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Guo M, Lin H, Wang K, Cao L, Sui J. Data fusion of near-infrared and Raman spectroscopy: An innovative tool for non-destructive prediction of the TVB-N content of salmon samples. Food Res Int 2024; 189:114564. [PMID: 38876596 DOI: 10.1016/j.foodres.2024.114564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/16/2024]
Abstract
Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings (-20, 0, -4, 20 °C, and dynamic temperature) using near-infrared (NIR) and Raman spectroscopy. A partial least square support vector machine (LSSVM) regression model was established through the integration of NIR and Raman spectral data using low-level data fusion (LLDF) and mid-level data fusion (MLDF) strategies. Notably, compared to a single spectrum analysis, the LLDF approach provided the most accurate prediction model, achieving an R2P of 0.910 and an RMSEP of 1.922 mg/100 g. Furthermore, MLDF models based on 2D-COS and VIP achieved R2P values of 0.885 and 0.906, respectively. These findings demonstrated the effectiveness of the proposed method for precise quantitative detection of salmon TVB-N, laying a technical foundation for the exploration of similar approaches in the study of other meat products. This approach has the potential to assess and monitor the freshness of seafood, ensuring consumer safety and enhancing product quality.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China; College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang 843100, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China.
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
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2
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Chen J, Zhang J, Wang N, Xiao B, Sun X, Li J, Zhong K, Yang L, Pang X, Huang F, Chen A. Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring. Food Chem 2024; 445:138755. [PMID: 38387318 DOI: 10.1016/j.foodchem.2024.138755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.
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Affiliation(s)
- Jiaci Chen
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Juan Zhang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Nan Wang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Bin Xiao
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Xiaoyun Sun
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Jiapeng Li
- China Meat Research Center, Beijing, China.
| | - Ke Zhong
- Shandong Academy of Grape, Jinan, China.
| | - Longrui Yang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Xiangyi Pang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Fengchun Huang
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Ailiang Chen
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing, China.
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Ni Y, Zhao J, Zhang W, Tan L, Li Y, Li H, Xu B. Efficient Fresh Lamp Light-Harvesting Films with the Self-Activating Continuous and Recyclable Bactericidal Ability for Ultrapersistent Freshness of Perishable Muscle Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:2756-2764. [PMID: 38048174 DOI: 10.1021/acs.jafc.3c07299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
A large quantity of perishable muscle food is being wasted due to harmful bacteria infestation during the sales and circulation each year and facing challenges. In this study, a self-activated bactericidal active film (PLA/g-C3N4@PCN-224) responsive to fresh lamp light was prepared, which showed excellent hydrophobicity, water vapor resistance, and thermal stability. Due to the synergistic effect between light-induced reactive oxygen species and the high specific surface area of g-C3N4@PCN-224, this film still maintains 99.99% bactericidal efficacy against Escherichia coli and Staphylococcus aureus after 10 days of continuous bactericidal activity test. The results of cell and hemolysis experiments indicated that the film was safe and nontoxic and can effectively preserve fresh pork for 7 days. Moreover, the film also exhibited a recyclable and efficient killing activity. A strategy for achieving ultrapersistent freshness of perishable muscle food was provided.
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Affiliation(s)
- Yongsheng Ni
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
| | - Jinsong Zhao
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
| | - Wendi Zhang
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
| | - Lijun Tan
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
| | - Yumeng Li
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
| | - Haoran Li
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
| | - Baocai Xu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, Anhui Province China
- Engineering Research Center of Bio-Process of Ministry of Education, School of Food & Biological Engineering, Hefei University of Technology, Hefei 230601, Anhui Province, China
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Jin P, Fu Y, Niu R, Zhang Q, Zhang M, Li Z, Zhang X. Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy. Foods 2023; 12:2756. [PMID: 37509847 PMCID: PMC10379075 DOI: 10.3390/foods12142756] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/05/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky-Golay smoothing, SG; Savitzky-Golay 1 derivative, SG-1st; and Savitzky-Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R2p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R2p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.
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Affiliation(s)
- Peilin Jin
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Yifan Fu
- Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China
| | - Renzhong Niu
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Qi Zhang
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Mingyue Zhang
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Zhigang Li
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Xiaoshuan Zhang
- Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China
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Wang W, Li T, Chen J, Ye Y. Inhibition of Salmonella Enteritidis by Essential Oil Components and the Effect of Storage on the Quality of Chicken. Foods 2023; 12:2560. [PMID: 37444298 PMCID: PMC10341335 DOI: 10.3390/foods12132560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
This research investigates the antibacterial potential of plant essential oil components including thymol, carvacrol, citral, cinnamaldehyde, limonene, and β-pinene against Salmonella Enteritidis (S. Enteritidis). Through the determination of minimum inhibitory concentration, three kinds of natural antibacterial agents with the best inhibitory effect on S. Enteritidis were determined, namely thymol (128 μg/mL), carvacrol (256 μg/mL), and cinnamaldehyde (128 μg/mL). Physical, chemical, microbial, and sensory characteristics were regularly monitored on days 0, 2, 4, and 6. The findings of this study reveal that both thymol at MIC of 128 μg/mL and carvacrol at MIC of 256 μg/mL not only maintained the sensory quality of chicken, but also decreased the pH, moisture content, and TVB-N value. Additionally, thymol, carvacrol and cinnamaldehyde successfully inhibited the formation of S. Enteritidis biofilm, thereby minimizing the number of S. Enteritidis and the total aerobic plate count in chicken. Hence, thymol, carvacrol, and cinnamaldehyde have more effective inhibitory activities against S. Enteritidis, which can effectively prevent the spoilage of chicken and reduce the loss of its functional components.
