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Wang D, Zhang M, Jiang Q, Mujumdar AS. Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application. Foods 2024; 13:1662. [PMID: 38890891 PMCID: PMC11171494 DOI: 10.3390/foods13111662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/02/2024] [Accepted: 05/24/2024] [Indexed: 06/20/2024] Open
Abstract
The quality of fresh foods tends to deteriorate rapidly during harvesting, storage, and transportation. Intelligent detection equipment is designed to monitor and ensure product quality in the supply chain, measure appropriate food quality parameters in real time, and thus minimize quality degradation and potential financial losses. Through various available tracking devices, consumers can obtain actionable information about fresh food products. This paper reviews the recent progress in intelligent detection equipment for sensing the quality deterioration of fresh foods, including computer vision equipment, electronic nose, smart colorimetric films, hyperspectral imaging (HSI), near-infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), ultrasonic non-destructive testing, and intelligent tracing equipment. These devices offer the advantages of high speed, non-destructive operation, precision, and high sensitivity.
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Affiliation(s)
- Dianyuan Wang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; (D.W.); (Q.J.)
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi 214122, China
| | - Min Zhang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; (D.W.); (Q.J.)
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi 214122, China
| | - Qiyong Jiang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China; (D.W.); (Q.J.)
| | - Arun S. Mujumdar
- Department of Bioresource Engineering, Macdonald Campus, McGill University, Ste. Anne decBellevue, QC H9X 3V9, Canada;
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Mulowayi AM, Shen ZH, Nyimbo WJ, Di ZF, Fallah N, Zheng SH. Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis. Sci Rep 2024; 14:8514. [PMID: 38609452 PMCID: PMC11014857 DOI: 10.1038/s41598-024-59151-y] [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: 12/12/2023] [Accepted: 04/08/2024] [Indexed: 04/14/2024] Open
Abstract
The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.
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Affiliation(s)
- Arcel Mutombo Mulowayi
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Zhen Hui Shen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Engineering College, Fujian Jiangxia University, Fuzhou, 350108, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Witness Joseph Nyimbo
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Zhi Feng Di
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Nyumah Fallah
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Shu He Zheng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China.
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Li S, Li J, Wang Q, Shi R, Yang X, Zhang Q. Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2024; 15:1324753. [PMID: 38322826 PMCID: PMC10844474 DOI: 10.3389/fpls.2024.1324753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/08/2024] [Indexed: 02/08/2024]
Abstract
Introduction Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. Methods To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. Results The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). Discussion This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.
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Affiliation(s)
- Sheng Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Jiangbo Li
- 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
| | - Ruiyao Shi
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
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Gullifa G, Barone L, Papa E, Giuffrida A, Materazzi S, Risoluti R. Portable NIR spectroscopy: the route to green analytical chemistry. Front Chem 2023; 11:1214825. [PMID: 37818482 PMCID: PMC10561305 DOI: 10.3389/fchem.2023.1214825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.
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Affiliation(s)
- G. Gullifa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - L. Barone
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - E. Papa
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - A. Giuffrida
- Department of Chemical Sciences, University of Catania, Catania, Italy
| | - S. Materazzi
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
| | - R. Risoluti
- Department of Chemistry, “Sapienza” Università di Roma, Rome, Italy
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Wang Y, Han M, Xu Y, Wang X, Cheng M, Cui Y, Xiao Z, Qu J. Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:3854-3862. [PMID: 37496451 DOI: 10.1039/d3ay00774j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The quantitative determination of the soluble solid content (SSC) of potatoes using NIR spectroscopy is useful for predicting the internal and external quality of potato products, especially fried products. In this study, the effect of peel on the partial least squares regression (PLSR) quantitative prediction of potato SSC was investigated by transmission and reflection. The results show that the variable sorting for normalization (VSN) pre-processing method improved model accuracy. Additive multiplicative scattering effects and intensity drift interference of the peels were reduced. The model accuracy reached a correlation coefficient of prediction (RP) of 0.85. The selection algorithm using variable combination population analysis and iterative retention of information variables (VCPA-IRIV) demonstrated that peel increases unnecessary information. When the effect of irrelevant variables was reduced, the results reached RP = 0.88 and the root mean square error of prediction (RMSEP) = 0.25 in the transmission mode was close to that of the full-wavelength peeled PLSR model (RP = 0.89 and RMSEP = 0.25). This indicates that the use of the combined algorithm (VSN-VCPA-IRIV) reduces the effect of the peel and enables samples with a peel to still be predicted accurately in the full-wavelength model. It also improves detection efficiency through the extraction of the necessary variables and optimizes the stability and accuracy of the model.
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Affiliation(s)
- Yi Wang
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Minjie Han
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Yingchao Xu
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Xiangyou Wang
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Meng Cheng
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Yingjun Cui
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Zhengwei Xiao
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
| | - Junzhe Qu
- College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China.
