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Kang Z, Fan R, Zhan C, Wu Y, Lin Y, Li K, Qing R, Xu L. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning. Molecules 2024; 29:682. [PMID: 38338424 PMCID: PMC10856461 DOI: 10.3390/molecules29030682] [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: 11/29/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
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Affiliation(s)
- Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rongsheng Fan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Chunyi Zhan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Youli Wu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Yi Lin
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Kunyu Li
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rui Qing
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
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Bakhshipour A. A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques. Food Sci Nutr 2023; 11:6116-6132. [PMID: 37823103 PMCID: PMC10563735 DOI: 10.1002/fsn3.3548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 10/13/2023] Open
Abstract
A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.
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Affiliation(s)
- Adel Bakhshipour
- Department of Biosystems Engineering, Faculty of Agricultural SciencesUniversity of GuilanRashtIran
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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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Mazni IA, Setumin S, Osman MS, Osman MK, Tahir MS. Characterising Colour Feature Descriptors for Ficus carica L. Ripeness Classification Based on Artificial Neural Network (ANN). PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY 2023. [DOI: 10.47836/pjst.31.2.07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Excessive feature dimensions impact the effectiveness of machine learning, computationally expensive and the analysis of feature correlations in the engineering area. This paper uses the colour descriptor to get the most optimal feature to improve time consumption and efficiency. This study investigated Ficus carica L. (figs) with three classification stages. The ripening classification of fig was examined using colour features descriptor with two different colour models, RGB and HSV. In addition, the machine learning classification model based on Artificial Neural Network (ANN) that utilised the Feed-Forward Neural Network (FFNN) model to classify the ripeness of fig is considered in this characterisation. Five different numbers of binning were characterised for RGB and HSV. Both colour feature descriptors were compared in terms of accuracy, sensitivity, precision, and time consumption to identify the dimension of the optimal feature. Based on the result, reducing the size of images will improve the time consumption with comparable accuracy. Moreover, the reduction of features dimension cannot be too small or too big due to inequitable enough to differentiate the ripeness stages and lead to a false error state. The optimal features dimension in binning for RGB was 8 (R/G/B) bins with 96.7% accuracy. Meanwhile, 96.7% accuracy for HSV at 15, 5, and 5 (H, S, V) bins as optimal colour features.
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Al-Dairi M, Pathare PB, Al-Yahyai R, Jayasuriya H, Al-Attabi Z. Postharvest quality, technologies, and strategies to reduce losses along the supply chain of banana: A review. Trends Food Sci Technol 2023. [DOI: 10.1016/j.tifs.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Early identification of mushy Halibut syndrome with hyperspectral image analysis. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Zhao Y, Kang Z, Chen L, Guo Y, Mu Q, Wang S, Zhao B, Feng C. Quality classification of kiwifruit under different storage conditions based on deep learning and hyperspectral imaging technology. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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8
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Peruvian Biopolymers (Sapote Gum, Tunta, and Potato Starches) as Suitable Coating Material to Extend the Shelf Life of Bananas. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02902-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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9
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He H, Sun DW, Wu Z, Pu H, Wei Q. On-off-on fluorescent nanosensing: Materials, detection strategies and recent food applications. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2021.11.029] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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10
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Yang H, Chen J, Yang S, Zhang T, Xia X, Zhang K, Deng S, He G, Gao H, He Q, Deng R. CRISPR/Cas14a-Based Isothermal Amplification for Profiling Plant MicroRNAs. Anal Chem 2021; 93:12602-12608. [PMID: 34506121 DOI: 10.1021/acs.analchem.1c02137] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
MicroRNAs (miRNAs) play key roles in biological processes in plants, such as stress resistance, yet can hardly be quantified by an enzyme-involved terminal polymerization process due to their 2'-O-methyl modifications at the 3'-terminal. Herein, we proposed a CRISPR/Cas14a-based rolling circle amplification (termed Cas14R) assay, allowing reverse transcription-free and demethylation-free detection of plant miRNAs with single-nucleotide resolution. The employment of target-templated rolling circle amplification circumvents the extension of the unaccessible 2'-O-methyl group at the 3'-terminal. Particularly, the activated Cas14a confers the trans-cleavage activity for identifying target single-stranded DNA sequences without the necessity of the protospacer adjacent motif, generalizing the detection of miRNA sequences and the integration of different isothermal amplification techniques. Ultimately, the Cas14R assay has been applied to profile miR156a to evaluate the ripeness process of banana, indicating its feasibility in analyzing the roles of miRNAs in biological processes of plants.
