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Ma C, Li X, Mao N, Wang M, Cui C, Jia H, Liu X, Sun Q. Semi-invasive wearable clinic: Solution-processed smart microneedle electronics for next-generation integrated diagnosis and treatment. Biosens Bioelectron 2024; 260:116427. [PMID: 38823368 DOI: 10.1016/j.bios.2024.116427] [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: 02/23/2024] [Revised: 05/16/2024] [Accepted: 05/23/2024] [Indexed: 06/03/2024]
Abstract
The integrated smart electronics for real-time monitoring and personalized therapy of disease-related analytes have been gradually gaining tremendous attention. However, human tissue barriers, including the skin barrier and brain-blood barrier, pose significant challenges for effective biomarker detection and drug delivery. Microneedle (MN) electronics present a promising solution to overcome these tissue barriers due to their semi-invasive structures, enabling effective drug delivery and target-analyte detection without compromising the tissue configuration. Furthermore, MNs can be fabricated through solution processing, facilitating large-scale manufacturing. This review provides a comprehensive summary of the recent three-year advancements in smart MNs development, categorized as follows. First, the solution-processed technology for MNs is introduced, with a focus on various printing technologies. Subsequently, smart MNs designed for sensing, drug delivery, and integrated systems combining diagnosis and treatment are separately summarized. Finally, the prospective and promising applications of next-generation MNs within mediated diagnosis and treatment systems are discussed.
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Affiliation(s)
- Chao Ma
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaomeng Li
- National Center for International Joint Research of Micro-nano Molding Technology, School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Ning Mao
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China
| | - Mengwei Wang
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China
| | - Cancan Cui
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China
| | - Hanyu Jia
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China
| | - Xuying Liu
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China
| | - Qingqing Sun
- School of Materials Science and Engineering, Zhengzhou Key Laboratory of Flexible Electronic Materials and Thin-Film Technologies, Zhengzhou University, Zhengzhou 450001, China.
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He P, Chen Y, Lin L, Guo H, Yang F. A "turn-on" fluorescent sensor for herbicide quizalofop-p-ethyl based on cyanostilbene-pyridine macrocycle. Talanta 2024; 276:126269. [PMID: 38776773 DOI: 10.1016/j.talanta.2024.126269] [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: 04/11/2024] [Revised: 05/12/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024]
Abstract
Quizalofop-p-ethyl is a widely used herbicide that also poses a risk to human health and environmental safety. However, there is still a lack of simple and in-situ detecting method for quizalofop-p-ethyl so far. In this work, the fluorescent sensor was firstly developed on detection of quizalofop-p-ethyl based on cyanostilbene-pyridine macrocycle (CPM). CPM was prepared by the "1 + 1" condensation of pyridine-substituted cyanostilbene derivative with 4,4'-Bis(chloromethyl)biphenyl in 68 % yield. The weak fluorescence of CPM in aqueous media transferred to strong orange fluorescence after sensing quizalofop-p-ethyl. This sensing behavior exhibited high selectivity among 28 kinds of herbicides and ions. The limitation of detection (LOD) was 2.98 × 10-8 M and the limitation of quantification (LOQ) was 9.94 × 10-8 M (λex = 390 nm, λem = the maximum emission between 512 nm and 535 nm) with a dynamic range of 0.01-0.9 eq. The binding constant (Ka) of quizalofop-p-ethyl to the sensor CPM was 3.2 × 106 M-1. The 1:1 sensing mechanism was confirmed as that quizalofop-p-ethyl was located in the cavity of CPM, which enhanced aggregating effect and reduced the intramolecular rotation of aromatic groups for better AIE effect. The sensing ability of CPM for quizalofop-p-ethyl had been efficiently applied in test paper experiments, agricultural product tests and real water samples, revealing that CPM has good application prospect for simple and in-situ detection of quizalofop-p-ethyl in real environment.
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Affiliation(s)
- Pan He
- College of Chemistry and Materials Sciences, Fujian Normal University, Fuzhou, 350007, PR China; Fujian Key Laboratory of Polymer Materials, Fuzhou, 350007, PR China; Fujian provincial Key Laboratory of Advanced Materials Oriented Chemical Engineering, Fuzhou, 350007, PR China
| | - Yuxi Chen
- College of Chemistry and Materials Sciences, Fujian Normal University, Fuzhou, 350007, PR China
| | - Liangbin Lin
- College of Chemistry and Materials Sciences, Fujian Normal University, Fuzhou, 350007, PR China
| | - Hongyu Guo
- College of Chemistry and Materials Sciences, Fujian Normal University, Fuzhou, 350007, PR China; Fujian Key Laboratory of Polymer Materials, Fuzhou, 350007, PR China
| | - Fafu Yang
- College of Chemistry and Materials Sciences, Fujian Normal University, Fuzhou, 350007, PR China; Fujian provincial Key Laboratory of Advanced Materials Oriented Chemical Engineering, Fuzhou, 350007, PR China.
