intensive, expensive, and requires a large amount of toxic organic solvent, along with high probability for the decomposition of a compound before the analysis. Thus, modern sample preparation techniques, such as the quick, easy, cheap, effective, rugged, and safe method, have been widely accepted in the scientific community for its versatile application; however, it still requires a laboratory setup for the extraction and purification processes, which also involves the utilization of a toxic solvent. Therefore, it is crucial to elucidate recent technologies that are simple, portable, green, quick, and cost-effective for onsite and infield residue detections. Several technologies, such as surface-enhanced Raman spectroscopy, quantum dots, biosensing, and miniaturized gas chromatography, are now available. Further, several onsite techniques, such as ion mobility-mass spectrometry, are now being upgraded; some of them, although unable to analyze field sample directly, can analyze a large number of compounds within very short time (such as time-of-flight and Orbitrap mass spectrometry). Thus, to stay updated with scientific advances and analyze organic contaminants effectively and safely, it is necessary to study all of the state-of-art technology.
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Affiliation(s)
- Md Musfiqur Rahman
- Natural Products Chemistry Laboratory, Chonnam National University, Gwangju, Republic of Korea
| | - Dong Ju Lee
- Natural Products Chemistry Laboratory, Chonnam National University, Gwangju, Republic of Korea
| | - Ara Jo
- Natural Products Chemistry Laboratory, Chonnam National University, Gwangju, Republic of Korea
| | - Seung Hee Yun
- Natural Products Chemistry Laboratory, Chonnam National University, Gwangju, Republic of Korea
| | - Jong-Bang Eun
- Department of Food Science and Technology and BK 21 plus Program, Graduate School of Chonnam National University, Gwangju, Republic of Korea
| | - Moo-Hyeog Im
- Department of Food Engineering, Daegu University, Gyeongbuk, Republic of Korea
| | - Jae-Han Shim
- Natural Products Chemistry Laboratory, Chonnam National University, Gwangju, Republic of Korea
| | - A M Abd El-Aty
- Department of Pharmacology, Faculty of Veterinary Medicine, Cairo University, Giza, Egypt.,Department of Medical Pharmacology, Medical Faculty, Ataturk University, Erzurum, Turkey
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