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Liu X, Pant U, Logan N, He Q, Greer B, Elliott CT, Cao C. Non-linear responses via agglomeration and aggregation of gold nanoparticles for surface-enhanced Raman spectroscopy (SERS) coupled with chemometric analysis for chlorpyrifos detection. Food Chem 2024; 455:139944. [PMID: 38850989 DOI: 10.1016/j.foodchem.2024.139944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
This study investigates the behaviour of gold nanoparticles (AuNPs) when exposed to chlorpyrifos, an agricultural pesticide, and its application in detecting the pesticide via surface-enhanced Raman spectroscopy (SERS). Under synergistic addition of NaCl, AuNPs undergo agglomeration at lower chlorpyrifos concentrations but aggregation at higher concentrations, resulting in a distinctive nonlinear SERS response. A linear relationship is obtained between 0.001 and 1 ppm with detection limit (LOD) of 0.009 ppm, while an inverse response is observed at higher concentrations (1-1000 ppm) with a LOD of 1 ppm. Combining the colorimetric response of AuNP solutions, their absorbance spectra, and principal component analysis can improve detection reliability. The assay, coupled with a simple recovery method using acetonitrile swabbing, achieves high reproducibility in detecting chlorpyrifos in cucumber, even at concentrations as low as 0.11 ppm. This approach can be tailored for various chlorpyrifos concentrations not only in cucumbers but also in different food matrices.
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Affiliation(s)
- Xiaotong Liu
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom
| | - Udit Pant
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom
| | - Natasha Logan
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom
| | - Qiqi He
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom
| | - Brett Greer
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom
| | - Christopher T Elliott
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University (Rangsit Campus), Khlong Luang, Pathum Thani 12120, Thailand
| | - Cuong Cao
- Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, United Kingdom; Material and Advanced Technologies for Healthcare, Queen's University of Belfast, - 18-30 Malone Road Belfast, BT9 5DL, United Kingdom.
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2
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Yang H, Qian H, Xu Y, Zhai X, Zhu J. A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea. Foods 2024; 13:2363. [PMID: 39123554 PMCID: PMC11311742 DOI: 10.3390/foods13152363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography-mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea.
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Affiliation(s)
- Hanhua Yang
- School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
| | - Hao Qian
- School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
| | - Yi Xu
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China;
| | - Xiaodong Zhai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China;
| | - Jiaji Zhu
- School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
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3
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Averkiev A, Rodriguez RD, Fatkullin M, Lipovka A, Yang B, Jia X, Kanoun O, Sheremet E. Towards solving the reproducibility crisis in surface-enhanced Raman spectroscopy-based pesticide detection. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 935:173262. [PMID: 38768719 DOI: 10.1016/j.scitotenv.2024.173262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/08/2024] [Accepted: 05/13/2024] [Indexed: 05/22/2024]
Abstract
Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.
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Affiliation(s)
| | | | | | - Anna Lipovka
- Tomsk Polytechnic University, Lenina ave. 30, Tomsk, Russia
| | - Bin Yang
- School of Chemistry and Chemical Engineering/State Key Laboratory Incubation Base for Green Processing of Chemical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China
| | - Xin Jia
- School of Chemistry and Chemical Engineering/State Key Laboratory Incubation Base for Green Processing of Chemical Engineering, Shihezi University, Shihezi, Xinjiang 832003, China.
| | - Olfa Kanoun
- Professorship of Measurement and Sensor Technology, Chemnitz University of Technology, Chemnitz 09126, Germany
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4
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Holden CA, McAinsh MR, Taylor JE, Beckett P, Albacete A, Martínez-Andújar C, Morais CLM, Martin FL. Attenuated total reflection Fourier-transform infrared spectroscopy for the prediction of hormone concentrations in plants. Analyst 2024; 149:3380-3395. [PMID: 38712606 DOI: 10.1039/d3an01817b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Plant hormones are important in the control of physiological and developmental processes including seed germination, senescence, flowering, stomatal aperture, and ultimately the overall growth and yield of plants. Many currently available methods to quantify such growth regulators quickly and accurately require extensive sample purification using complex analytic techniques. Herein we used ultra-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) to create and validate the prediction of hormone concentrations made using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectral profiles of both freeze-dried ground leaf tissue and extracted xylem sap of Japanese knotweed (Reynoutria japonica) plants grown under different environmental conditions. In addition to these predictions made with partial least squares regression, further analysis of spectral data was performed using chemometric techniques, including principal component analysis, linear discriminant analysis, and support vector machines (SVM). Plants grown in different environments had sufficiently different biochemical profiles, including plant hormonal compounds, to allow successful differentiation by ATR-FTIR spectroscopy coupled with SVM. ATR-FTIR spectral biomarkers highlighted a range of biomolecules responsible for the differing spectral signatures between growth environments, such as triacylglycerol, proteins and amino acids, tannins, pectin, polysaccharides such as starch and cellulose, DNA and RNA. Using partial least squares regression, we show the potential for accurate prediction of plant hormone concentrations from ATR-FTIR spectral profiles, calibrated with hormonal data quantified by UHPLC-HRMS. The application of ATR-FTIR spectroscopy and chemometrics offers accurate prediction of hormone concentrations in plant samples, with advantages over existing approaches.
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Affiliation(s)
- Claire A Holden
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | - Martin R McAinsh
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | - Jane E Taylor
- Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
| | | | - Alfonso Albacete
- Institute for Agro-Environmental Research and Development of Murcia (IMIDA), Department of Plant Production and Agrotechnology, C/ Mayor s/n, La Alberca, E-30150 Murcia, Spain
- CEBAS-CSIC, Department of Plant Nutrition, Campus Universitario de Espinardo, E-30100 Murcia, Spain
| | | | - Camilo L M Morais
- Center for Education, Science and Technology of the Inhamuns Region, State University of Ceará, Tauá 63660-000, Brazil
- Graduate Program in Chemistry, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal 59072-970, Brazil
| | - Francis L Martin
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK.
- Biocel UK Ltd, Hull HU10 6TS, UK
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Kumar K, M MS, Kumar P, Munjal R, Mukhopadhyay S, Mondal DP, Khan MA, Vandana V. Detection of water pollutants using super-hydrophobic porous silicon-based SERS substrates. Mikrochim Acta 2024; 191:357. [PMID: 38814503 DOI: 10.1007/s00604-024-06425-x] [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: 03/22/2024] [Accepted: 05/09/2024] [Indexed: 05/31/2024]
Abstract
Super hydrophobic porous silicon surface is prepared using a wet chemical synthesis route. Scanning electron microscopic investigation confirms a correlation between pore size and reaction time. SERS substrates are prepared by silver nanoparticle deposition on porous silicon surface. They exhibit excellent characteristics in terms of sensitivity, reproducibility, stability, and uniformity. They could detect rhodamine 6G in femtomolar range with SERS enhancement factor of ~ 6.1 × 1012, which is best ever reported for these substrates. Molecule-specific sensing of water pollutants such as methylene blue, glyphosate, and chlorpyrifos, is demonstrated for concentrations well below their permissible limits along with excellent enhancement factors. Porous silicon substrate functionalized with Ag nanoparticles demonstrates to be a promising candidate for low-cost, long-life, reliable sensors for environmental conservation applications.