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Mutunga T, Sinanovic S, Harrison CS. Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater. SENSORS (BASEL, SWITZERLAND) 2024; 24:3191. [PMID: 38794044 PMCID: PMC11125874 DOI: 10.3390/s24103191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.
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Affiliation(s)
- Titus Mutunga
- School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, Scotland, UK; (S.S.); (C.S.H.)
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Shivaani M, Madan P. Application of imaging and spectroscopy techniques for grading of bovine embryos - a review. Front Vet Sci 2024; 11:1364570. [PMID: 38774909 PMCID: PMC11107339 DOI: 10.3389/fvets.2024.1364570] [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: 01/08/2024] [Accepted: 04/18/2024] [Indexed: 05/24/2024] Open
Abstract
Although embryo transfers have grown considerably in the cattle industry, the selection of embryos required for successful pregnancies remains a challenging task. Visual inspection of 7th-day embryos using a stereomicroscope, followed by classification based on morphological features is the most commonly practiced procedure. However, there are inaccuracies and inconsistencies in the manual grading of bovine embryos. The objective of this review was to evaluate the potential of imaging and spectroscopic techniques in the selection of bovine embryos. Digital analysis of microscopic images through extracting visual features in the embryo region, and classification using machine learning methods have yielded about 88-96% success in pregnancies. The Raman spectral pattern provides valuable information regarding developmental stages and quality of the embryo. The Raman spectroscopy approach has also been successfully used to determine various parameters of bovine oocytes. Besides, Fourier Transform Infrared (FTIR) spectroscopy has the ability to assess embryo quality through analyzing embryo composition, including nucleic acid and amides present. Hyperspectral Imaging has also been used to characterize metabolite production during embryo growth. Although the time-lapse imaging approach is beneficial for morphokinetics evaluation of embryo development, optimized protocols are required for successful implementation in bovine embryo transfers. Most imaging and spectroscopic findings are still only at an experimental stage. Further research is warranted to improve the repeatability and practicality to implement in commercial facilities.
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Affiliation(s)
| | - Pavneesh Madan
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
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Yi C, Zhang Z, Huang T, Xiao H. Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124068. [PMID: 38417234 DOI: 10.1016/j.saa.2024.124068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 03/01/2024]
Abstract
The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.
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Affiliation(s)
- Cancan Yi
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, 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.
| | - Zhenyu Zhang
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, 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
| | - Tao Huang
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, 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
| | - Han Xiao
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, 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
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Wahyuni WT, Putra BR, Rahman HA, Ivandini TA, Irkham, Khalil M, Rahmawati I. Effect of Aspect Ratio of a Gold-Nanorod-Modified Screen-Printed Carbon Electrode for Carbaryl Detection in Three Different Samples of Vegetables. ACS OMEGA 2024; 9:1497-1515. [PMID: 38239286 PMCID: PMC10796111 DOI: 10.1021/acsomega.3c07831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 01/22/2024]
Abstract
In this study, three different sizes of gold nanorods (AuNRs) were synthesized using the seed-growth method by adding various volumes of AgNO3 as 400, 600, and 800 μL into the growth solution of gold nanoparticles. Three different sizes of AuNRs were then characterized using UV-vis spectroscopy, high-resolution transmission electron microscopy (HRTEM), selected area electron diffraction (SAED) patterns, and atomic force microscopy (AFM) to investigate the surface morphology, topography, and aspect ratios of each synthesized AuNR. The aspect ratios from the histogram of size distributions of three AuNRs as 2.21, 2.53, and 2.85 can be calculated corresponding to the addition of AgNO3 volumes of 400, 600, and 800 μL. Moreover, each AuNR in three different aspect ratios was drop-cast onto the surface of a commercial screen-printed carbon electrode (SPCE) to obtain three different SPCE-modified AuNRs (SPCE-A400, SPCE-A600, and SPCE-A800, respectively). All SPCE-modified AuNRs were then evaluated for their electrochemical behavior using cyclic voltammetry and electrochemical impedance spectroscopy (EIS) techniques and the highest electrochemical performance was shown as the order of magnitude of SPCE-A400 > SPCE-A600/SPCE-A800. The reason for the highest electrocatalytic activity of SPCE-A400 might be due to the smallest particle size and uniform distribution of AuNRs ∼ 2.2, which enhanced the charge transfer, thus providing the highest electroactive surface area (0.6685 cm2) compared to other electrodes. These results also confirm that the sensing mechanism for all SPCE-modified AuNRs is controlled by diffusion phenomena. In addition, the optimum pH was obtained as 4 for carbaryl detection for all SPCE-modified AuNRs with the highest current shown by SPCE-A400. Furthermore, SPCE-A400 has the highest fundamental parameters (surface coverage, catalytic rate constant, electron transfer rate constant, and adsorption capacity) for carbaryl detection, which were investigated using cyclic voltammetry and chronoamperometric techniques. The electroanalytical performances of all SPCE-modified AuNRs for carbaryl detection were also investigated with SPCE-A400 displaying the best performance among other electrodes in terms of its linearity (0.2-100 μM), limit of detection (LOD) ∼ 0.07 μM, and limit of quantification (LOQ) ∼ 0.2 μM. All SPCE-modified AuNRs were also subsequently evaluated for their stability, reproducibility, and selectivity in the presence of interfering species such as NaNO2, NH4NO3, Zn(CH3CO2)2, FeSO4, diazinon, and glucose and show reliable results as depicted from %RSD values less than 3%. At last, all SPCE-modified AuNRs have been employed for carbaryl detection using a standard addition technique in three different samples of vegetables (cabbage, cucumber, and Chinese cabbage) with its results (%recovery ≈ 100%) within the acceptable analytical range. In conclusion, this work demonstrates the great potential of a disposable device based on an AuNR-modified SPCE for rapid detection and high sensitivity in monitoring the concentration of carbaryl as a residual pesticide in vegetable samples.
