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Yin X, Wang H, Lu W, Ge L, Cui Y, Zhao Q, Liang J, Shen Q, Liu A, Xue J. Evaluation of Lipid Oxidation Characteristics in Salmon after Simulation of Cold Chain Interruption Using Rapid Evaporation Ionization Mass Spectrometry. J Agric Food Chem 2024; 72:1391-1404. [PMID: 38177996 DOI: 10.1021/acs.jafc.3c07423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Temperature fluctuations occurring during the cold chain logistics of salmon contribute to lipid oxidation. This study aimed to simulate cold chain interruption through freeze-thaw operations and evaluate the lipidomics data from salmon samples subjected to different numbers of freeze-thaw cycles by using rapid evaporative ionization mass spectrometry (REIMS) combined with an intelligent surgical knife (iKnife). The results indicated significant differences in the relative abundance of characteristic ions among the samples (p < 0.05). A total of 34 ions with variable importance for the projection values ≥1 were identified as potential biomarkers, including m/z 719.4233 ([PCC36:5-NH(CH3)3]-), m/z 337.3134 ([FAC22:1]-), m/z 720.4666 ([PEC35:6-H]-), m/z 309.2780 ([FAC20:1]-), m/z 777.4985 ([PCC40:4-NH(CH3)3]-), m/z 745.4421 ([PCC38:6-NH(CH3)3]-/[PEC38:6-NH3]-), m/z 747.4665 ([PCC38:5-NH(CH3)3]-/[PEC38:5-NH3]-), etc. The degree of lipid oxidation was found to be associated with the number of freeze-thaw cycles, exhibiting the most significant alterations in the relative abundance of lipid ions in the 8T samples. Additionally, sensory evaluation by the CIE-L*a*b* method and volatile analysis by headspace solid-phase microextraction gas chromatography-mass spectrometry demonstrated significant differences (p < 0.05) in color and odor among the salmon samples, with a correlation to the number of freeze-thaw cycles. The primary compounds responsible for alterations in salmon odor were aldehydes with lower odor thresholds. In summary, the iKnife-REIMS method accurately differentiated salmon muscle tissues based on varying levels of lipid oxidation, thus expanding the application of REIMS.
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Affiliation(s)
- Xuelian Yin
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
| | - Honghai Wang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
| | - Weibo Lu
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
| | - Lijun Ge
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
| | - Yiwei Cui
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
| | - Qiaoling Zhao
- Zhoushan Institute of Food & Drug Control, Zhoushan 316000, China
| | - Jingjing Liang
- Zhejiang Provincial Institute for Food and Drug Control, Hangzhou 310052, China
| | - Qing Shen
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
| | - Aichun Liu
- Testing Centre, Hangzhou Academy of Agricultural Sciences, Hangzhou310004,China
| | - Jing Xue
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou310018,China
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Jia W, Guo A, Bian W, Zhang R, Wang X, Shi L. Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways. Crit Rev Food Sci Nutr 2023:1-15. [PMID: 38127336 DOI: 10.1080/10408398.2023.2295016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.
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Affiliation(s)
- Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Agricultural Product Processing and Inspection Center, Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi, China
- Agricultural Product Quality Research Center, Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an, China
- Food Safety Testing Center, Shaanxi Sky Pet Biotechnology Co., Ltd, Xi'an, China
| | - Aiai Guo
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
| | - Wenwen Bian
- Agricultural Product Processing and Inspection Center, Shaanxi Testing Institute of Product Quality Supervision, Xi'an, Shaanxi, China
| | - Rong Zhang
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
| | - Xin Wang
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
| | - Lin Shi
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an, China
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Cui Y, Lu W, Xue J, Ge L, Yin X, Jian S, Li H, Zhu B, Dai Z, Shen Q. Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification. Food Chem 2023; 429:136986. [PMID: 37516053 DOI: 10.1016/j.foodchem.2023.136986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 07/02/2023] [Accepted: 07/22/2023] [Indexed: 07/31/2023]
Abstract
The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.
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Affiliation(s)
- Yiwei Cui
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Weibo Lu
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Jing Xue
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Lijun Ge
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Xuelian Yin
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Shikai Jian
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China
| | - Haihong Li
- Hangzhou Linping District Maternal & Child Health Care Hospital, Hangzhou 311113, China
| | - Beiwei Zhu
- National Engineering Research Center of Seafood, Collaborative Innovation Center of Provincial and Ministerial Co-Construction for Seafood Deep Processing, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
| | - Zhiyuan Dai
- Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China.
| | - Qing Shen
- Department of Clinical Laboratory, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China.
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Song G, Guo X, Li Q, Wang D, Yuan T, Li L, Shen Q, Zheng F, Gong J. Lipidomic fingerprinting of plasmalogen-loaded zein nanoparticles during in vitro multiple-stage digestion using rapid evaporative ionization mass spectrometry. Int J Biol Macromol 2023; 237:124193. [PMID: 36990418 DOI: 10.1016/j.ijbiomac.2023.124193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 03/09/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023]
Abstract
Plasmalogens (Pls) as the hydrophobic bioactive compound have shown potential in enhancing neurological disorders. However, the bioavailability of Pls is limited because of their poor water solubility during digestion. Herein, the hollow dextran sulfate/chitosan - coated zein nanoparticles (NPs) loaded with Pls was prepared. Subsequently, a novel in situ monitoring method utilizing rapid evaporative ionization mass spectrometry (REIMS) coupled with electric soldering iron ionization (ESII) was proposed to assess the lipidomic fingerprint alteration of Pls-loaded zein NPs during in vitro multiple-stage digestion in real time. A total of 22 Pls in NPs were structurally characterized and quantitatively analyzed, and the lipidomic phenotypes at each digestion stage were evaluated by multivariate data analysis. During multiple-stage digestion, Pls were hydrolyzed to lyso-Pls and free fatty acids by phospholipases A2, while the vinyl ether bond was retained at the sn-1 position. The result revealed that the contents of Pls groups were significantly reduced (p < 0.05). The multivariate data analysis results indicated that the ions at m/z 748.28, m/z 750.69, m/z 774.38, m/z 836.58, and etc. were the significant candidate contributors for monitoring the variation of Pls fingerprints during digestion. Results demonstrated that the proposed method exhibited potential for real-time tracking the lipidomic characteristics of nutritional lipid NPs digestion in the human gastrointestinal tract.
