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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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Raki H, Aalaila Y, Taktour A, Peluffo-Ordóñez DH. Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review. Foods 2023; 13:11. [PMID: 38201039 PMCID: PMC10777928 DOI: 10.3390/foods13010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/27/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024] Open
Abstract
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.
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Affiliation(s)
- Hind Raki
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
| | - Yahya Aalaila
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
| | - Ayoub Taktour
- Materials Sciences and Nanotechnoloy (MSN), University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco;
| | - Diego H. Peluffo-Ordóñez
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
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Li Y, Gu M, Liu X, Lin J, Jiang H, Song H, Xiao X, Zhou W. Sequencing and analysis of the complete mitochondrial genomes of Toona sinensis and Toona ciliata reveal evolutionary features of Toona. BMC Genomics 2023; 24:58. [PMID: 36726084 PMCID: PMC9893635 DOI: 10.1186/s12864-023-09150-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/24/2023] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Toona is a critical genus in the Meliaceae, and the plants of this group are an asset for both restorative and restorative purposes, the most flexible of which are Toona sinensis and Toona ciliata. To concentrate on the advancement of mitochondrial(Mt) genome variety in T.sinensis and T.ciliata, the Mt genomes of the two species were sequenced in high throughput independently, after de novo assembly and annotation to construct a Mt genome map for comparison in genome structure. Find their repetitive sequences and analyze them in comparison with the chloroplast genome, along with Maximum-likelihood(ML) phylogenetic analysis with 16 other relatives. RESULTS (1) T. sinensis and T.ciliata are both circular structures with lengths of 683482 bp and 68300 bp, respectively. They share a high degree of similarity in encoding genes and have AT preferences. All of them have the largest Phe concentration and are the most frequently used codons. (2) Both of their Mt genome are highly preserved in terms of structural and functional genes, while the main variability is reflected in the length of tRNA, the number of genes, and the value of RSCU. (3) T. siniensis and T. ciliata were detected to have 94 and 87 SSRs, respectively, of which mononucleotides accounted for the absolute proportion. Besides, the vast majority of their SSRs were found to be poly-A or poly-T. (4)10 and 11 migrating fragments were identified in the comparison with the chloroplast genome, respectively. (5) In the ML evolutionary tree, T.sinensis and T.ciliata clustered individually into a small branch with 100% support, reflecting two species of Toona are very similarly related to each other. CONCLUSIONS This research provides a basis for the exploitation of T.sinensis and T.ciliata in terms of medicinal, edible, and timber resources to avoid confusion; at the same time, it can explore the evolutionary relationship between the Toona and related species, which does not only have an important practical value, but also provides a theoretical basis for future hybrid breeding of forest trees, molecular markers, and evolutionary aspects of plants, which has great scientific significance.
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Affiliation(s)
- Youli Li
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
| | - Min Gu
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
| | - Xuanzhe Liu
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
| | - Jianna Lin
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
| | - Huier Jiang
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
| | - Huiyun Song
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
| | - Xingcui Xiao
- grid.464457.00000 0004 0445 3867Sichuan Academy of Forestry Sciences, Chengdu, 61008 Sichuan China
| | - Wei Zhou
- grid.20561.300000 0000 9546 5767College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 51000 Guangdong China
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Sun L, Cui X, Fan X, Suo X, Fan B, Zhang X. Automatic detection of pesticide residues on the surface of lettuce leaves using images of feature wavelengths spectrum. FRONTIERS IN PLANT SCIENCE 2023; 13:929999. [PMID: 36777538 PMCID: PMC9909533 DOI: 10.3389/fpls.2022.929999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 11/08/2022] [Indexed: 06/18/2023]
Abstract
The inappropriate application of pesticides to vegetable crops often results in environmental pollution, which seriously impacts the environment and human health. Given that current methods of pesticide residue detection are associated with issues such as low accuracy, high equipment cost, and complex flow, this study puts forward a new method for detecting pesticide residues on lettuce leaves. To establish this method, spectral analysis was used to determine the characteristic wavelength of pesticide residues (709 nm), machine vision equipment was improved, and a bandpass filter and light source of characteristic wavelength were installed to acquire leaf image information. Next, image preprocessing and feature information extraction were automatically implemented through programming. Several links were established for the training model so that the required feature information could be automatically extracted after the batch input of images. The pesticide residue detected using the chemical method was taken as the output and modeled, together with the input image information, using the convolutional neural network (CNN) algorithm. Furthermore, a prediction program was rewritten to generalize the input images during the prediction process and directly obtain the output pesticide residue. The experimental results revealed that when the detection device and method designed in this study were used to detect pesticide residues on lettuce leaves in a key state laboratory, the coefficient of determination of the equation reached 0.883, and the root mean square error (RMSE) was 0.134 mg/L, indicating high accuracy and that the proposed method integrated the advantages of spectrum detection and deep learning. According to comparison testing, the proposed method can meet Chinese national standards in terms of accuracy. Moreover, the improved machine vision equipment was less expensive, thus providing powerful support for the application and popularization of the proposed method.