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Affiliation(s)
- Keshendra Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Industrial Waste Utilization, Nano and Biomaterials Division, CSIR - Advanced Materials and Processes Research Institute (CSIR-AMPRI), Bhopal, 462026, India
| | - Mohd Shafeeq M
- Alloy Composites and Cellular Materials Division, CSIR - Advanced Materials and Processes Research Institute (CSIR-AMPRI), Bhopal, 462026, India
| | - Pradip Kumar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Green Engineered Materials and Additive Manufacturing Division, CSIR - Advanced Materials and Processes Research Institute (CSIR-AMPRI), Bhopal, 462026, India
| | - Ritika Munjal
- Department of Chemistry, School of Basic Sciences, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India
| | - Suman Mukhopadhyay
- Department of Chemistry, School of Basic Sciences, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India
| | - Dehi Pada Mondal
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Alloy Composites and Cellular Materials Division, CSIR - Advanced Materials and Processes Research Institute (CSIR-AMPRI), Bhopal, 462026, India
| | - Mohd Akram Khan
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- Industrial Waste Utilization, Nano and Biomaterials Division, CSIR - Advanced Materials and Processes Research Institute (CSIR-AMPRI), Bhopal, 462026, India
| | - Vandana Vandana
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
- Industrial Waste Utilization, Nano and Biomaterials Division, CSIR - Advanced Materials and Processes Research Institute (CSIR-AMPRI), Bhopal, 462026, India.
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He P, Dumont E, Göksel Y, Slipets R, Schmiegelow K, Chen Q, Zor K, Boisen A. SERS mapping combined with chemometrics, for accurate quantification of methotrexate from patient samples. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123536. [PMID: 37862841 DOI: 10.1016/j.saa.2023.123536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/28/2023] [Accepted: 10/12/2023] [Indexed: 10/22/2023]
Abstract
Despite the technological development in Raman instrumentation that has democratized access to 2D sample scanning capabilities, most quantitative surface-enhanced Raman scattering (SERS) analyses are still performed by only acquiring a single or a few spectra per sample and performing univariate data analysis on those. This strategy can however reach its limit when analytes need to be detected and quantified in complex matrices. In that case, surface fouling and competition between the target analyte and interfering compounds can impair univariate SERS data analysis, underlining the need for a more in-depth data analysis strategy based on exploiting of full-spectrum information. In this paper, a multivariate data analysis strategy was developed, for analyzing SERS maps of methotrexate (MTX) from patient samples, including all steps from baseline correction, selection of wavelength, and the relevant pixels in the map (image threshold segmentation), as well as quantitative model construction based on partial-least squares regression. Among the different baseline correction methods evaluated, standard normal variable transformation and Savitzky-Golay smoothing proved to be more suitable, while the genetic algorithm wavelength screening method was able to screen out MTX-related SERS spectral regions more efficiently. Importantly, with the here-developed process, it was sufficient to use MTX-spiked commercial serum when building quantitative models, removing the need to work with MTX-spiked patient samples, and consequently enabling time- and resource-saving quantitative analyses. Besides, the developed multivariate data analysis approach showed superior performances compared with univariate analysis, with 30 % improved sensitivity (detection limit of 5.7 µM), 25 % higher reproducibility (average relative standard variation of 15.6 %), and 110 % better accuracy (average prediction error of -10.5 %).
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Affiliation(s)
- Peihuan He
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, PR China; School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; School of Materials Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, PR China
| | - Elodie Dumont
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; BioInnovation Institute Foundation, Copenhagen N, 2200, Denmark.
| | - Yaman Göksel
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; BioInnovation Institute Foundation, Copenhagen N, 2200, Denmark
| | - Roman Slipets
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; BioInnovation Institute Foundation, Copenhagen N, 2200, Denmark
| | - Kjeld Schmiegelow
- Department of Paediatrics and Adolescent Medicine, Rigshospitalet University Hospital, Copenhagen 2100, Denmark
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Kinga Zor
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; BioInnovation Institute Foundation, Copenhagen N, 2200, Denmark
| | - Anja Boisen
- Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics (IDUN), Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark; BioInnovation Institute Foundation, Copenhagen N, 2200, Denmark
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7
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Chu BY, Lin C, Nie PC, Xia ZY. Research Status in the Use of Surface-Enhanced Raman Scattering (SERS) to Detect Pesticide Residues in Foods and Plant-Derived Chinese Herbal Medicines. Int J Anal Chem 2024; 2024:5531430. [PMID: 38250173 PMCID: PMC10798841 DOI: 10.1155/2024/5531430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/19/2023] [Accepted: 01/02/2024] [Indexed: 01/23/2024] Open
Abstract
Surface-enhanced Raman scattering (SERS) technology has unique advantages in the rapid detection of pesticides in plant-derived foods, leading to reduced detection limits and increased accuracy. Plant-derived Chinese herbal medicines have similar sources to plant-derived foods; however, due to the rough surfaces and complex compositions of herbal medicines, the detection of pesticide residues in this context continues to rely heavily on traditional methods, which are time consuming and laborious and are unable to meet market demands for portability. The application of flexible nanomaterials and SERS technology in this realm would allow rapid and accurate detection in a portable format. Therefore, in this review, we summarize the underlying principles and characteristics of SERS technology, with particular focus on applications of SERS for the analysis of pesticide residues in agricultural products. This paper summarizes recent research progress in the field from three main directions: sample pretreatment, SERS substrates, and data processing. The prospects and limitations of SERS technology are also discussed, in order to provide theoretical support for rapid detection of pesticide residues in Chinese herbal medicines.
