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Yuce M, Yildirim E, Ekinci M, Turan M, Ilhan E, Aydin M, Agar G, Ucar S. N-acetyl-cysteine mitigates arsenic stress in lettuce: Molecular, biochemical, and physiological perspective. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 207:108390. [PMID: 38373369 DOI: 10.1016/j.plaphy.2024.108390] [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: 12/10/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/21/2024]
Abstract
Agricultural land contaminated with heavy metals such as non-biodegradable arsenic (As) has become a serious global problem as it adversely affects agricultural productivity, food security and human health. Therefore, in this study, we investigated how the administration of N-acetyl-cysteine (NAC), regulates the physio-biochemical and gene expression level to reduce As toxicity in lettuce. According to our results, different NAC levels (125, 250 and 500 μM) significantly alleviated the growth inhibition and toxicity induced by As stress (20 mg/L). Shoot fresh weight, root fresh weight, shoot dry weight and root dry weight (33.05%, 55.34%, 17.97% and 46.20%, respectively) were decreased in plants grown in As-contaminated soils compared to lettuce plants grown in soils without the addition of As. However, NAC applications together with As stress increased these growth parameters. While the highest increase in shoot fresh and dry weight (58.31% and 37.85%, respectively) was observed in 250 μM NAC application, the highest increase in root fresh and dry weight (75.97% and 63.07%, respectively) was observed in 125 μM NAC application in plants grown in As-polluted soils. NAC application decreased the amount of ROS, MDA and H2O2 that increased with As stress, and decreased oxidative damage by regulating hormone levels, antioxidant and enzymes involved in nitrogen metabolism. According to gene expression profiles, LsHIPP28 and LsABC3 genes have shown important roles in reducing As toxicity in leaves. This study will provide insight for future studies on how NAC applications develop resistance to As stress in lettuce.
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Affiliation(s)
- Merve Yuce
- Atatürk University, Faculty of Agriculture, Department of Horticulture, Erzurum, Turkey.
| | - Ertan Yildirim
- Atatürk University, Faculty of Agriculture, Department of Horticulture, Erzurum, Turkey
| | - Melek Ekinci
- Atatürk University, Faculty of Agriculture, Department of Horticulture, Erzurum, Turkey
| | - Metin Turan
- Yeditepe University, Faculty of Economy and Administrative Sciences, Department of Agricultural Trade and Management, Istanbul, Turkey
| | - Emre Ilhan
- Erzurum Technical University, Faculty of Science, Department of Molecular Biology and Genetics, 25050, Erzurum, Turkey
| | - Murat Aydin
- Atatürk University, Faculty of Agriculture, Department of Agricultural Biotechnology, Erzurum, Turkey
| | - Guleray Agar
- Atatürk University, Faculty of Science, Department of Biology, Erzurum, Turkey
| | - Sumeyra Ucar
- Erzurum Technical University, Faculty of Science, Department of Molecular Biology and Genetics, 25050, Erzurum, Turkey
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Zhai Y, Zhou L, Qi H, Gao P, Zhang C. Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0124. [PMID: 38239738 PMCID: PMC10795768 DOI: 10.34133/plantphenomics.0124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/17/2023] [Indexed: 01/22/2024]
Abstract
Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.
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Affiliation(s)
- Yuanning Zhai
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
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3
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Luo S, Yuan X, Liang R, Feng K, Xu H, Zhao J, Wang S, Lan Y, Long Y, Deng H. Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 297:122720. [PMID: 37058840 DOI: 10.1016/j.saa.2023.122720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 05/14/2023]
Abstract
Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with Rp2 of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with Rp2 of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with Rp2 of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.
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Affiliation(s)
- Shuiyang Luo
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Xue Yuan
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
| | - Ruiqing Liang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Kunsheng Feng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haitao Xu
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Jing Zhao
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Shaokui Wang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yubin Lan
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Yongbing Long
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haidong Deng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
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Sanaeifar A, Yang C, de la Guardia M, Zhang W, Li X, He Y. Proximal hyperspectral sensing of abiotic stresses in plants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160652. [PMID: 36470376 DOI: 10.1016/j.scitotenv.2022.160652] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.
