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Qiao M, Xia G, Xu Y, Cui T, Fan C, Li Y, Han S, Qian J. Prediction of moisture content for a single maize kernel based on viscoelastic properties. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:6594-6604. [PMID: 38520293 DOI: 10.1002/jsfa.13483] [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: 09/20/2023] [Revised: 01/03/2024] [Accepted: 03/23/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties. RESULTS Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency-sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force-time graphs and viscoelastic parameters were extracted based on the four-element Maxwell model. Then, viscoelastic parameters and data of force-time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force-time graphs. The results indicate that models utilizing the force-time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S-G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively. CONCLUSIONS This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Mengmeng Qiao
- College of Engineering, China Agricultural University, Beijing, People's Republic of China
- Universität Bremen, Bremen, Germany
| | | | - Yang Xu
- College of Engineering, China Agricultural University, Beijing, People's Republic of China
| | - Tao Cui
- College of Engineering, China Agricultural University, Beijing, People's Republic of China
| | - Chenlong Fan
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, People's Republic of China
| | - Yibo Li
- College of Engineering, China Agricultural University, Beijing, People's Republic of China
| | - Shaoyun Han
- College of Engineering, China Agricultural University, Beijing, People's Republic of China
| | - Jun Qian
- College of Engineering, China Agricultural University, Beijing, People's Republic of China
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Zhang Y, Liu S, Zhou X, Cheng J. Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology. Molecules 2024; 29:2968. [PMID: 38998920 PMCID: PMC11243293 DOI: 10.3390/molecules29132968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/18/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.
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Affiliation(s)
- Yurong Zhang
- School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China; (Y.Z.); (S.L.)
- Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
- Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
| | - Shuxian Liu
- School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China; (Y.Z.); (S.L.)
- Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
- Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
| | - Xianqing Zhou
- School of Food and Strategic Reserves, Henan University of Technology, Zhengzhou 450001, China; (Y.Z.); (S.L.)
- Engineering Research Center of Grain Storage and Security of Ministry of Education, Zhengzhou 450001, China
- Henan Provincial Engineering Technology Research Center on Grain Post Harvest, Zhengzhou 450001, China
| | - Junhu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
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Wu N, Weng S, Xiao Q, Jiang H, Zhao Y, He Y. Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 311:123889. [PMID: 38340442 DOI: 10.1016/j.saa.2024.123889] [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: 11/09/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/12/2024]
Abstract
Bakanae disease is a common seed-borne disease of rice. Rapid and accurate detection of bakanae pathogens carried by rice seeds is essential for the health of rice germplasm resources and the safety of rice production. This study aims to propose a general framework for species identification of major bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning. Seven varieties of rice seeds and four kinds of bakanae pathogens were analyzed. One-dimensional deep convolution neural networks (DCNNs) were first constructed using complete datasets. They achieved accuracies larger than 96.5% on the testing sets of most datasets, exceeding the conventional SVM and PLS-DA models. Then the developed DCNNs were transferred to detect other complete training sets. Most of the deep transferred models achieved comparable or even better performance than the original DCNNs. Two smaller target training sets were further constructed by randomly selecting spectra from the complete training sets. As the size of the target training sets reduced, the accuracies of all models on the corresponding testing sets also decreased gradually. Visualization analysis were conducted using the t-distribution stochastic neighbor embedding (t-SNE) algorithm and a proposed gradient-weighted activation wavelength (Grad-AW) method. They all showed that deep transfer learning could utilize the representation patterns in the source datasets to improve the target tasks. The overall results indicated that the bakanae pathogens were all identified accurately under our proposed framework. Hyperspectral imaging combined with deep transfer learning provided a new idea for the quality detection of large-scale seeds in modern seed industry.
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Affiliation(s)
- Na Wu
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Hubiao Jiang
- School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Yun Zhao
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
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Wang Z, An T, Wang W, Fan S, Chen L, Tian X. Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122679. [PMID: 37011441 DOI: 10.1016/j.saa.2023.122679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (Rp) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.
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Affiliation(s)
- Zheli Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ting An
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenchao Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Liping Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xi Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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Liu Q, Sun T, Wen X, Zeng M, Chen J. Detecting the Minimum Limit on Wheat Stripe Rust in the Latent Period Using Proximal Remote Sensing Coupled with Duplex Real-Time PCR and Machine Learning. PLANTS (BASEL, SWITZERLAND) 2023; 12:2814. [PMID: 37570968 PMCID: PMC10420842 DOI: 10.3390/plants12152814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/27/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Wheat stripe rust (WSR) is an airborne disease that causes severe damage to wheat. The rapid and early detection of WSR is essential for the prevention and control of this disease. The minimum detection limit (MDL) is one of the most important characteristics of quantitative methods that can be used to determine the scope and applicability of a measurement technique. Three wheat cultivars were inoculated with Puccinia striiformis f.sp. tritici (Pst), and a spectrometer was used to collect the canopy hyperspectral data, and the Pst content was obtained via a duplex real-time polymerase chain reaction (PCR) during the latent period, respectively. The disease index (DI) and molecular disease index (MDI) were calculated. The regression tree algorithm was used to determine the MDL of the Pst based on hyperspectral feature parameters. The logistic, IBK, and random committee algorithms were used to construct the classification model based on the MDL. The results showed that when the MDL was 0.7, IBK had the best recognition accuracy. The optimal model, which used the spectral feature R_2nd.dv ((the second derivative of the original hyperspectral value)) and the modeling ratio 2:1, had an accuracy of 91.67% on the testing set and 90.67% on the 10-fold cross-validation. Thus, during the latent period, the MDL of Pst was determined using hyperspectral technology as 0.7.