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Affiliation(s)
- Bing-Yan Chu
- School of Pharmacy, Zhejiang University of Technology, Hangzhou 310014, China
- School of Medicine, Hangzhou City University, Hangzhou 310015, China
| | - Chi Lin
- School of Medicine, Hangzhou City University, Hangzhou 310015, China
| | - Peng-Cheng Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Zheng-Yan Xia
- School of Medicine, Hangzhou City University, Hangzhou 310015, China
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8
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Zhu M, Gao J, Chen Z, Sun X, Duan Y, Tian X, Gu J, Shi Q, Sun M. Au nano-cone array for SERS detection of associated miRNA in lymphoma patients. Mikrochim Acta 2023; 191:40. [PMID: 38110769 DOI: 10.1007/s00604-023-06095-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/08/2023] [Indexed: 12/20/2023]
Abstract
Based on Au nano-cone array (Au-NCA) and a three-segment hybridization strategy, a novel SERS biosensor is proposed for the ultrasensitive detection of the microRNA miR-21. The uniform, stable, and reproducible Au-NCA was prepared by the single-layer colloidal ball template method. Subsequently, the target was hybridized with sequence 2. The resulting target-sequence 2 complex was then hybridized with sequence 1 anchored on Au-NCA. Thus, a three-segment sequence complex was formed. SERS measurements can be performed without the need for complex purification and amplification steps. Due to the ability of miR-21 to perform specific complementary hybridization with two sequences, SERS biosensors have superior specificity for miR-21 without interference from other miRNAs. Under the optimal conditions, the SERS biosensor was applied and the limit of detection (LOD) was as low as 3.02 aM. This method has been successfully used to the detection of miR-21 in the serum of lymphoma patients and healthy volunteers. The results are consistent with the traditional test methods. Therefore, this novel SERS biosensor shows excellent clinical translational potential in the detection of lymphoma.
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Affiliation(s)
- Miao Zhu
- Department of Hematology, Clinical Medical College, Yangzhou University, Yangzhou, 225001, People's Republic of China
- Yangzhou Institute of Hematology, Yangzhou, 225001, People's Republic of China
| | - Junyan Gao
- Department of Pediatrics, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225001, People's Republic of China
| | - Zhiyue Chen
- Department of Hematology, Clinical Medical College, Yangzhou University, Yangzhou, 225001, People's Republic of China
| | - Xing Sun
- Yangzhou Institute of Hematology, Yangzhou, 225001, People's Republic of China
| | - Yu Duan
- Department of Nuclear Medicine, Clinical Medical College, Yangzhou University, Yangzhou, 225001, People's Republic of China
| | - Xiuchun Tian
- Department of Pathology, Clinical Medical College, Yangzhou University, Yangzhou, 225001, People's Republic of China
| | - Jian Gu
- Department of Hematology, Clinical Medical College, Yangzhou University, Yangzhou, 225001, People's Republic of China
| | - Qingqing Shi
- Yangzhou Institute of Hematology, Yangzhou, 225001, People's Republic of China.
| | - Mei Sun
- Department of Hematology, Clinical Medical College, Yangzhou University, Yangzhou, 225001, People's Republic of China.
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Dos Santos DP, Sena MM, Almeida MR, Mazali IO, Olivieri AC, Villa JEL. Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends. Anal Bioanal Chem 2023; 415:3945-3966. [PMID: 36864313 PMCID: PMC9981450 DOI: 10.1007/s00216-023-04620-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/20/2023] [Indexed: 03/04/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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Affiliation(s)
- Diego P Dos Santos
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil
| | - Marcelo M Sena
- Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, 31270-901, Brazil
- Instituto Nacional de Ciência e Tecnologia em Bioanalítica (INCT Bio), Campinas, SP, 13083-970, Brazil
| | - Mariana R Almeida
- Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, 31270-901, Brazil
| | - Italo O Mazali
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil
| | - Alejandro C Olivieri
- Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química Rosario (IQUIR-CONICET), Suipacha 531, 2000, Rosario, Argentina
| | - Javier E L Villa
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil.
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Chen Z, Sun Y, Shi J, Zhang W, Zhang X, Hang X, Li Z, Zou X. Convenient self-assembled PDADMAC/PSS/Au@Ag NRs filter paper for swift SERS evaluate of non-systemic pesticides on fruit and vegetable surfaces. Food Chem 2023; 424:136232. [PMID: 37207598 DOI: 10.1016/j.foodchem.2023.136232] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/05/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023]
Abstract
The goal of food safety supervision is to directly identify the pesticide residues on the surface of fruits and vegetables. This study proposed to develop a facile, non-destructive, and sensitive method based on surface-enhanced Raman scattering (SERS) to detect non-systemic pesticides on the surface of fruits and vegetables. The composite material was prepared by loading CTAB guided Au@Ag NRs with positive charge onto filter paper which was modified with PDADMAC(+) and PSS(-) using electrostatic adsorption. Au@Ag NRs with bimetallic synergies were effectively adsorbed in the fiber grid to generate 3D SERS hotspots within a few microns of depth. The results showed that the 3D composite flexible substrate had a high SERS activity, great repeatability, and sensitivity when the method was utilized to detect 4-MBA, methyl-parathion, thiram and chlorpyrifos. Three kinds of non-systemic pesticides on the peel could be detected directly and quickly owing to the arbitrary bending of the substrate, demonstrating the efficiency of the SERS "paste-reading" method. The acquired findings demonstrated that PDADMAC/PSS/Au@Ag NRs composite filter paper had the potential to provide rapid feedback for in-situ analysis of pesticide residues on the surface of fruit and vegetable.
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Affiliation(s)
- Zhiyang Chen
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Yue Sun
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Jiyong Shi
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Wen Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xinai Zhang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaowei Hang
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhihua Li
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
| | - Xiaobo Zou
- Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, @, Zhenjiang, Jiangsu 212013, China; China-UK Joint Laboratory for Nondestructive Detection of Agro-products, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
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11
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Li J, Zhang L, Zhu F, Song Y, Yu K, Zhao Y. Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 290:122221. [PMID: 36549243 DOI: 10.1016/j.saa.2022.122221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.
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Affiliation(s)
- Junmeng Li
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Liang Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Fengle Zhu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yuling Song
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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12
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Xiong Y, Huang J, Wu R, Geng X, Zuo H, Wang X, Xu L, Ai S. Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid Determination of Chlorpyrifos Residues in Rice. APPLIED SPECTROSCOPY 2023; 77:160-169. [PMID: 36368896 DOI: 10.1177/00037028221141728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), coupled with characteristic peak screening methods, was developed for analyzing chlorpyrifos (CM) pesticide residues in rice. Au nanoparticles (AuNPs) were prepared as Raman signal enhancement. Magnesium sulfate (MgSO4), primary secondary amine (PSA), and C18 were used to purify the rice extraction. A successive projections algorithm (SPA) was performed to identify the optimal characteristic peaks of CM in rice from full Raman spectroscopy. Support vector machine (SVM) and partial least squares (PLS) were implemented to investigate the quantitative analysis models. The results demonstrated that six Raman peaks such as 671, 834, 1016, 1114, 1436, and 1444 cm-1 were selected by the SPA and SVM models and had better performance using six peaks (only 0.92% of the full spectra variables) with R2p = 0.97, RMSEP = 2.89 and RPD = 4.26, and the experiment time for a sample was accomplished within 10 min. Recovery for five unknown concentration samples was 97.45-103.96%, and T-test results also displayed no obvious differences between the measured value and the predicted value. The study stated that SERS, combined with characteristic peak screening methods, can be applied to rapidly monitor the chlorpyrifos residue in rice.
