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Ma H, Guo J, Liu G, Xie D, Zhang B, Li X, Zhang Q, Cao Q, Li X, Ma F, Li Y, Wan G, Li Y, Wu D, Ma P, Guo M, Yin J. Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review. Crit Rev Food Sci Nutr 2024:1-23. [PMID: 38523442 DOI: 10.1080/10408398.2024.2329956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.
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Affiliation(s)
- Haiyang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Jiajun Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guishan Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Delang Xie
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Bingbing Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaojun Li
- School of Electronic and Electrical Engineering, Ningxia University, Yinchuan, China
| | - Qian Zhang
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Qingqing Cao
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Xiaoxue Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Fang Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Yang Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Guoling Wan
- College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Yan Li
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Di Wu
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Ping Ma
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Mei Guo
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
| | - Junjie Yin
- School of Food Science and Engineering, Ningxia University, Yinchuan, Ningxia, China
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Zhou Z, Tian D, Yang Y, Cui H, Li Y, Ren S, Han T, Gao Z. Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection. Curr Res Food Sci 2024; 8:100679. [PMID: 38304002 PMCID: PMC10831501 DOI: 10.1016/j.crfs.2024.100679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.
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Affiliation(s)
- Zixuan Zhou
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Daoming Tian
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
- Beidaihe Rest and Recuperation Center of PLA, Qinhuangdao, 066000, China
| | - Yingao Yang
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Han Cui
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, 300457, China
| | - Yanchun Li
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Shuyue Ren
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Tie Han
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Zhixian Gao
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
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Lu Y, Li C, Wang Y, Wang Z, Liu C, Fan H, Sun T. A SERS Responsive DGT Sensing Device for On-Site Determination of Organic Contaminants Underwater. ACS Sens 2023; 8:3762-3771. [PMID: 37811839 DOI: 10.1021/acssensors.3c01169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Although diffusion gradient in the thin-film technique (DGT) is highly regarded in environmental analysis, the traditional DGT devices cannot serve as sensing devices but in situ sampling devices. Here we report a surface enhanced Raman scattering (SERS) responsive DGT sensing device (SERS-DGT Sensor) capable of on-site determination of organic contaminants underwater. This is achieved by innovatively utilizing a SERS responsive liquid suspension of Au nanoparticles supported on graphene oxide (AuNPs@GO) as the DGT binding phase. Liquid suspension is synthesized via a combined secondary growth and molecular welding approach and used as DGT binding phase AuNPs@GO exhibit good SERS activity, aqueous stability, and adsorption performance. Based on the development time range of 24-144 h, the measurement of sulfadiazine (SMT) by SERS-DGT Sensor is evaluated in the concentration range of 0.3289-2631 ng mL-1. The SERS-DGT sampler is valid for measuring SMT under a wide range of environmental conditions (i.e, pH 5-9, ionic strength 0.0001-0.05 mol L-1 NaCl, DOM concentrations 0-100 mg L-1, the values of TC: SMT ≤ 20 and MNZ: SMT ≤ 20). SERS-DGT Sensor is applied to the practical test of SMT content in pig breeding wastewater, and compared with the grab sampling method, the results confirm that this novel hyphenated technique exhibits good accuracy and precision. The platform proves to be versatile by extending the method to the monitoring of rhodamine 6G, metronidazole, fluoxetine, and enrofloxacin. In this article, SERS-DGT Sensor, a platform for directly on-site sensing of organic DGT, holds great potential for in situ sampling and on-site sensing for a wide range of organics and provides a new idea for environmental monitoring.
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Affiliation(s)
- Yunshu Lu
- Department of Chemistry, Northeastern University, Shenyang, Liaoning 110819, P. R. China
| | - Changwei Li
- School of New Energy, Ningbo University of Technology, Ningbo 315336, P. R. China
- School of Materials Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, P. R. China
| | - Yun Wang
- Department of Chemistry, Northeastern University, Shenyang, Liaoning 110819, P. R. China
| | - Ziyue Wang
- Department of Chemistry, Northeastern University, Shenyang, Liaoning 110819, P. R. China
| | - Chang Liu
- Department of Chemistry, Jinzhou Medical University, Jinzhou, Liaoning 121001, P. R. China
| | - Hongtao Fan
- College of Chemistry Chemical Engineering, and Environmental Engineering, Liaoning University of Petroleum & Chemical Technology, Fushun 113001, P. R. China
| | - Ting Sun
- Department of Chemistry, Northeastern University, Shenyang, Liaoning 110819, P. R. China
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Kharbach M, Alaoui Mansouri M, Taabouz M, Yu H. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches. Foods 2023; 12:2753. [PMID: 37509845 PMCID: PMC10379817 DOI: 10.3390/foods12142753] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/30/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
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Affiliation(s)
- Mourad Kharbach
- Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
- Department of Computer Sciences, University of Helsinki, 00560 Helsinki, Finland
| | - Mohammed Alaoui Mansouri
- Nano and Molecular Systems Research Unit, University of Oulu, 90014 Oulu, Finland
- Research Unit of Mathematical Sciences, University of Oulu, 90014 Oulu, Finland
| | - Mohammed Taabouz
- Biopharmaceutical and Toxicological Analysis Research Team, Laboratory of Pharmacology and Toxicology, Faculty of Medicine and Pharmacy, University Mohammed V in Rabat, Rabat BP 6203, Morocco
| | - Huiwen Yu
- Shenzhen Hospital, Southern Medical University, Shenzhen 518005, China
- Chemometrics group, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
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Lin C, Li Y, Peng Y, Zhao S, Xu M, Zhang L, Huang Z, Shi J, Yang Y. Recent development of surface-enhanced Raman scattering for biosensing. J Nanobiotechnology 2023; 21:149. [PMID: 37149605 PMCID: PMC10163864 DOI: 10.1186/s12951-023-01890-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 04/10/2023] [Indexed: 05/08/2023] Open
Abstract
Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.