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Affiliation(s)
- Qi Liu
- Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China; (T.S.)
- Key Laboratory of Prevention and Control of Invasive Alien Species in Agriculture & Forestry of the North-Western Desert Oasis, Ministry of Agriculture and Rural Affairs, Urumqi 830052, China
| | - Tingting Sun
- Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China; (T.S.)
- Key Laboratory of Prevention and Control of Invasive Alien Species in Agriculture & Forestry of the North-Western Desert Oasis, Ministry of Agriculture and Rural Affairs, Urumqi 830052, China
| | - Xiaojie Wen
- Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China; (T.S.)
- Key Laboratory of Prevention and Control of Invasive Alien Species in Agriculture & Forestry of the North-Western Desert Oasis, Ministry of Agriculture and Rural Affairs, Urumqi 830052, China
| | - Minghao Zeng
- Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China; (T.S.)
- Key Laboratory of Prevention and Control of Invasive Alien Species in Agriculture & Forestry of the North-Western Desert Oasis, Ministry of Agriculture and Rural Affairs, Urumqi 830052, China
| | - Jing Chen
- Key Laboratory of the Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China; (T.S.)
- Key Laboratory of Prevention and Control of Invasive Alien Species in Agriculture & Forestry of the North-Western Desert Oasis, Ministry of Agriculture and Rural Affairs, Urumqi 830052, China
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Zhang H, Huang L, Xu C, Li Z, Yin X, Chen T, Wang Y, Li G. Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy. APPLIED OPTICS 2023; 62:5306-5316. [PMID: 37707236 DOI: 10.1364/ao.491777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/13/2023] [Indexed: 09/15/2023]
Abstract
This paper proposes a Panax notoginseng (P. notoginseng) quantitative analysis based on terahertz time-domain spectroscopy and two-dimensional correlation spectroscopy (2DCOS). By imposing temperature perturbation combined with 2DCOS, the one-dimensional absorbance spectra were transformed into 2DCOS synchronous spectra, which reflected the differences in characteristic information between different P. notoginseng contents more clearly. Then, the feature information of P. notoginseng contents was extracted from the 2DCOS synchronous spectra by a competitive adaptive reweighted sampling (CARS) method and was used to build a quantitative model combined with a support vector regression machine (SVR), called 2DCOS-CARS-SVR. We obtained a more accurate analysis result than the commonly used principal component analysis (PCA)-partial least squares regression (PLSR) and PCA-SVR. The prediction set correlation coefficient and root mean square error reached 0.9915% and 0.8160%, respectively.
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7
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Haghbin N, Bakhshipour A, Zareiforoush H, Mousanejad S. Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics. PLANT METHODS 2023; 19:53. [PMID: 37268945 PMCID: PMC10236597 DOI: 10.1186/s13007-023-01032-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1st derivative, and Savitzky-Golay 2nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits' firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky-Golay 1st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R2) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R2 values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.
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Affiliation(s)
- Najmeh Haghbin
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
| | - Hemad Zareiforoush
- Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
| | - Sedigheh Mousanejad
- Department of Plant Protection, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
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Hu Y, Ma B, Wang H, Li Y, Zhang Y, Yu G. Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging. Foods 2023; 12:foods12091773. [PMID: 37174311 PMCID: PMC10178042 DOI: 10.3390/foods12091773] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.
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Affiliation(s)
- Yating Hu
- College of Mechanical and Electrical Engineering, Shihezi University, 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
| | - Yujie Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yuanjia Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Guowei Yu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
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Xu L, Liu J, Wang C, Li Z, Zhang D. Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model. APPLIED OPTICS 2023; 62:2756-2765. [PMID: 37133116 DOI: 10.1364/ao.485099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.
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Identification of Transgenic Agricultural Products and Foods Using NIR Spectroscopy and Hyperspectral Imaging: A Review. Processes (Basel) 2023. [DOI: 10.3390/pr11030651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.
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Xu P, Sun W, Xu K, Zhang Y, Tan Q, Qing Y, Yang R. Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning. Foods 2022; 12:foods12010144. [PMID: 36613360 PMCID: PMC9818215 DOI: 10.3390/foods12010144] [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: 11/14/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.