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Affiliation(s)
- Yao Xiong
- College of Software, 91595Jiangxi Agricultural University, Nanchang, China
| | - Junshi Huang
- College of Engineering, 91595Jiangxi Agricultural University, Nanchang, China
| | - Ruimei Wu
- College of Engineering, 91595Jiangxi Agricultural University, Nanchang, China
| | - Xiang Geng
- College of Food Science and Engineering, 91595Jiangxi Agricultural University, Nanchang, China
| | - Haigen Zuo
- School of Chemistry and Food Science, 118322Nanchang Normal University, Nanchang, China
| | - Xu Wang
- College of Food Science and Engineering, 91595Jiangxi Agricultural University, Nanchang, China
| | - Lulu Xu
- College of Software, 91595Jiangxi Agricultural University, Nanchang, China
| | - Shirong Ai
- College of Software, 91595Jiangxi Agricultural University, Nanchang, China
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13
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Zniber M, Vahdatiyekta P, Huynh TP. Analysis of urine using electronic tongue towards non-invasive cancer diagnosis. Biosens Bioelectron 2023; 219:114810. [PMID: 36272349 DOI: 10.1016/j.bios.2022.114810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 04/27/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.
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Affiliation(s)
- Mohammed Zniber
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, 20500, Turku, Finland
| | - Parastoo Vahdatiyekta
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, 20500, Turku, Finland
| | - Tan-Phat Huynh
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, 20500, Turku, Finland.
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14
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SERS-based sensor coupled with multivariate models for rapid detection of palm oil adulteration with Sudan II and IV dyes. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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15
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Tian H, Feng Y, Yang X, Li S, Pang C, Ma C. Development of a new and facile method for determination of chlorpyrifos residues in green tea by dispersive liquid-liquid microextraction. Sci Rep 2022; 12:15542. [PMID: 36109661 PMCID: PMC9477813 DOI: 10.1038/s41598-022-20021-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/07/2022] [Indexed: 11/21/2022] Open
Abstract
In this work a simple, rapid, and environmentally friendly method has been established for the determination of chlorpyrifos residue in green tea by dispersive liquid-liquid microextraction and gas chromatography-flame photometric detection. Some experimental parameters that influence extraction efficiency, such as the kind and volume of disperser solvents and extraction solvents, extraction time, addition of salt and pH, were investigated. And the optimal experimental conditions were obtained, quantitative analysis was carried out using external standard method. The correlation coefficient of the calibration curves was 0.999 with in 0.05 mg/kg to 5 mg/kg. The results showed that under the optimum conditions, the enrichment factors of the chlorpyrifos was about 554.51, the recoveries for standard addition fell in the range from 91.94 to 104.70% and the relative standard deviations was 4.61%. The limit of quantification of chlorpyrifos in green tea was 0.02 μg/mL at the signal/noise ratio of 3.
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Affiliation(s)
- Hai Tian
- Analysis and Testing Center, Chinese Academy of Tropical Agricultural Science & Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Haikou, China
| | - Yujie Feng
- Institute of Plant Protection, Ministry of Agriculture, Hainan Academy of Agricultural Science & Scientific Observation and Experiment Station of Crop Pests in HaiKou, Haikou, China.
| | - Xinfeng Yang
- Analysis and Testing Center, Chinese Academy of Tropical Agricultural Science & Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Haikou, China
| | - Shuhuai Li
- Analysis and Testing Center, Chinese Academy of Tropical Agricultural Science & Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Haikou, China
| | - Chaohai Pang
- Analysis and Testing Center, Chinese Academy of Tropical Agricultural Science & Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Haikou, China
| | - Chen Ma
- Analysis and Testing Center, Chinese Academy of Tropical Agricultural Science & Hainan Provincial Key Laboratory of Quality and Safety for Tropical Fruits and Vegetables, Haikou, China
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16
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He Y, Xu W, Qu M, Zhang C, Wang W, Cheng F. Recent advances in the application of Raman spectroscopy for fish quality and safety analysis. Compr Rev Food Sci Food Saf 2022; 21:3647-3672. [PMID: 35794726 DOI: 10.1111/1541-4337.12968] [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: 01/28/2022] [Revised: 03/29/2022] [Accepted: 04/14/2022] [Indexed: 11/27/2022]
Abstract
Fish is one of the highly demanded aquatic products, and its quality and safety play a pivotal role in daily diet. However, the possible hazardous substance in perishable fish both in pre- and postharvest periods may decrease their values and pose a threat to public health. Laborious and expensive traditional methods drive the need of developing effective tools for detecting fish quality and safety properties in a rapid, nondestructive, and effective manner. Recent advances in Raman spectroscopy (RS) and surface-enhanced Raman scattering (SERS) have shown enormous potential in various aspects, which largely boost their applications in fish quality and safety evaluation. They have incomparable merits such as providing molecule fingerprint information and allowing for rapid, sensitive, and noninvasive detection with simple sample preparation. This review provides a comprehensive overview focusing on the applications of RS and SERS for fish quality assessment and safety inspection, highlighting the hazardous substance and illegal behavior both in preharvest (veterinary drug residues and environmental pollutants) and postharvest (freshness and illegal behavior) particularly. Moreover, challenges and prospects are also proposed to facilitate the vigorous development of RS and SERS. This review is aimed to emphasize potential opportunities for applying RS and SERS as promising techniques for routine food quality and safety detection. PRACTICAL APPLICATION: With these applications, it can be clearly indicated that RS and SERS are promising and powerful in fish quality and safety surveillance, thereby reducing the occurrence of commercial fraud and food safety issues. More efforts still should be concentrated on exploiting the high-performance Raman instruments, establishing a universal Raman database, developing reproducible SERS substrates and combing RS with other versatile spectral techniques to promote these technologies from laboratory to practice. It is hoped that this review should arouse more research interests in RS and SERS technologies for fish quality and safety surveillance, as well as provide more insights to make a breakthrough.