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Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Ce Yang
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, United States.
| | - Miguel de la Guardia
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain.
| | - Wenkai Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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Yang F, Sun J, Cheng J, Fu L, Wang S, Xu M. Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning. J FOOD PROCESS ENG 2023. [DOI: 10.1111/jfpe.14304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Fengyi Yang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jiehong Cheng
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Lvhui Fu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Simin Wang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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Huang SY, Mukundan A, Tsao YM, Kim Y, Lin FC, Wang HC. Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197308. [PMID: 36236407 PMCID: PMC9571956 DOI: 10.3390/s22197308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/15/2022] [Accepted: 09/23/2022] [Indexed: 05/08/2023]
Abstract
Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.
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Affiliation(s)
- Shuan-Yu Huang
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Beitun District, Taichung City 406053, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High Tech Innovations (AIM-HI), Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High Tech Innovations (AIM-HI), Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Youngjo Kim
- Department of Mechanical Engineering, Far Eastern University, P. Paredes St., Sampaloc, Manila 1015, Philippines
| | - Fen-Chi Lin
- Department of Ophthalmology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Correspondence: (F.-C.L.); (H.-C.W.)
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High Tech Innovations (AIM-HI), Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Correspondence: (F.-C.L.); (H.-C.W.)
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Yu S, Fan J, Lu X, Wen W, Shao S, Guo X, Zhao C. Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce. FRONTIERS IN PLANT SCIENCE 2022; 13:927832. [PMID: 35845657 PMCID: PMC9279906 DOI: 10.3389/fpls.2022.927832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed via multivariate analysis methods. In this study, two end-to-end deep learning models were developed based on 2D convolutional neural networks (2DCNN) and fully connected neural networks (FCNN; Deep2D and DeepFC, respectively) to rapidly and non-destructively predict the phenotyping traits of lettuces from spectral reflectance. Three linear and two nonlinear multivariate analysis methods were used to develop models to weigh the performance of the deep learning models. The models based on multivariate analysis methods require a series of manual feature extractions, such as pretreatment and wavelength selection, while the proposed models can automatically extract the features in relation to phenotyping traits. A visible near-infrared hyperspectral camera was used to image lettuce plants growing in the field, and the spectra extracted from the images were used to train the network. The proposed models achieved good performance with a determination coefficient of prediction ( R p 2 ) of 0.9030 and 0.8490 using Deep2D for soluble solids content and DeepFC for pH, respectively. The performance of the deep learning models was compared with five multivariate analysis method. The quantitative analysis showed that the deep learning models had higher R p 2 than all the multivariate analysis methods, indicating better performance. Also, wavelength selection and different pretreatment methods had different effects on different multivariate analysis methods, and the selection of appropriate multivariate analysis methods and pretreatment methods increased more time and computational cost. Unlike multivariate analysis methods, the proposed deep learning models did not require any pretreatment or dimensionality reduction and thus are more suitable for application in high-throughput plant phenotyping platforms. These results indicate that the deep learning models can better predict phenotyping traits of plants using spectral reflectance.
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Affiliation(s)
- Shuan Yu
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Song Shao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chunjiang Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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8
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Dong C, Yang C, Liu Z, Zhang R, Yan P, An T, Zhao Y, Li Y. Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging. SENSORS 2021; 21:s21238051. [PMID: 34884054 PMCID: PMC8659440 DOI: 10.3390/s21238051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/16/2021] [Accepted: 11/21/2021] [Indexed: 12/04/2022]
Abstract
Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.
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Affiliation(s)
- Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
| | - Chongshan Yang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Zhongyuan Liu
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Rentian Zhang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Peng Yan
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
| | - Ting An
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Yan Zhao
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
- Correspondence: (Y.Z.); (Y.L.)
| | - Yang Li
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China; (C.D.); (C.Y.); (Z.L.); (R.Z.); (P.Y.); (T.A.)
- Correspondence: (Y.Z.); (Y.L.)
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Cao Y, Sun J, Yao K, Xu M, Tang N, Zhou X. Nondestructive detection of lead content in oilseed rape leaves based on
MRF‐HHO‐SVR
and hyperspectral technology. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13793] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Yan Cao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Min Xu
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Ningqiu Tang
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
| | - Xin Zhou
- School of Electrical and Information Engineering of Jiangsu University Zhenjiang China
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