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Affiliation(s)
- Chenglong Lin
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing, 100049, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yanyan Li
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing, 100049, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yusi Peng
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing, 100049, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Shuai Zhao
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing, 100049, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Meimei Xu
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing, 100049, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Lingxia Zhang
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Zhengren Huang
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
| | - Jianlin Shi
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yong Yang
- State Key Laboratory of High-Performance Ceramics and Superfine Microstructures, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai, 200050, People's Republic of China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
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Li F, Huang Y, Wang X, Wang D, Fan M. Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification. LUMINESCENCE 2023; 38:302-307. [PMID: 36702476 DOI: 10.1002/bio.4449] [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: 12/24/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/28/2023]
Abstract
The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.
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Affiliation(s)
- Fan Li
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Yuting Huang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Xueqing Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Dongmei Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
| | - Meikun Fan
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China
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Qiao J, Hu S, Chen L, Gan J, Li G, Wen L, Shi S. Cross-Platform Identification and Classification of Fentanyl Analogs by Tandem Mass Spectrometry (MS/MS). ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2144343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Juanjuan Qiao
- Research Institute of Advanced Technologies, Ningbo University, Ningbo, China
| | - Shundi Hu
- Research Institute of Advanced Technologies, Ningbo University, Ningbo, China
- China Innovation Instrument Company, Ningbo, China
| | - La Chen
- Research Institute of Advanced Technologies, Ningbo University, Ningbo, China
- China Innovation Instrument Company, Ningbo, China
| | - Jianqin Gan
- China Innovation Instrument Company, Ningbo, China
| | - Gangqiang Li
- Research Institute of Advanced Technologies, Ningbo University, Ningbo, China
- China Innovation Instrument Company, Ningbo, China
| | - Luhong Wen
- Research Institute of Advanced Technologies, Ningbo University, Ningbo, China
- China Innovation Instrument Company, Ningbo, China
- Hua Yue Enterprise Holdings, Guangzhou, China
| | - Shengyang Shi
- Research Institute of Advanced Technologies, Ningbo University, Ningbo, China
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Qu C, Li Y, Du S, Geng Y, Su M, Liu H. Raman spectroscopy for rapid fingerprint analysis of meat quality and security: Principles, progress and prospects. Food Res Int 2022; 161:111805. [DOI: 10.1016/j.foodres.2022.111805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/06/2022] [Accepted: 08/18/2022] [Indexed: 11/28/2022]
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Zhang D, Liang P, Chen W, Tang Z, Li C, Xiao K, Jin S, Ni D, Yu Z. Rapid field trace detection of pesticide residue in food based on surface-enhanced Raman spectroscopy. Mikrochim Acta 2021; 188:370. [PMID: 34622367 DOI: 10.1007/s00604-021-05025-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/19/2021] [Indexed: 12/17/2022]
Abstract
Surface-enhanced Raman spectroscopy is an alternative detection tool for monitoring food security. However, there is still a lack of a conclusion of SERS detection with respect to pesticides and real sample analysis, and the summary of intelligent algorithms in SERS is also a blank. In this review, a comprehensive report of pesticides detection using SERS technology is given. The SERS detection characteristics of different types of pesticides and the influence of substrate on inspection are discussed and compared by the typical ways of classification. The key points, including the progress in real sample analysis and Raman data processing methods with intelligent algorithm, are highlighted. Lastly, major challenges and future research trends of SERS analysis of pesticide residue are also addressed. SERS has been proven to be a powerful technique for rapid test of residue pesticides in complex food matrices, but there still is a tremendous development space for future research.
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Affiliation(s)
- De Zhang
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China.
| | - Wenwen Chen
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhexiang Tang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Chen Li
- Jiangxi Sericulture and Tea Research Institute, Nanchang, 330203, China
| | - Kunyue Xiao
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shangzhong Jin
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Dejiang Ni
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhi Yu
- College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China.
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