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Affiliation(s)
- Yingchao He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
| | - Weidong Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Maozhen Qu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
| | - Chao Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
| | - Wenjun Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Hangzhou, China
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of On Site Processing Equipment for Agricultural Products of Ministry of Agriculture and Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou, China
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17
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Barimah AO, Chen P, Yin L, El-Seedi HR, Zou X, Guo Z. SERS nanosensor of 3-aminobenzeneboronic acid labeled Ag for detecting total arsenic in black tea combined with chemometric algorithms. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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18
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An Improved POD Model for Fast Semi-Quantitative Analysis of Carbendazim in Fruit by Surface Enhanced Raman Spectroscopy. Molecules 2022; 27:molecules27134230. [PMID: 35807472 PMCID: PMC9268280 DOI: 10.3390/molecules27134230] [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: 06/08/2022] [Revised: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 12/10/2022] Open
Abstract
The current detection method of carbendazim suffers from the disadvantages of complicated preprocessing and long cycle time. In order to solve the problem of rapid quantitative screening of finite contaminants, this article proposed a qualitative method based on characteristic peaks and a semi-quantitative method based on threshold to detect carbendazim in apple, and finally the method is evaluated by a validation system based on binary output. The results showed that the detection limit for carbendazim was 0.5 mg/kg, and the detection probability was 100% when the concentration was no less than 1 mg/kg. The semi-quantitative analysis method had a false positive rate of 0% and 5% at 0.5 mg/kg and 2.5 mg/kg, respectively. The results of method evaluation showed that when the added concentration was greater than 2.5 mg/kg, the qualitative detection method was consistent with the reference method. When the concentration was no less than 5 mg/kg, the semi-quantitative method is consistent between different labs. The semi-quantitative method proposed in this study can achieve the screening of finite contaminants in blind samples and simplify the test validation process through the detection probability model, which can meet the needs of rapid on-site detection and has a good application prospect.
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19
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Azimi S, Docoslis A. Recent Advances in the Use of Surface-Enhanced Raman Scattering for Illicit Drug Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:3877. [PMID: 35632286 PMCID: PMC9143835 DOI: 10.3390/s22103877] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/10/2022] [Accepted: 05/16/2022] [Indexed: 02/07/2023]
Abstract
The rapid increase in illicit drug use and its adverse health effects and socio-economic consequences have reached alarming proportions in recent years. Surface-enhanced Raman scattering (SERS) has emerged as a highly sensitive analytical tool for the detection of low dosages of drugs in liquid and solid samples. In the present article, we review the state-of-the-art use of SERS for chemical analysis of illicit drugs in aqueous and complex biological samples, including saliva, urine, and blood. We also include a review of the types of SERS substrates used for this purpose, pointing out recent advancements in substrate fabrication towards quantitative and qualitative detection of illicit drugs. Finally, we conclude by providing our perspective on the field of SERS-based drug detection, including presently faced challenges. Overall, our review provides evidence of the strong potential of SERS to establish itself as both a laboratory and in situ analytical method for fast and sensitive drug detection and identification.
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Affiliation(s)
| | - Aristides Docoslis
- Department of Chemical Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada;
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20
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Haruna SA, Li H, Zareef M, Mehedi Hassan M, Arslan M, Geng W, Wei W, Abba Dandago M, Yao-Say Solomon Adade S, Chen Q. Application of NIR spectroscopy for rapid quantification of acid and peroxide in crude peanut oil coupled multivariate analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120624. [PMID: 34824004 DOI: 10.1016/j.saa.2021.120624] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/11/2021] [Accepted: 11/10/2021] [Indexed: 06/13/2023]
Abstract
Two key parameters (acidity and peroxide content) for evaluation of the oxidation level in crude peanut oil have been studied. The titrimetric analysis was carried out for reference data collection. Then, near-infrared spectroscopy in combination with chemometric algorithms such as partial least square (PLS); bootstrapping soft shrinkage-PLS (BOSS-PLS); uninformative variable elimination-PLS (UVE-PLS), and competitive-adaptive reweighted sampling-PLS (CARS-PLS) were attempted and assessed. The correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to individually evaluate the performance of the models. Optimum results were noticed with CARS-PLS, 0.9517 ≤ Rc ≤ 0.9670, 0.9503 ≤ Rp ≤ 0.9637, 0.0874 ≤ RMSEP ≤ 0.5650, and 3.14 ≤ RPD ≤ 3.64. Therefore, this affirmed that the near-infrared spectroscopy coupled with CARS-PLS could be used as a simple, fast, and non-invasive technique for quantifying acid value and peroxide value in crude peanut oil.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P.M.B 3244, Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Arslan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Munir Abba Dandago
- Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P.M.B 3244, Kano, Kano State, Nigeria
| | | | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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21
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WANG J, WU X, ZHENG J, WU B. Rapid identification of green tea varieties based on FT-NIR spectroscopy and LDA/QR. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.73022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
| | - Xiaohong WU
- Jiangsu University, China; Jiangsu University, China
| | | | - Bin WU
- Chuzhou Polytechnic, China
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22
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Naqvi SMZA, Zhang Y, Ahmed S, Abdulraheem MI, Hu J, Tahir MN, Raghavan V. Applied surface enhanced Raman Spectroscopy in plant hormones detection, annexation of advanced technologies: A review. Talanta 2022; 236:122823. [PMID: 34635213 DOI: 10.1016/j.talanta.2021.122823] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/20/2021] [Accepted: 08/22/2021] [Indexed: 12/13/2022]
Abstract
Plant hormones are the molecules that control the vigorous development of plants and help to cope with the stress conditions efficiently due to vital and mechanized physiochemical regulations. Biologists and analytical chemists, both endorsed the extreme problems to quantify plant hormones due to their low level existence in plants and the technological support is devastatingly required to established reliable and efficient detection methods of plant hormones. Surface Enhanced Raman Spectroscopy (SERS) technology is becoming vigorously favored and can be used to accurately and specifically identify biological and chemical molecules. Subsistence molecular properties with varying excitation wavelength require the pertinent substrate to detect SERS signals from plant hormones. Three typical mechanisms of Raman signal enhancement have been discovered, electromagnetic, chemical and Tip-enhanced Raman spectroscopy (TERS). Though, complex detection samples hinder in consistent and reproducible results of SERS-based technology. However, different algorithmic models applied on preprocessed data enhanced the prediction performances of Raman spectra by many folds and decreased the fluorescence value. By incorporating SERS measurements into the microfluidic platform, further highly repeatable SERS results can be obtained. This review paper tends to study the fundamental working principles, methods, applications of SERS systems and their execution in experiments of rapid determination of plant hormones as well as several ways of integrated SERS substrates. The challenges to develop an SERS-microfluidic framework with reproducible and accurate results for plant hormone detection are discussed comprehensively and highlighted the key areas for future investigation briefly.