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Affiliation(s)
- Wulan Tri Wahyuni
- Analytical
Chemistry Division, Department of Chemistry, Faculty of Mathematics
and Natural Sciences, Kampus IPB Dramaga, Bogor 16680, Indonesia
- Tropical
Biopharmaca Research Center, Institute of Research and Community Empowerment, IPB University, Bogor 16680, Indonesia
| | - Budi Riza Putra
- Research
Center for Metallurgy, National Research
and Innovation Agency (BRIN), PUSPIPTEK Gd. 470, South
Tangerang, Banten 15315, Indonesia
| | - Hemas Arif Rahman
- Analytical
Chemistry Division, Department of Chemistry, Faculty of Mathematics
and Natural Sciences, Kampus IPB Dramaga, Bogor 16680, Indonesia
| | - Tribidasari A. Ivandini
- Department
of Chemistry, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok 16424, Indonesia
| | - Irkham
- Department
of Chemistry, Faculty of Mathematics and Natural Sciences, University of Padjajaran, Bandung 45363, Indonesia
| | - Munawar Khalil
- Department
of Chemistry, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok 16424, Indonesia
| | - Isnaini Rahmawati
- Department
of Chemistry, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok 16424, Indonesia
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Chen Z, Dong X, Liu C, Wang S, Dong S, Huang Q. Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach. Sci Rep 2023; 13:19855. [PMID: 37963934 PMCID: PMC10645736 DOI: 10.1038/s41598-023-45954-y] [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: 08/12/2023] [Accepted: 10/26/2023] [Indexed: 11/16/2023] Open
Abstract
Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it is important to develop methods and tools for the rapid detection of pesticides/fungicides in fruits and vegetables, which are highly demanded in the current markets. Surface-enhanced Raman spectroscopy (SERS) can achieve trace chemical detection, while it is still a challenge to apply SERS for the detection and identification of mixed pesticides/fungicides. In this work, we tried to combine SERS technique and deep learning spectral analysis for the determination of mixed chlorpyrifos and pyrimethanil on the surface of fruits including apples and strawberries. Especially, the multi-channel convolutional neural networks-gate recurrent unit (MC-CNN-GRU) classification model was used to extract sequence and spatial information in the spectra, so that the accuracy of the optimized classification model could reach 99% even when the mixture ratio of pesticide/fungicide varied considerably. This work therefore demonstrates an effective application of using SERS combined deep learning approach in the rapid detection and identification of different mixed pesticides in agricultural products.
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Affiliation(s)
- Zhu Chen
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
- Anhui Province Key Laboratory of Aquaculture and Stock Enhancement, Fisheries Research Institution, Anhui Academy of Agricultural Sciences, Hefei, China
| | - Xuan Dong
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
- CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Anhui Key Laboratory of Environmental Toxicology and Pollution Control Technology, Institute of Intelligent Machines, Hefei Institute of Intelligent Agriculture, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Chao Liu
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
- CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Anhui Key Laboratory of Environmental Toxicology and Pollution Control Technology, Institute of Intelligent Machines, Hefei Institute of Intelligent Agriculture, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Shenghao Wang
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
- CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Anhui Key Laboratory of Environmental Toxicology and Pollution Control Technology, Institute of Intelligent Machines, Hefei Institute of Intelligent Agriculture, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Department of Basic Sciences, Army Academy of Artillery and Air Defense, Hefei, China
| | - Shanshan Dong
- Henan Key Laboratory of Ion-Beam Bioengineering, School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Qing Huang
- Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China.
- CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Anhui Key Laboratory of Environmental Toxicology and Pollution Control Technology, Institute of Intelligent Machines, Hefei Institute of Intelligent Agriculture, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
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Yu G, Li H, Li Y, Hu Y, Wang G, Ma B, Wang H. Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon. Foods 2023; 12:foods12091742. [PMID: 37174281 PMCID: PMC10177868 DOI: 10.3390/foods12091742] [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: 03/20/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.
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Affiliation(s)
- Guowei Yu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Huihui Li
- Analysis and Testing Center, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
- Food Quality Supervision and Testing Center (Shihezi), Ministry of Agriculture and Rural Affairs, Shihezi 832000, China
| | - Yujie Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yating Hu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Gang Wang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832003, China
| | - Benxue Ma
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832003, China
| | - Huting Wang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832003, China
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