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Hou S, Zhang Y, Yuan D, Feng X, Zhang Y. Determination of seawater COD spectra using double-loop contraction and sorted frog optimization. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2024; 89:1613-1629. [PMID: 38619893 DOI: 10.2166/wst.2024.101] [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: 06/26/2023] [Accepted: 02/02/2024] [Indexed: 04/17/2024]
Abstract
This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet-visible (UV-Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV-Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra.
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Affiliation(s)
- Shiwei Hou
- Qilu University of Technology (Shandong Academy of Sciences), Institute of Oceanographic Instrumentation, Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061 Qingdao, China
| | - Yingying Zhang
- Qilu University of Technology (Shandong Academy of Sciences), Institute of Oceanographic Instrumentation, Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061 Qingdao, China E-mail:
| | - Da Yuan
- Qilu University of Technology (Shandong Academy of Sciences), Institute of Oceanographic Instrumentation, Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061 Qingdao, China
| | - Xiandong Feng
- Qilu University of Technology (Shandong Academy of Sciences), Institute of Oceanographic Instrumentation, Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061 Qingdao, China
| | - Ying Zhang
- Qilu University of Technology (Shandong Academy of Sciences), Institute of Oceanographic Instrumentation, Shandong Provincial Key Laboratory of Ocean Environmental Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, No 37 Miaoling Road, 266061 Qingdao, China
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Zhang H, He Q, Yang C, Lu M, Liu Z, Zhang X, Li X, Dong C. Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion. SENSORS (BASEL, SWITZERLAND) 2023; 23:9684. [PMID: 38139529 PMCID: PMC10748152 DOI: 10.3390/s23249684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/29/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.
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Affiliation(s)
- Haowen Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Qinghai He
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China;
| | - Chongshan Yang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Min Lu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Zhongyuan Liu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Xiaojia Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China;
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China; (H.Z.); (C.Y.); (M.L.); (Z.L.); (X.Z.)
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
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Wang W, Man Z, Li X, Chen R, You Z, Pan T, Dai X, Xiao H, Liu F. Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress. JOURNAL OF HAZARDOUS MATERIALS 2023; 449:131010. [PMID: 36801724 DOI: 10.1016/j.jhazmat.2023.131010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (Rp=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (Rp=0.9161) for SP and CARS-ELM (Rp=0.9021) for MDA, all with Rp higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.
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Affiliation(s)
- Wei Wang
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Zun Man
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Zhengkai You
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaorong Dai
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo 315100, China
| | - Hang Xiao
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
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Zhang XW, Chen ZG, Jiao F. Application of the combination method based on RF and LE in near infrared spectral modeling. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122247. [PMID: 36549073 DOI: 10.1016/j.saa.2022.122247] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/04/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The dimensionality of near-infrared (NIR) spectral data is often extremely large. Dimensionality reduction of spectral data can effectively reduce the redundant information and correlation between spectral variables and simplify the model, which is crucial to increasing the model's performance. As a nonlinear feature extraction method, Laplacian Eigenmaps (LE) may preserve the local neighborhood information of the dataset, has high robustness, and is simple to compute. However, when the LE algorithm maps the data from high-dimensional space to low-dimensional space, it is often disturbed by irrelevant information and multicollinearity in the spectral data, which lowers the model's prediction performance. Random Frog (RF) algorithm can eliminate noise and collinearity in the spectrum. Therefore, before using the LE algorithm, we use the RF algorithm to eliminate irrelevant information in the spectrum and reduce the correlation between the spectra variables to increase the efficiency of the LE algorithm. We used the RF + LE algorithm to reduce the dimensionality of two public NIRS datasets (soil datasets and pharmaceutical tablets datasets) and compared it with RF and LE algorithms alone. We utilized Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) to establish regression models. The experimental findings demonstrate that compared with the RF algorithm and LE algorithm, the RF + LE combination method can reduce the dimension of spectral variables and model complexity, and improve regression models' prediction accuracy and stability. It is an effective dimensionality reduction method for the near-infrared spectrum.