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Affiliation(s)
- Syed Muhammad Zaigham Abbas Naqvi
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Yanyan Zhang
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Shakeel Ahmed
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Mukhtar Iderawumi Abdulraheem
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China; Oyo State College of Education, Lanlate, 202001, Nigeria.
| | - Jiandong Hu
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Muhammad Naveed Tahir
- Department of Agronomy, PMAS-Arid Agriculture University Rawalpindi, 46300, Pakistan.
| | - Vijaya Raghavan
- Department of Bioresource Engineering, Faculty of Agriculture and Environmental Studies, McGill University, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada
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23
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Pan H, Ahmad W, Jiao T, Zhu A, Ouyang Q, Chen Q. Label-free Au NRs-based SERS coupled with chemometrics for rapid quantitative detection of thiabendazole residues in citrus. Food Chem 2021; 375:131681. [PMID: 34863601 DOI: 10.1016/j.foodchem.2021.131681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 12/17/2022]
Abstract
Citrus is a highly consumed fruit worldwide. However, the excessive use of thiabendazole (TBZ) pesticides during citrus cultivation poses a health risk to people. Hence, a rapid and quantitative method has been established for TBZ determination in citrus by coupling gold nanorods (Au NRs) based surface enhanced Raman scattering (SERS) coupling chemometrics. The results show that support vector machine (SVM) can distinguish TBZ residues of different orders of magnitude with 99.1667% accuracy and that genetic algorithm-partial least squares (GA-PLS) had the best performance in the quantitative prediction of TBZ residues (Rp2 = 0.9737, RMSEP = 0.1179 and RPD = 5.85) in citrus. The limit of detection (LOD) was 0.33 μg mL-1. Furthermore, the proposed method was validated by a standard HPLC method using t-test with no significant difference. Therefore, the proposed Au NRs-based SERS technique can be used for the rapid quantitative analysis of TBZ residues in citrus.
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Affiliation(s)
- Haihui Pan
- School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Waqas Ahmad
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Tianhui Jiao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Afang Zhu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
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24
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Jiang L, Mehedi Hassan M, Jiao T, Li H, Chen Q. Rapid detection of chlorpyrifos residue in rice using surface-enhanced Raman scattering coupled with chemometric algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 261:119996. [PMID: 34091354 DOI: 10.1016/j.saa.2021.119996] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 06/12/2023]
Abstract
Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01-1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t-test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice.
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Affiliation(s)
- Lan Jiang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Tianhui Jiao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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25
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Au@Ag nanoflowers based SERS coupled chemometric algorithms for determination of organochlorine pesticides in milk. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111978] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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26
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Kang W, Lin H, Jiang H, Yao-Say Solomon Adade S, Xue Z, Chen Q. Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review. Compr Rev Food Sci Food Saf 2021; 20:5145-5172. [PMID: 34409725 DOI: 10.1111/1541-4337.12823] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/24/2021] [Accepted: 07/06/2021] [Indexed: 01/18/2023]
Abstract
Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.
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Affiliation(s)
- Wencui Kang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Hao Jiang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | | | - Zhaoli Xue
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
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27
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Rong Y, Hassan MM, Ouyang Q, Chen Q. Lanthanide ion (Ln 3+ )-based upconversion sensor for quantification of food contaminants: A review. Compr Rev Food Sci Food Saf 2021; 20:3531-3578. [PMID: 34076359 DOI: 10.1111/1541-4337.12765] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 12/23/2022]
Abstract
The food safety issue has gradually become the focus of attention in modern society. The presence of food contaminants poses a threat to human health and there are a number of interesting researches on the detection of food contaminants. Upconversion nanoparticles (UCNPs) are superior to other fluorescence materials, considering the benefits of large anti-Stokes shifts, high chemical stability, non-autofluorescence, good light penetration ability, and low toxicity. These properties render UCNPs promising candidates as luminescent labels in biodetection, which provides opportunities as a sensitive, accurate, and rapid detection method. This paper intended to review the research progress of food contaminants detection by UCNPs-based sensors. We have proposed the key criteria for UCNPs in the detection of food contaminants. Additionally, it highlighted the construction process of the UCNPs-based sensors, which includes the synthesis and modification of UCNPs, selection of the recognition elements, and consideration of the detection principle. Moreover, six kinds of food contaminants detected by UCNPs technology in the past 5 years have been summarized and discussed fairly. Last but not least, it is outlined that UCNPs have great potential to be applied in food safety detection and threw new insight into the challenges ahead.
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Affiliation(s)
- Yawen Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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28
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Jiang H, He Y, Chen Q. Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3448-3456. [PMID: 33270243 DOI: 10.1002/jsfa.10975] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/17/2020] [Accepted: 12/03/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND The edible oil storage period is one of the important indicators for evaluating the intrinsic quality of edible oil. The present study aimed to develop a portable electronic nose device for the qualitative identification of the edible oil storage period. First, four metal oxide semiconductor gas sensors, comprising TGS2600, TGS2611, TGS2620 and MQ138, were selected to prepare a sensor array to assemble a portable electronic nose device. Second, the homemade portable electronic nose device was used to obtain the odor change information of edible oil samples during different storage periods, and the sensor features were extracted. Finally, three pattern recognition methods, comprising linear discriminant analysis (LDA), K-nearest neighbors (KNN) and support vector machines (SVM), were compared to establish a qualitative identification model of the edible oil storage period. The input features and related parameters of the model were optimized by a five-fold cross-validation during the process of model establishment. RESULTS The research results showed that the recognition performance of the non-linear SVM model was significantly better than that of the linear LDA and KNN models, especially in terms of generalization performance, which had a correct recognition rate of 100% when predicting independent samples in the prediction set. CONCLUSION The overall results demonstrate that it is feasible to apply the homemade portable electronic nose device with the help of the appropriate pattern recognition methods to achieve the fast and efficient identification of the edible oil storage period, which provides an effective analysis tool for the quality detection of the edible oil storage. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
| | - Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, China
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29
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Liu S, Li H, Hassan MM, Ali S, Chen Q. SERS based artificial peroxidase enzyme regulated multiple signal amplified system for quantitative detection of foodborne pathogens. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107733] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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30
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Sun R, Yang W, Li Y, Sun C. Multi-residue analytical methods for pesticides in teas: a review. Eur Food Res Technol 2021. [DOI: 10.1007/s00217-021-03765-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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31
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Wang J, Chen Q, Belwal T, Lin X, Luo Z. Insights into chemometric algorithms for quality attributes and hazards detection in foodstuffs using Raman/surface enhanced Raman spectroscopy. Compr Rev Food Sci Food Saf 2021; 20:2476-2507. [DOI: 10.1111/1541-4337.12741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/08/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Jingjing Wang
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering Jiangsu University Zhenjiang People's Republic of China
| | - Tarun Belwal
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, Key Laboratory of Agro‐Products Postharvest Handling of Ministry of Agriculture and Rural Affairs, Zhejiang Key Laboratory for Agri‐Food Processing, National‐Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment Zhejiang University Hangzhou People's Republic of China
- Ningbo Research Institute Zhejiang University Ningbo People's Republic of China
- Fuli Institute of Food Science Hangzhou People's Republic of China
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32
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Rapid detection of mercury in food via rhodamine 6G signal using surface-enhanced Raman scattering coupled multivariate calibration. Food Chem 2021; 358:129844. [PMID: 33940287 DOI: 10.1016/j.foodchem.2021.129844] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 03/18/2021] [Accepted: 04/07/2021] [Indexed: 11/22/2022]
Abstract
Considering food safety and limitations of biorecognition elements, this study focused on the development of a novel method for predicting mercury (Hg2+) in fish and water samples using surface-enhanced Raman scattering (SERS) coupled wavenumber selection chemometric method. Herein, core-shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as SERS substrate, and rhodamine 6G (R6G) was used as signaling probe for Hg2+. In the presence of Hg2+, citrate ion of Au@Ag NPs induced complexation and become amalgam causes desorption of R6G occurred, resulted in decreased SERS signal intensity. Compared to surface Plasmon resonance method, SERS coupled genetic algorithm-partial least squares realized good correlation coefficient (0.9745 and 0.9773) in their prediction over the concentration ranges 1.0 × 102 to 1.0 × 10-3 µg/g. The recovery (88.45 - 94.73%) and precision (coefficient of variations, 3.28 - 5.76%) exhibiting satisfactory results suggested that the proposed method could be employed to predict Hg2+ in fish and water samples towards quality and safety monitoring.