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Tu S, Wang Z, Zhang W, Li Y, She Y, Du H, Yi C, Qin B, Liu Z. A new technology for rapid determination of isomers of hydroxybenzoic acid by terahertz spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 279:121313. [PMID: 35598575 DOI: 10.1016/j.saa.2022.121313] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 06/15/2023]
Abstract
This study investigated the feasibility of using terahertz (THz) technology for the rapid identification of isomers. The time-domain spectra of 2-hydroxybenzoic acid (2-HA), 3-hydroxybenzoic acid (3-HA), and 4-hydroxybenzoic acid (4-HA) were measured by a THz time-domain spectroscopy system (THz-TDS) in the range of 0.3-1.8 THz. Aiming at the isomer classification problem, a THz spectral data classification model based on a variational mode decomposition-particle swarm optimization-support vector machine (VMD-PSO-SVM) method was proposed. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) were used to extract the first eight intrinsic mode functions (IMFs) of the time-domain signal. Principal component analysis (PCA) was used to extract the first 80 principal components of each modal component as the classification feature vector. The particle swarm optimization (PSO) and support vector machine (SVM) algorithms were used to construct 2-, 3-, and 4-HA classification models. We found that the prediction accuracy of the VMD-PSO-SVM model was significantly higher than that of EMD-PSO-SVM model regardless of the modal components. For both EMD and VMD, with the increase in the IMF number, the corresponding classification recognition accuracy tended to decrease. The results showed that the rapid identification model of hydroxybenzoic acid isomers based on THz spectroscopy and SVM was effective and feasible, providing an accurate and rapid method for the chemical synthesis and quality monitoring of biomedicine.
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Affiliation(s)
- Shan Tu
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, Guilin 541004, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China; Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin 541004, China
| | - Zhigang Wang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China.
| | - Wentao Zhang
- Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin 541004, China.
| | - Yuanpeng Li
- Guangxi Key Laboratory of Nuclear Physics and Technology, Guangxi Normal University, Guilin 541004, China
| | - Yulai She
- Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin 541004, China
| | - Hao Du
- Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin University of Electronic Technology, Guilin 541004, China
| | - Cancan Yi
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Bo Qin
- The 34th Research Institute of CETC, Guilin 541004, China
| | - Zhiqiang Liu
- The 34th Research Institute of CETC, Guilin 541004, China
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Li Z, Lin H, Wang L, Cao L, Sui J, Wang K. Optical sensing techniques for rapid detection of agrochemicals: Strategies, challenges, and perspectives. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156515. [PMID: 35667437 DOI: 10.1016/j.scitotenv.2022.156515] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/24/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
In recent years, the irrational use of agrochemicals has caused great harm to the environment and public health. Along with the rapid development of optical technology and nanotechnology, the research of optical sensing methods in agrochemical detection has been developed rapidly owing to its advantages of simplicity, fast response, and cost-effectiveness. In this review, the strategies of employing optical systems based on colorimetric sensor, fluorescence, chemiluminescence, terahertz spectroscopy, surface plasmon resonance, and surface-enhanced Raman spectroscopy for sensing agrochemicals were summarized. In addition, the challenges in the practical application of optical sensing technologies for agrochemical detection were discussed in-depth, and potential future trends and prospects of these techniques were addressed. A variety of nanomaterials have been developed for enhancing the sensitivity of optical sensing systems. The optical properties of nanomaterials are governed by their size, shape, and chemical structure. Although each optical sensing system holds its advantages, there are still many challenges that need to be overcome in practical applications. With the continuous developments in novel functional nanomaterials, sample preparation methods, and spectral processing algorithms, optical sensors are expected to have powerful potential for rapid testing of agrochemicals in the environment and foods.
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Affiliation(s)
- Zhuoran Li
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Hong Lin
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Lei Wang
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Limin Cao
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Jianxin Sui
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Kaiqiang Wang
- College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China; Fujian Provincial Key Laboratory of Breeding Lateolabrax Japonicus, Ningde, Fujian 355299, China.