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Affiliation(s)
- Wu Wang
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China; (T.L.); (J.C.); (Y.Y.)
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Cho JS, Choi B, Lim JH, Choi JH, Yun DY, Park SK, Lee G, Park KJ, Lee J. Determination of Freshness of Mackerel ( Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging. Foods 2023; 12:2305. [PMID: 37372515 DOI: 10.3390/foods12122305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/02/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023] Open
Abstract
Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.
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Affiliation(s)
- Jeong-Seok Cho
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Byungho Choi
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
- Department of Food Science and Technology, Chung-Ang University, Anseong 17546, Republic of Korea
| | - Jeong-Ho Lim
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Jeong Hee Choi
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Dae-Yong Yun
- Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Seul-Ki Park
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Gyuseok Lee
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Kee-Jai Park
- Smart Food Manufacturing Project Group, Korea Food Research Institute, Wanju 55365, Republic of Korea
| | - Jihyun Lee
- Department of Food Science and Technology, Chung-Ang University, Anseong 17546, Republic of Korea
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Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse. Meat Sci 2023; 200:109169. [PMID: 37001445 DOI: 10.1016/j.meatsci.2023.109169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 03/14/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023]
Abstract
Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R2 in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.
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Shi Y, Wang Y, Hu X, Li Z, Huang X, Liang J, Zhang X, Zhang D, Zou X, Shi J. Quantitative characterization of the diffusion behavior of sucrose in marinated beef by HSI and FEA. Meat Sci 2023; 195:109002. [DOI: 10.1016/j.meatsci.2022.109002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 11/09/2022]
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Zou W, Peng Y, Yang D, Zuo J, Li Y, Guo Q. An Intelligent Detector for Sensing Pork Freshness In Situ Based on a Multispectral Technique. BIOSENSORS 2022; 12:998. [PMID: 36354507 PMCID: PMC9688451 DOI: 10.3390/bios12110998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Fresh pork is prone to spoilage during storage, transportation, and sale, resulting in reduced freshness. The total viable count (TVC) and total volatile basic nitrogen (TVB-N) content are key indicators for evaluating the freshness of fresh pork, and when they reach unacceptable limits, this seriously threatens dietary safety. To realize the on-site, low-cost, rapid, and non-destructive testing and evaluation of fresh pork freshness, a miniaturized detector was developed based on a cost-effective multi-channel spectral sensor. The partial least squares discriminant analysis (PLS-DA) model was used to distinguish fresh meat from deteriorated meat. The detector consists of microcontroller, light source, multi-channel spectral sensor, heat-dissipation modules, display system, and battery. In this study, the multispectral data of pork samples with different freshness levels were collected by the developed detector, and its ability to distinguish pork freshness was based on different spectral shape features (SSF) (spectral ratio (SR), spectral difference (SD), and normalized spectral intensity difference (NSID)) were compared. The experimental results show that compared with the original multispectral modeling, the performance of the model based on spectral shape features is significantly improved. The model established by optimizing the spectral shape feature variables has the best performance, and the discrimination accuracy of its prediction set is 91.67%. In addition, the validation accuracy of the optimal model was 86.67%, and its sensitivity and variability were 87.50% and 85.71%, respectively. The results show that the detector developed in this study is cost-effective, compact in its structure, stable in its performance, and suitable for the on-site digital rapid non-destructive testing of freshness during the storage, transportation, and sale of fresh pork.
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Xu W, Zhang F, Wang J, Ma Q, Sun J, Tang Y, Wang J, Wang W. Real-Time Monitoring of the Quality Changes in Shrimp ( Penaeus vannamei) with Hyperspectral Imaging Technology during Hot Air Drying. Foods 2022; 11:3179. [PMID: 37430926 PMCID: PMC9601712 DOI: 10.3390/foods11203179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/17/2022] Open
Abstract
Hot air drying is the most common processing method to extend shrimp's shelf life. Real-time monitoring of moisture content, color, and texture during the drying process is important to ensure product quality. In this study, hyperspectral imaging technology was employed to acquire images of 104 shrimp samples at different drying levels. The water distribution and migration were monitored by low field magnetic resonance and the correlation between water distribution and other quality indicators were determined by Pearson correlation analysis. Then, spectra were extracted and competitive adaptive reweighting sampling was used to optimize characteristic variables. The grey-scale co-occurrence matrix and color moments were used to extract the textural and color information from the images. Subsequently, partial least squares regression and least squares support vector machine (LSSVM) models were established based on full-band spectra, characteristic spectra, image information, and fused information. For moisture, the LSSVM model based on full-band spectra performed the best, with residual predictive deviation (RPD) of 2.814. For L*, a*, b*, hardness, and elasticity, the optimal models were established by LSSVM based on fused information, with RPD of 3.292, 2.753, 3.211, 2.807, and 2.842. The study provided an in situ and real-time alternative to monitor quality changes of dried shrimps.
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Affiliation(s)
| | | | | | | | | | | | | | - Wenxiu Wang
- College of Food Science and Technology, Hebei Agricultural University, Baoding 071000, China
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