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33
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Jiang H, He Y, Xu W, Chen Q. Quantitative Detection of Acid Value During Edible Oil Storage by Raman Spectroscopy: Comparison of the Optimization Effects of BOSS and VCPA Algorithms on the Characteristic Raman Spectra of Edible Oils. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01939-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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34
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Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9030050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.
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35
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Real-coded GA coupled to PLS for rapid detection and quantification of tartrazine in tea using FT-IR spectroscopy. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2020.110583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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36
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Xu Y, Hassan MM, Ali S, Li H, Ouyang Q, Chen Q. Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:1667-1674. [PMID: 33522812 DOI: 10.1021/acs.jafc.0c06513] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Pesticide residues in food have been a grave concern to consumers. Herein, we have developed a dual-mode SERS chip using Cu2O mesoporous spheres decorated with Ag nanoparticles (MCu2O@Ag NPs) as both sensing and degradation/clearing unit for rapid detection of pymetrozine and thiram pesticides in tea samples. Three kinds of chemometric algorithms were comparatively applied to analyze the collected SERS spectra of pesticides. In comparison, random frog-partial least squares achieved the best performance with root mean square error of prediction and residual predictive deviation values of 0.9871, 6.17, and 0.9873, 6.64 for pymetrozine and thiram, respectively. Additionally, the prepared SERS chip showed great photocatalytic activity to degrade pesticides under visible light irradiation. Through a facile method, this work presented a novel dual-functional SERS chip for the rapid detection and degradation of low-concentration pesticides in both environmental and food samples.
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Affiliation(s)
- Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Shujat Ali
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang212013, People's Republic of China
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37
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Anjos O, Caldeira I, Pedro SI, Canas S. FT-Raman methodology applied to identify different ageing stages of wine spirits. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.110179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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38
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Zareef M, Mehedi Hassan M, Arslan M, Ahmad W, Ali S, Ouyang Q, Li H, Wu X, Chen Q. Rapid prediction of caffeine in tea based on surface-enhanced Raman spectroscopy coupled multivariate calibration. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105431] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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39
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Duan N, Qi S, Guo Y, Xu W, Wu S, Wang Z. Fe3O4@Au@Ag nanoparticles as surface-enhanced Raman spectroscopy substrates for sensitive detection of clenbuterol hydrochloride in pork with the use of aptamer binding. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.110017] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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40
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Chen Q, Jiao T, Yang M, Li H, Ahmad W, Hassan MM, Guo Z, Ali S. Pre etched Ag nanocluster as SERS substrate for the rapid quantification of AFB1 in peanut oil via DFT coupled multivariate calibration. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 239:118411. [PMID: 32474366 DOI: 10.1016/j.saa.2020.118411] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 04/11/2020] [Accepted: 04/22/2020] [Indexed: 06/11/2023]
Abstract
The current study extends the use of surface-enhanced Raman spectroscopy (SERS) combined with density functional theory (DFT) and multivariate calibration towards the rapid quantification of aflatoxin B1 (AFB1) in peanut oil samples. It reports the design of pre etched Ag nanocluster as an active SERS substrate for quantifying AFB1, after being impregnated on its surface. The SERS spectra of AFB1@pre etched Ag nanocluster was recorded and its respective theoretical spectrum was calculated by density functional theory (DFT) to assign the characteristic peaks. The baseline drift and rotation effects were masked by the first-order derivative preprocessing method followed by multivariate calibration. The BP-AdaBoost model exhibited optimum prediction (Rp = 0.9283 and 0.9332) ability over the concentration range 5-100 and 100-1000 ngmL-1, respectively. The limit of detection calculated was 5.0 ngmL-1 and the obtained recoveries were in the range from 90.4% to 113.1% in spiked peanut oil samples. Additionally, precision analysis revealed an RSD ca. 5%, suggesting the applicability of the pre etched Ag nanocluster SERS substrate towards AFB1 detection. Thus, the proposed SERS platform exploiting DFT and BP-AdaBoost model was found reproducible for the quantification of AFB1 in peanut oil.
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Affiliation(s)
- Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Tianhui Jiao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Mingxiu Yang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Waqas Ahmad
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Zhiming Guo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Shujat Ali
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
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41
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Lin X, Sun DW. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.06.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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42
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Signal optimized rough silver nanoparticle for rapid SERS sensing of pesticide residues in tea. Food Chem 2020; 338:127796. [PMID: 32805691 DOI: 10.1016/j.foodchem.2020.127796] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/24/2020] [Accepted: 08/06/2020] [Indexed: 01/19/2023]
Abstract
Trace detection of toxic chemicals in foodstuffs is of great concern in recent years. Surface-enhanced Raman scattering (SERS) has drawn significant attention in the monitoring of food safety due to its high sensitivity. This study synthesized signal optimized flower-like silver nanoparticle-(AgNP) with EF at 25 °C of 1.39 × 106 to extend the SERS application for pesticide sensing in foodstuffs. The synthesized AgNP was deployed as SERS based sensing platform to detect methomyl, acetamiprid-(AC) and 2,4-dichlorophenoxyacetic acid-(2,4-D) residue levels in green tea via solid-phase extraction. A linear correlation was twigged between the SERS signal and the concentration for methomyl, AC and 2,4-D with regression coefficient of 0.9974, 0.9956 and 0.9982 and limit of detection of 5.58 × 10-4, 1.88 × 10-4 and 4.72 × 10-3 µg/mL, respectively; the RSD value < 5% was recorded for accuracy and precision analysis suggesting that proposed method could be deployed for the monitoring of methomyl, AC and 2,4-D residue levels in green tea.