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Affiliation(s)
- Peng Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Wenbin Sun
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Kang Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Yunpeng Zhang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Qian Tan
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Yiren Qing
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Ranbing Yang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
- Correspondence: ; Tel.: +86-0898-66267576
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Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.105048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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Cai Z, Huang W, Wang Q, Li J. Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models. FRONTIERS IN PLANT SCIENCE 2022; 13:952942. [PMID: 36035725 PMCID: PMC9399745 DOI: 10.3389/fpls.2022.952942] [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: 05/26/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
Citrus fruits are susceptible to fungal infection after harvest. To reduce the economic loss, it is necessary to reject the infected citrus fruit before storage and transportation. However, the infected area in the early stage of decay is almost invisible on the fruit surface, so the detection of early decayed citrus is very challenging. In this study, a structured-illumination reflectance imaging (SIRI) system combined with a visible light-emitting diode (LED) lamp and a monochrome camera was developed to detect early fungal infection in oranges. Under sinusoidal modulation illumination with spatial frequencies of 0.05, 0.15, and 0.25 cycles mm-1, three-phase-shifted images with phase offsets of - 2π/3, 0, and 2π/3 were acquired for each spatial frequency. The direct component (DC) and alternating component (AC) images were then recovered by image demodulation using a three-phase-shifting approach. Compared with the DC image, the decayed area can be clearly identified in the AC image and RT image (AC/DC). The optimal spatial frequency was determined by analyzing the AC image and pixel intensity distribution. Based on the texture features extracted from DC, AC, and RT images, four kinds of classification models including partial least square discriminant analysis (PLS-DA), support vector machine (SVM), least squares-support vector machine (LS-SVM), and k-nearest neighbor (KNN) were established to detect the infected oranges, respectively. Model optimization was also performed by extracting important texture features. Compared to all models, the PLS-DA model developed based on eight texture features of RT images achieved the optimal classification accuracy of 96.4%. This study showed for the first time that the proposed SIRI system combined with appropriate texture features and classification model can realize the early detection of decayed oranges.
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Affiliation(s)
- Zhonglei Cai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiangbo Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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14
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Two-wavelength image detection of early decayed oranges by coupling spectral classification with image processing. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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15
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An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
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Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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16
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Kamruzzaman M, Kalita D, Ahmed MT, ElMasry G, Makino Y. Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data. Anal Chim Acta 2022; 1202:339390. [DOI: 10.1016/j.aca.2021.339390] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/23/2021] [Accepted: 12/20/2021] [Indexed: 11/26/2022]
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17
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Jin B, Zhang C, Jia L, Tang Q, Gao L, Zhao G, Qi H. Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning. ACS OMEGA 2022; 7:4735-4749. [PMID: 35187294 PMCID: PMC8851633 DOI: 10.1021/acsomega.1c04102] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 01/20/2022] [Indexed: 05/15/2023]
Abstract
Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300-1400 nm and secondarily concentrated at approximately 1050-1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.
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Affiliation(s)
- Baichuan Jin
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Chu Zhang
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Liangquan Jia
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Qizhe Tang
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lu Gao
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Guangwu Zhao
- College
of Agriculture and Food Science, Zhejiang
Agriculture and Forestry University, Lin’an 311300, China
| | - Hengnian Qi
- School
of Information Engineering, Huzhou University, Huzhou 313000, China
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18
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Zhang L, Wang Y, Wei Y, An D. Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel. Food Chem 2022; 370:131047. [PMID: 34626928 DOI: 10.1016/j.foodchem.2021.131047] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/29/2021] [Accepted: 08/31/2021] [Indexed: 11/18/2022]
Abstract
Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data.
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Affiliation(s)
- Liu Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
| | - Yaqian Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
| | - Yaoguang Wei
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China.
| | - Dong An
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China.
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19
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Krauz L, Páta P, Kaiser J. Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging. SENSORS 2022; 22:s22020603. [PMID: 35062571 PMCID: PMC8781588 DOI: 10.3390/s22020603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/28/2021] [Accepted: 01/10/2022] [Indexed: 12/05/2022]
Abstract
Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.
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Affiliation(s)
- Lukáš Krauz
- Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech Republic; (P.P.); (J.K.)
- Correspondence: ; Tel.: +420-22435-2113
| | - Petr Páta
- Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech Republic; (P.P.); (J.K.)
| | - Jan Kaiser
- Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech Republic; (P.P.); (J.K.)
- FOMEI s.r.o., U Libeňského pivovaru 2015, 180 00 Prague, Czech Republic
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20
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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21
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Bai J, Zhang L, Cai J, Wang Y, Tian X. Laser light backscattering image to predict moisture content of mango slices with different ripeness during drying process. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13900] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Jun‐Wen Bai
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Lu Zhang
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Jian‐Rong Cai
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Yu‐Chi Wang
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
| | - Xiao‐Yu Tian
- School of Food and Biological Engineering Jiangsu University Zhenjiang China
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22
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Wang X, Zhang H, Song R, He X, Mao P, Jia S. Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis. SENSORS 2021; 21:s21175804. [PMID: 34502695 PMCID: PMC8434479 DOI: 10.3390/s21175804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/27/2021] [Accepted: 08/26/2021] [Indexed: 11/16/2022]
Abstract
Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.
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