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Affiliation(s)
- Xiao-Wen Zhang
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
| | - Zheng-Guang Chen
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
| | - Feng Jiao
- College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, China
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Sun Z, Pan H, Zuo M, Li J, Liang L, Ho CT, Zou X. Non-destructive assessment of equivalent umami concentrations in salmon using hyperspectral imaging technology combined with multivariate algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121890. [PMID: 36126621 DOI: 10.1016/j.saa.2022.121890] [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: 07/19/2022] [Revised: 09/05/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
This study utilized equivalent umami concentrations (EUC) to characterize umami intensity in salmon with different freeze-thaw times. A rapid and non-destructive method was established to determine EUC values in salmon which is based on hyperspectral imaging (HSI) system combined with multiple characteristic variable screening methods. The established CARS-PLS model showed greater advantages in correlating the reference values of spectral data with EUC in salmon with Rc of 0.9012, Rp of 0.9009, RMSECV of 0.82, and RMSEP of 0.88. The model was employed pixel-wise to visualize the distribution of EUC with different freeze-thaw times, which demonstrated the reduction of EUC value with the increasing of freeze-thaw times. Therefore, this reseearch showed hyperspectral imaging (HSI) system combined with chemometrics possesses a substantial capability to predict and visualize the EUC of salmon, which would provide an intuitive understanding of salmon quality prediction and detection.
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Affiliation(s)
- Zongbao Sun
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China.
| | - Haodong Pan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
| | - Min Zuo
- National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing, 100048, China
| | - Junkui Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
| | - Liming Liang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
| | - Chi-Tang Ho
- Department of Food Science, Rutgers University, New Brunswick, NJ 08903, USA.
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang City, 212013, China
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Wu X, Li G, Fu X, He F, Wu W. Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable
NIR
spectrometer. Food Energy Secur 2023. [DOI: 10.1002/fes3.447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Affiliation(s)
- Xin Wu
- Chongqing College of Electronic Engineering Chongqing China
| | - Guanglin Li
- College of Engineering and Technology Southwest University Chongqing China
| | - Xinglan Fu
- College of Engineering and Technology Southwest University Chongqing China
| | - Fengyun He
- College of Engineering and Technology Southwest University Chongqing China
| | - Weixin Wu
- Chongqing Academy of Metrology and Quality Inspection Chongqing China
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Fast Quantitative Modelling Method for Infrared Spectrum Gas Logging Based on Adaptive Step Sliding Partial Least Squares. ENERGIES 2022. [DOI: 10.3390/en15041325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Infrared spectroscopy (IR) quantitative analysis technology has shown excellent development potential in the field of oil and gas logging. However, due to the high overlap of the IR absorption peaks of alkane molecules and the offset of the absorption peaks in complex environments, the quantitative analysis of IR spectroscopy applied in the field puts forward higher requirements for modelling speed and accuracy. In this paper, a new type of fast IR spectroscopy quantitative analysis method based on adaptive step-sliding partial least squares (ASS-PLS) is designed. A sliding step control function is designed to change the position of the local PLS analysis model in the full spectrum band adaptively based on the relative change of the current root mean square error and the global minimum root-mean-square error for rapid modelling. The study in this paper reveals the influence of the position and width of the local modelling window on the performance, and how to quickly determine the optimal modelling window in an uncertain sample environment. The performance of the proposed algorithm has been compared with three typical quantitative analysis methods by experiments on an IR spectrum dataset of 400 alkane samples. The results show that this method has a fast quantitative modelling speed with high analysis accuracy and stability. It has important practical value for promoting IR spectroscopy gas-logging technology.
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Liu X, Sun Z, Zuo M, Zou X, Wang T, Li J. Quantitative detection of restructured steak adulteration based on hyperspectral technology combined with a wavelength selection algorithm cascade strategy. FOOD SCIENCE AND TECHNOLOGY RESEARCH 2021. [DOI: 10.3136/fstr.27.859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Xiaoyu Liu
- School of Food and Biological Engineering, Jiangsu University
| | - Zongbao Sun
- School of Food and Biological Engineering, Jiangsu University
| | - Min Zuo
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University
| | - Tianzhen Wang
- School of Food and Biological Engineering, Jiangsu University
| | - Junkui Li
- School of Food and Biological Engineering, Jiangsu University
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