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Yang R, Li Y, Zheng J, Qiu J, Song J, Xu F, Qin B. A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning. MATERIALS (BASEL, SWITZERLAND) 2022; 15:6093. [PMID: 36079475 PMCID: PMC9457567 DOI: 10.3390/ma15176093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/24/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.
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Affiliation(s)
- Ruizhao Yang
- Key Laboratory of Complex System Optimization and Big Data Processing, Guangxi Colleges and Universities, Yulin Normal University, Yulin 537000, China
- Optoelectronic Information Research Center, School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537000, China
| | - Yun Li
- School of Chemistry and Food Science, Yulin Normal University, Yulin 537000, China
| | - Jincun Zheng
- Key Laboratory of Complex System Optimization and Big Data Processing, Guangxi Colleges and Universities, Yulin Normal University, Yulin 537000, China
- Research Center of Intelligent Information and Communication Technology, School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537000, China
| | - Jie Qiu
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Jinwen Song
- Research Center of Intelligent Information and Communication Technology, School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537000, China
| | - Fengxia Xu
- Research Center of Intelligent Information and Communication Technology, School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537000, China
| | - Binyi Qin
- Key Laboratory of Complex System Optimization and Big Data Processing, Guangxi Colleges and Universities, Yulin Normal University, Yulin 537000, China
- Research Center of Intelligent Information and Communication Technology, School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537000, China
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8
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Applications of Advanced Data Analytic Techniques in Food Safety and Risk Assessment. Curr Opin Food Sci 2022. [DOI: 10.1016/j.cofs.2022.100937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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9
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Yang R, Li Y, Qin B, Zhao D, Gan Y, Zheng J. Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy. RSC Adv 2022; 12:1769-1776. [PMID: 35425184 PMCID: PMC8979129 DOI: 10.1039/d1ra06905e] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/20/2021] [Indexed: 12/24/2022] Open
Abstract
Feature extraction is a key factor to detect pesticides using terahertz spectroscopy. Compared to traditional methods, deep learning is able to obtain better insights into complex data features at high levels of abstraction. However, reports about the application of deep learning in THz spectroscopy are rare. The main limitation of deep learning to analyse terahertz spectroscopy is insufficient learning samples. In this study, we proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and the residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy. The Wasserstein generative adversarial network and pretraining model technology were employed to solve the problem of insufficient learning samples for training the ResNet. The Wasserstein generative adversarial network was used for generating more new learning samples. At the same time, pretraining model technology was applied to reduce the training parameters, in order to avoid residual neural network overfitting. The results demonstrate that our proposed method achieves a 91.4% accuracy rate, which is better than those of support vector machine, k-nearest neighbor, naïve Bayes model and ensemble learning. In summary, our proposed method demonstrates the potential application of deep learning in pesticide residue detection, expanding the application of THz spectroscopy.
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Affiliation(s)
- Ruizhao Yang
- School of Physics and Telecommunication Engineering, Yulin Normal University Yulin China
| | - Yun Li
- College of Chemistry and Food Science, Yulin Normal University Yulin China
| | - Binyi Qin
- School of Physics and Telecommunication Engineering, Yulin Normal University Yulin China
- Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing, Yulin Normal University Yulin China
| | - Di Zhao
- School of Physics and Telecommunication Engineering, Yulin Normal University Yulin China
| | - Yongjin Gan
- School of Physics and Telecommunication Engineering, Yulin Normal University Yulin China
| | - Jincun Zheng
- School of Physics and Telecommunication Engineering, Yulin Normal University Yulin China
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Wang X, Bouzembrak Y, Lansink AO, van der Fels-Klerx HJ. Application of machine learning to the monitoring and prediction of food safety: A review. Compr Rev Food Sci Food Saf 2021; 21:416-434. [PMID: 34907645 DOI: 10.1111/1541-4337.12868] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/15/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022]
Abstract
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.
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Affiliation(s)
- Xinxin Wang
- Business Economics, Wageningen University & Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- Wageningen Food Safety Research, Wageningen University & Research, Wageningen, The Netherlands
| | - Agjm Oude Lansink
- Business Economics, Wageningen University & Research, Wageningen, The Netherlands
| | - H J van der Fels-Klerx
- Business Economics, Wageningen University & Research, Wageningen, The Netherlands.,Wageningen Food Safety Research, Wageningen University & Research, Wageningen, The Netherlands
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Abstract
Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.
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