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Affiliation(s)
- Hao Yang
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Junbo Chen
- Analytical & Testing Center, Sichuan University, Chengdu, Sichuan 610064, China
| | - Sen Yang
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Ting Zhang
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Xuhan Xia
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Kaixiang Zhang
- School of Pharmaceutical Sciences, Key Laboratory of Targeting Therapy and Diagnosis for Critical Diseases, Zhengzhou University, Zhengzhou 450001, China
| | - Sha Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Guiping He
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Hong Gao
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Qiang He
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
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Physicochemical and mechanical properties during storage-cum maturity stages of raw harvested wild banana (Musa balbisiana, BB). JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00907-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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12
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He W, He H, Wang F, Wang S, Li R, Chang J, Li C. Rapid and Uninvasive Characterization of Bananas by Hyperspectral Imaging with Extreme Gradient Boosting (XGBoost). ANAL LETT 2021. [DOI: 10.1080/00032719.2021.1952214] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Weiwen He
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Fanglin Wang
- Institute of Forensic Science, Ministry of Public Security, Beijing, China
| | - Shuyue Wang
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Runkang Li
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Jing Chang
- Institute of Forensic Science, Ministry of Public Security, Beijing, China
| | - Chunyu Li
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
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Özdoğan G, Lin X, Sun DW. Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.044] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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14
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Discrimination of Tomato Maturity Using Hyperspectral Imaging Combined with Graph-Based Semi-supervised Method Considering Class Probability Information. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-020-01955-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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15
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Lin X, Xu JL, Sun DW. Evaluating drying feature differences between ginger slices and splits during microwave-vacuum drying by hyperspectral imaging technique. Food Chem 2020; 332:127407. [PMID: 32645677 DOI: 10.1016/j.foodchem.2020.127407] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/11/2023]
Abstract
This study aimed to investigate the difference between ginger slices (vertically cut) and splits (horizontally cut) during microwave-vacuum drying (MVD) procedures. MVD ginger slices showed a higher shrinkage rate and a higher hardness value, with a more porous structure of the surface layer. MVD ginger splits had higher rehydration rates at the first 15 min of the rehydration. Nine optimal wavelengths were selected by regression coefficients (RC) from the partial least squares regression (PLSR) model based on the raw data. A simplified PLSR model based on optimal wavelengths showed a good performance with a coefficient of determination in prediction (Rp2) of 0.973 and a root mean square error in prediction (RMSEP) of 4.63%. Texture features of grey level co-occurrence matrix (GLCM) of moisture prediction maps demonstrated a more uniform moisture distribution in MVD ginger slices than that in splits in the original geometry.
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Affiliation(s)
- Xiaohui Lin
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Jun-Li Xu
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland.
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Yan T, Duan L, Chen X, Gao P, Xu W. Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging. RSC Adv 2020; 10:41936-41945. [PMID: 35516565 PMCID: PMC9057915 DOI: 10.1039/d0ra06925f] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 11/01/2020] [Indexed: 11/21/2022] Open
Abstract
Radix Glycyrrhizae is used as a functional food and traditional medicine. The geographical origin of Radix Glycyrrhizae is a determinant factor influencing the chemical and physical properties as well as its medicinal and health effects. The visible/near-infrared (Vis/NIR) (376–1044 nm) and near-infrared (NIR) hyperspectral imaging (915–1699 nm) were used to identify the geographical origin of Radix Glycyrrhizae. Convolutional neural network (CNN) and recurrent neural network (RNN) models in deep learning methods were built using extracted spectra, with logistic regression (LR) and support vector machine (SVM) models as comparisons. For both spectral ranges, the deep learning methods, LR and SVM all exhibited good results. The classification accuracy was over 90% for the calibration, validation, and prediction sets by the LR, CNN, and RNN models. Slight differences in classification performances existed between the two spectral ranges. Further, interpretation of the CNN model was conducted to identify the important wavelengths, and the wavelengths with high contribution rates that affected the discriminant analysis were consistent with the spectral differences. Thus, the overall results illustrate that hyperspectral imaging with deep learning methods can be used to identify the geographical origin of Radix Glycyrrhizae, which provides a new basis for related research. Hyperspectral imaging provides an effective way to identify the geographical origin of Radix Glycyrrhizae to assess its quality.![]()
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Affiliation(s)
- Tianying Yan
- College of Information Science and Technology, Shihezi University Shihezi 832003 China .,Key Laboratory of Oasis Ecology Agriculture, Shihezi University Shihezi 832003 China
| | - Long Duan
- College of Information Science and Technology, Shihezi University Shihezi 832003 China .,Key Laboratory of Oasis Ecology Agriculture, Shihezi University Shihezi 832003 China
| | - Xiaopan Chen
- College of Information Science and Technology, Shihezi University Shihezi 832003 China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University Shihezi 832003 China .,Key Laboratory of Oasis Ecology Agriculture, Shihezi University Shihezi 832003 China
| | - Wei Xu
- College of Agriculture, Shihezi University Shihezi 832003 China .,Xinjiang Production and Construction Corps Key Laboratory of Special Fruits and Vegetables Cultivation Physiology and Germplasm Resources Utilization Shihezi 832003 China
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Shao Y, Wang Y, Xuan G, Gao Z, Hu Z, Gao C, Wang K. Assessment of Strawberry Ripeness Using Hyperspectral Imaging. ANAL LETT 2020. [DOI: 10.1080/00032719.2020.1812622] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Yuanyuan Shao
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai’an, China
- Ministry of Agriculture and Rural Affairs, Nanjing Research Institute of Agricultural Mechanization, Nanjing, China
| | - Yongxian Wang
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai’an, China
| | - Guantao Xuan
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai’an, China
| | - Zongmei Gao
- Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Prosser, WA, USA
| | - Zhichao Hu
- Ministry of Agriculture and Rural Affairs, Nanjing Research Institute of Agricultural Mechanization, Nanjing, China
| | - Chong Gao
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai’an, China
| | - Kaili Wang
- College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai’an, China
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19
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Pathmanaban P, Gnanavel B, Anandan SS. Recent application of imaging techniques for fruit quality assessment. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.10.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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