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43
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He Y, Jiang H, Chen Q. High-precision identification of the actual storage periods of edible oil by FT-NIR spectroscopy combined with chemometric methods. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:3722-3728. [PMID: 32729876 DOI: 10.1039/d0ay00779j] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The actual storage period of edible oil is one of the important indicators of edible oil quality. A high-precision identification method based on the near-infrared (NIR) spectroscopy technique for the actual storage period of edible oil is proposed in this study. Firstly, a Fourier transform NIR (FT-NIR) spectrometer was used to collect NIR spectra of edible oil samples in different storage periods, and the obtained spectra were pretreated by standard normal transformation (SNV). Then, the characteristics of the pretreated spectra were analyzed by principal component analysis (PCA), and the spatial distribution of edible oil samples in different storage periods was visually presented using a PCA score plot. Finally, three pattern recognition methods, which were K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were compared to establish a qualitative identification model of edible oil in different storage periods. The results showed that the recognition performance of the SVM model was significantly superior to that of the KNN and RF models, especially in terms of generalization performance, and the SVM model had a recognition rate of 100% when predicting independent samples in the prediction set. It is suggested that FT-NIR spectroscopy combined with appropriate chemometric methods is feasible to realize fast and high-precision identification of actual storage periods of edible oil and provided an effective analysis tool for edible oil storage quality detection.
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Affiliation(s)
- Yingchao He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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44
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Quantification of deltamethrin residues in wheat by Ag@ZnO NFs-based surface-enhanced Raman spectroscopy coupling chemometric models. Food Chem 2020; 337:127652. [PMID: 32799158 DOI: 10.1016/j.foodchem.2020.127652] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 11/22/2022]
Abstract
Deltamethrin, one of the most toxic pyrethroids, is commonly used to inhibit pests in wheat. However, the trace levels of deltamethrin in wheat is alarming to human health. In this study, surface-enhanced Raman spectroscopy (SERS)-active silver nanoparticles-plated-zinc oxide nanoflowers (Ag@ZnO NFs) nano-sensor were employed for rapid and sensitive quantification of deltamethrin in wheat. To sufficiently utilize the chemical-related information in SERS spectra, various spectral pretreatment and chemometric models were studied. The mean centering (MC) coupling successive projection algorithm-partial least squares regression (SPA-PLS) provided optimal predictive performance (correlation coefficient of prediction (Rp) = 0.9736 and residual predictive deviation (RPD) = 4.75). The proposed method achieved the limit of detection (LOD) = 0.16 μg·kg-1, the recovery of predicted results was in the range of 96.33-109.17% and the relative standard deviation (RSD) was < 5%. The overall results suggested that SERS based Ag@ZnO NFs combined with MC-SPA-PLS could be an easy and efficient method to quantify deltamethrin residue levels in wheat.
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45
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Weng S, Yuan H, Zhang X, Li P, Zheng L, Zhao J, Huang L. Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy. Analyst 2020; 145:4827-4835. [PMID: 32515435 DOI: 10.1039/d0an00492h] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine learning methods generally require extra preprocessing or feature engineering, and handling large-scale data using these methods is challenging. In this study, deep learning networks were used as fully connected networks, convolutional neural networks (CNN), fully convolutional networks (FCN), and principal component analysis networks (PCANet) to determine their abilities to recognise drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of a one-dimensional vector or a two-dimensional matrix. The best recognition result for drugs in urine with an accuracy of 98.05% in the prediction set was obtained using CNN with spectra as input in the matrix form. The optimal quantitation for pirimiphos-methyl was obtained using FCN with spectra in the matrix form, and the analysis was accomplished with a determination coefficient of 0.9997 and a root mean square error of 0.1574 in the prediction set. These networks performed better than the common machine learning methods. Overall, the deep learning networks provide feasible alternatives for the recognition and quantitation of SERS.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.
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46
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Kutsanedzie FY, Agyekum AA, Annavaram V, Chen Q. Signal-enhanced SERS-sensors of CAR-PLS and GA-PLS coupled AgNPs for ochratoxin A and aflatoxin B1 detection. Food Chem 2020; 315:126231. [DOI: 10.1016/j.foodchem.2020.126231] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 01/14/2020] [Accepted: 01/15/2020] [Indexed: 10/25/2022]
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47
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Quantitative analysis of docetaxel by surface-enhanced Raman spectroscopy (SERS) combined with chemometric models and Ag@ZnO nanoparticles substrates. Microchem J 2020. [DOI: 10.1016/j.microc.2019.104373] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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48
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Interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) for quantitative analysis of surface-enhanced Raman scattering (SERS) spectra. Anal Chim Acta 2020; 1105:45-55. [DOI: 10.1016/j.aca.2020.01.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/15/2019] [Accepted: 01/08/2020] [Indexed: 12/11/2022]
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Xu Y, Kutsanedzie FYH, Hassan M, Zhu J, Ahmad W, Li H, Chen Q. Mesoporous silica supported orderly-spaced gold nanoparticles SERS-based sensor for pesticides detection in food. Food Chem 2020; 315:126300. [PMID: 32018077 DOI: 10.1016/j.foodchem.2020.126300] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 12/20/2019] [Accepted: 01/25/2020] [Indexed: 02/07/2023]
Abstract
In this study, a novel sensor fabricated with compactly arranged gold nanoparticles (AuNPs) templated from mesoporous silica film (MSF) via air-water interface has been confirmed as a promising surface-enhanced Raman scattering (SERS) substrate for detecting trace levels of 2,4-dichlorophenoxyacetic acid (2,4-D), pymetrozine and thiamethoxam. The densely arranged AuNPs@MSF had an average AuNPs size of 5.15 nm with small nanogaps (<2nm) between AuNPs, and exhibited a high SERS performance. SERS spectra of pesticides were collected after their adsorption on the AuNPs@MSF. The results showed that the concentration of 2,4-D, pymetrozine and thiamethoxam gave a good linear relationship with SERS intensity. Moreover, the designed SERS-based sensor (AuNPs@MSF) was stable for 3 months with ca. 3% relative standard deviation (RSD) and was applied successfully for the analysis of 2,4-D extraction from both environmental and food samples. The proposed SERS-based sensor was further validated by HPLC and showed satisfactory result (p > 0.05).
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Affiliation(s)
- Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Felix Y H Kutsanedzie
- Research and Innovation Center/Mechanical Engineering Department, Accra Technical University, Accra, Ghana
| | - Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Jiaji Zhu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Waqas Ahmad
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, People's Republic of China.
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Chen X, Lin M, Sun L, Xu T, Lai K, Huang M, Lin H. Detection and quantification of carbendazim in Oolong tea by surface-enhanced Raman spectroscopy and gold nanoparticle substrates. Food Chem 2019; 293:271-277. [DOI: 10.1016/j.foodchem.2019.04.085] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 04/11/2019] [Accepted: 04/24/2019] [Indexed: 11/25/2022]
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