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Ortiz-Martínez M, Molina González JA, Ramírez García G, de Luna Bugallo A, Justo Guerrero MA, Strupiechonski EC. Enhancing Sensitivity and Selectivity in Pesticide Detection: A Review of Cutting-Edge Techniques. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024; 43:1468-1484. [PMID: 38726957 DOI: 10.1002/etc.5889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/26/2024] [Accepted: 04/12/2024] [Indexed: 06/27/2024]
Abstract
The primary goal of our review was to systematically explore and compare the state-of-the-art methodologies employed in the detection of pesticides, a critical component of global food safety initiatives. New approach methods in the fields of luminescent nanosensors, chromatography, terahertz spectroscopy, and Raman spectroscopy are discussed as precise, rapid, and versatile strategies for pesticide detection in food items and agroecological samples. Luminescent nanosensors emerge as powerful tools, noted for their portability and unparalleled sensitivity and real-time monitoring capabilities. Liquid and gas chromatography coupled to spectroscopic detectors, stalwarts in the analytical chemistry field, are lauded for their precision, wide applicability, and validation in diverse regulatory environments. Terahertz spectroscopy offers unique advantages such as noninvasive testing, profound penetration depth, and bulk sample handling. Meanwhile, Raman spectroscopy stands out with its nondestructive nature, its ability to detect even trace amounts of pesticides, and its minimal requirement for sample preparation. While acknowledging the maturity and robustness of these techniques, our review underscores the importance of persistent innovation. These methodologies' significance extends beyond their present functions, highlighting their adaptability to meet ever-evolving challenges. Environ Toxicol Chem 2024;43:1468-1484. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Mónica Ortiz-Martínez
- Consejo Nacional de Humanidades, Ciencias y Tecnologías, Ciudad de México, México
- Centro de Ingeniería y Desarrollo Industrial, Santiago de Querétaro, México
| | - Jorge Alberto Molina González
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Juriquilla, Santiago de Querétaro, México
| | - Gonzalo Ramírez García
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Juriquilla, Santiago de Querétaro, México
| | - Andrés de Luna Bugallo
- Centro de Física Aplicada y Tecnología Avanzada, Universidad Nacional Autónoma de México, Juriquilla, Santiago de Querétaro, México
| | - Manuel Alejandro Justo Guerrero
- Istituto Nanoscienze and Scuola Normale Superiore, National Enterprise for nanoScience and nanoTechnology Consiglio Nazionale della Richerche, Pisa, Italy
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Liu Y, Pu H, Li Q, Sun DW. Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 286:122035. [PMID: 36332396 DOI: 10.1016/j.saa.2022.122035] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/27/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Pericarpium Citri Reticulatae (PCR) in longer storage years possess higher medicinal values, but their differentiation is difficult due to similar morphological characteristics. Therefore, this study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with a convolutional neural network (CNN) to identify PCR samples stored from 1 to 20 years. The absorption coefficient and refractive index spectra in the range of 0.2-1.5 THz were acquired. Partial least squares discriminant analysis, random forest, least squares support vector machines, and CNN were used to establish discriminant models, showing better performance of the CNN model than the others. In addition, the output data points of the CNN intermediate layer were visualized, illustrating gradual changes in these points from overlapping to clear separation. Overall, THz-TDS combined with CNN models could realize rapid identification of different year PCRs, thus providing an efficient alternative method for PCR quality inspection.
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Affiliation(s)
- Yao Liu
- School of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics (e) Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qian Li
- Shenzhen Institute of Terahertz Technology and Innovation, Shenzhen, Guangdong 518102, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics (e) Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
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Shao Y, Zhu D, Wang Y, Zhu Z, Tang W, Tian Z, Peng Y, Zhu Y. Moxa Wool in Different Purities and Different Growing Years Measured by Terahertz Spectroscopy. PLANT PHENOMICS 2022; 2022:9815143. [PMID: 35707451 PMCID: PMC9178489 DOI: 10.34133/2022/9815143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/01/2022] [Indexed: 11/06/2022]
Abstract
Moxa wool is a traditional Chinese herbal medicine, which can warm channels to dispel coldness. At present, there is no unified index to evaluate the purity and growing years of moxa wool in the market. Terpineol is one of the effective substances in the volatile oil of moxa wool. Here, we characterize the purity and growing years of moxa wool by studying terpineol. Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are the methods for monitoring terpineol at present, all of which have defects of complicated procedures. We established linear fitting to distinguish the different purities of moxa wool through the intensities (areas) of terpineol, the characteristic peaks, and the consequence presented; the coefficient of determination (R2) was higher than 0.90. Furthermore, based on the characteristic peak position of standard terpineol, the correlation model with the purity and growing year of moxa wool was set up, thereby differentiating the quality of moxa wool. We have built the partial least squares (PLS) model of the growing years of moxa wool with high accuracy, and the determination coefficient is greater than 0.98. In addition, we compare the quantitative accuracy of Raman spectroscopy with terahertz technology. Finally, a new method of terahertz spectroscopy to evaluate quality of moxa wool was found. It provides a new idea for the identification of inferior moxa wool in the market and a new method for identifying the quality of moxa wool in traditional Chinese medicine.
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Affiliation(s)
- Yongni Shao
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 20009, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
| | - Di Zhu
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 20009, China
| | - Yutian Wang
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 20009, China
| | - Zhi Zhu
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 20009, China
| | - Wenchao Tang
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, China
| | - Zhengan Tian
- Shanghai International Travel Healthcare Center, Shanghai Customs District P.R. 200335, China
| | - Yan Peng
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 20009, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
| | - Yiming Zhu
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 20009, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
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Zhao Y, Gouda M, Yu G, Zhang C, Lin L, Nie P, Huang W, Ye H, Ye Y, Zhou C, He Y. Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 225:112800. [PMID: 34547661 DOI: 10.1016/j.ecoenv.2021.112800] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/13/2021] [Accepted: 09/15/2021] [Indexed: 06/13/2023]
Abstract
Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC2), Cd-PC2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC2 chelates Cd2+ in a 2:1 ratio to form Cd(PC2)2 complex; Cd connected to thoils of PC2 and changed β-turn and random coil of PC2 peptide chain to β-Sheet which presented as terahertz vibrations of PC2 around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC2 as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.
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Affiliation(s)
- Yinglei Zhao
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Department of Nutrition & Food Science, National Research Centre, Dokki, Giza, Egypt
| | - Guohong Yu
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Chenghao Zhang
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Lei Lin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Pengcheng Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
| | - Wei Huang
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Hongbao Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Yunxiang Ye
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Chengquan Zhou
- Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, 310000 Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China.
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Galieni A, D'Ascenzo N, Stagnari F, Pagnani G, Xie Q, Pisante M. Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography. FRONTIERS IN PLANT SCIENCE 2021; 11:609155. [PMID: 33584752 PMCID: PMC7873487 DOI: 10.3389/fpls.2020.609155] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 11/18/2020] [Indexed: 05/24/2023]
Abstract
Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.
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Affiliation(s)
- Angelica Galieni
- Research Centre for Vegetable and Ornamental Crops, Council for Agricultural Research and Economics, Monsampolo del Tronto, Italy
| | - Nicola D'Ascenzo
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, I.R.C.C.S, Pozzilli, Italy
| | - Fabio Stagnari
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Giancarlo Pagnani
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Qingguo Xie
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, I.R.C.C.S, Pozzilli, Italy
| | - Michele Pisante
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
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