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Zhang L, Yu L, Zhao Z, Li P, Tan S. Chitosan oligosaccharide as a plant immune inducer on the Passiflora spp. (passion fruit) CMV disease. FRONTIERS IN PLANT SCIENCE 2023; 14:1131766. [PMID: 36814757 PMCID: PMC9939820 DOI: 10.3389/fpls.2023.1131766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
Cucumber mosaic virus (CMV), one of the main viruses, is responsible for Passiflora spp. (passion fruit) virus diseases, which negatively affect its planting, cultivation, and commercial quality. In this study, a laboratory anti-CMV activity screening model for Passiflora spp. CMV disease was first established. Then, the effects of different antiviral agents of chitosan oligosaccharide (COS), dufulin (DFL), and ningnanmycin (Ning) on CMV virulence rate in Passiflora spp. were determined. The virulence rate and anti-CMV activity in Passiflora spp. treated with COS were 50% and 45.48%, respectively, which were even better than those of DFL (66.67% and 27.30%, respectively) and Ning (83.30% and 9.17%, respectively). Field trials test results showed COS revealed better average control efficiency (47.35%) against Passiflora spp. CMV disease than those of DFL (40.93%) and Ning (33.82%), indicating that COS is effective in the control of the Passiflora spp. CMV disease. Meanwhile, the nutritional quality test results showed that COS could increase the contents of soluble solids, titratable acids, vitamin C, and soluble proteins in Passiflora spp. fruits as well as enhance the polyphenol oxidase (PPO), superoxide dismutase (SOD), and peroxidase (POD) activity in the leaves of Passiflora spp. seedlings. In addition, the combined transcriptome and proteome analysis results showed that COS mainly acted on the Brassinosteroids (BRs) cell signaling pathway, one of plant hormone signal transduction pathway, in Passiflora spp., thus activating the up-regulated expression of TCH4 and CYCD3 genes to improve the resistance to CMV disease. Therefore, our study results demonstrated that COS could be used as a potential plant immune inducer to control the Passiflora spp. CMV disease in the future.
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Affiliation(s)
- Liqun Zhang
- School of Liquor and Food Engineering, Guizhou University, Guiyang, Guizhou, China
| | - Lu Yu
- School of Liquor and Food Engineering, Guizhou University, Guiyang, Guizhou, China
| | - Zhi Zhao
- School of Liquor and Food Engineering, Guizhou University, Guiyang, Guizhou, China
| | - Pei Li
- School of Liquor and Food Engineering, Guizhou University, Guiyang, Guizhou, China
- Qiandongnan Engineering and Technology Research Center for Comprehensive Utilization of National Medicine, Kaili University, Kaili, China
| | - Shuming Tan
- School of Liquor and Food Engineering, Guizhou University, Guiyang, Guizhou, China
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Hao Y, Zhang C, Li X, Lei Z. Establishment of online deep learning model for insect-affected pests in “Yali” pears based on visible-near-infrared spectroscopy. Front Nutr 2022; 9:1026730. [PMID: 36352901 PMCID: PMC9637874 DOI: 10.3389/fnut.2022.1026730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 09/26/2022] [Indexed: 11/26/2022] Open
Abstract
Insect-affected pests, as an important indicator in inspection and quarantine, must be inspected in the imports and exports of fruits like “Yali” pears (a kind of duck head-shaped pear). Therefore, the insect-affected pests in Yali pears should be previously detected in an online, real-time, and accurate manner during the commercial sorting process, thus improving the import and export trade competitiveness of Yali pears. This paper intends to establish a model of online and real-time discrimination for recessive insect-affected pests in Yali pears during commercial sorting. The visible-near-infrared (Vis-NIR) spectra of Yali samples were pretreated to reduce noise interference and improve the spectral signal-to-noise ratio (SNR). The Competitive Adaptive Reweighted Sampling (CARS) method was adopted for the selection of feature modeling variables, while Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Block Attention Module-Convolutional Neural Networks (CBAM-CNN) were used to establish online discriminant models. T-distributed Stochastic Neighbor Embedding (T-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the clustering and attention distribution display of spectral features of deep learning models. The results show that the online discriminant model obtained by SGS pretreatment combined with the CBAM-CNN deep learning method exhibits the best performance, with 96.88 and 92.71% accuracy on the calibration set and validation set, respectively. The prediction time of a single pear is 0.032 s, which meets the online sorting requirements.
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Affiliation(s)
- Yong Hao
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China
- Key Laboratory of Conveyance Equipment of the Ministry of Education, Nanchang, China
- *Correspondence: Yong Hao,
| | - Chengxiang Zhang
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China
| | - Xiyan Li
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China
| | - Zuxiang Lei
- School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, China
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Ming J, Liu M, Lei M, Huang B, Chen L. Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:978937. [PMID: 36119610 PMCID: PMC9478200 DOI: 10.3389/fpls.2022.978937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R 2) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g-1, R 2 of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.
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Affiliation(s)
- Jing Ming
- Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Hubei University of Chinese Medicine, Wuhan, China
| | - Mingjia Liu
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Sciences, Xiangyang, China
| | - Mi Lei
- Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Hubei University of Chinese Medicine, Wuhan, China
| | - Bisheng Huang
- Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Hubei University of Chinese Medicine, Wuhan, China
| | - Long Chen
- Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Sciences, Xiangyang, China
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