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Calderón C, Lämmerhofer M. Nutritional lipidomics for the characterization of lipids in food. Advances in Food and Nutrition Research 2023. [PMID: 37516469 DOI: 10.1016/bs.afnr.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.
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Cui Y, Ge L, Lu W, Wang S, Li Y, Wang H, Huang M, Xie H, Liao J, Tao Y, Luo P, Ding YY, Shen Q. Real-Time Profiling and Distinction of Lipids from Different Mammalian Milks Using Rapid Evaporative Ionization Mass Spectrometry Combined with Chemometric Analysis. J Agric Food Chem 2022; 70:7786-7795. [PMID: 35696488 DOI: 10.1021/acs.jafc.2c01447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The price of mammalian milk from different animal species varies greatly due to differences in their yield and nutritional value. Therefore, the authenticity of dairy products has become a hotspot issue in the market due to the replacement or partial admixture of high-cost milk with its low-cost analog. Herein, four common commercial varieties of milk, including goat milk, buffalo milk, Holstein cow milk, and Jersey cow milk, were successfully profiled and differentiated from each other by rapid evaporative ionization mass spectrometry (REIMS) combined with chemometric analysis. This method was developed as a real-time lipid fingerprinting technique. Moreover, the established chemometric algorithms based on multivariate statistical methods mainly involved principal component analysis, orthogonal partial least squares-discriminant analysis, and linear discriminant analysis as the screening and verifying tools to provide insights into the distinctive molecules constituting the four varieties of milk. The ions with m/z 229.1800, 243.1976, 257.2112, 285.2443, 299.2596, 313.2746, 341.3057, 355.2863, 383.3174, 411.3488, 439.3822, 551.5051, 577.5200, 628.5547, 656.5884, 661.5455, 682.6015, and 684.6146 were selected as potential classified markers. The results of the present work suggest that the proposed method could serve as a reference for recognizing dairy fraudulence related to animal species and expand the application field of REIMS technology.
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Affiliation(s)
- Yiwei Cui
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Lijun Ge
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Weibo Lu
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Shitong Wang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Yunyan Li
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Haifeng Wang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Min Huang
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Hujun Xie
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Jie Liao
- Zhejiang Huacai Testing Technology Co., Ltd., Shaoxing, Zhejiang 311800, China
| | - Ye Tao
- Hangzhou Linping District Maternal & Child Health Care Hospital, Hangzhou, Zhejiang 311113, China
| | - Pei Luo
- State Key Laboratories for Quality Research in Chinese Medicines, Faculty of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Yin-Yi Ding
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
| | - Qing Shen
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, Zhejiang 310012, China
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Fan X, Ma Y, Li M, Li Y, Sang X, Zhao Q. Thermal treatments and their influence on physicochemical properties of sea cucumbers: A comprehensive review. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.15922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Xinru Fan
- College of Food Science and Engineering Dalian Ocean University Dalian 116023 China
- Dalian Key Laboratory of Marine Bioactive Substances Development and High Value Utilization Dalian 116023 China
- Liaoning Provincial Marine Healthy Food Engineering Research Centre Dalian, 116023 China
- Liaoning Provincial Aquatic Products Analyzing, Testing and Processing Technology Scientific Service Centre Dalian China
- Collaborative Innovation Center of Provincial and Ministerial co‐construction for Marine Food Deep Processing Dalian Polytechnic University Dalian, 116034 China
| | - Yongsheng Ma
- College of Food Science and Engineering Dalian Ocean University Dalian 116023 China
- Dalian Key Laboratory of Marine Bioactive Substances Development and High Value Utilization Dalian 116023 China
- Liaoning Provincial Marine Healthy Food Engineering Research Centre Dalian, 116023 China
- Liaoning Provincial Aquatic Products Analyzing, Testing and Processing Technology Scientific Service Centre Dalian China
- Collaborative Innovation Center of Provincial and Ministerial co‐construction for Marine Food Deep Processing Dalian Polytechnic University Dalian, 116034 China
| | - Meng Li
- College of Food Science and Engineering Dalian Ocean University Dalian 116023 China
- Dalian Key Laboratory of Marine Bioactive Substances Development and High Value Utilization Dalian 116023 China
- Liaoning Provincial Marine Healthy Food Engineering Research Centre Dalian, 116023 China
- Liaoning Provincial Aquatic Products Analyzing, Testing and Processing Technology Scientific Service Centre Dalian China
- Collaborative Innovation Center of Provincial and Ministerial co‐construction for Marine Food Deep Processing Dalian Polytechnic University Dalian, 116034 China
| | - Ying Li
- College of Food Science and Engineering Dalian Ocean University Dalian 116023 China
- Dalian Key Laboratory of Marine Bioactive Substances Development and High Value Utilization Dalian 116023 China
- Liaoning Provincial Marine Healthy Food Engineering Research Centre Dalian, 116023 China
- Liaoning Provincial Aquatic Products Analyzing, Testing and Processing Technology Scientific Service Centre Dalian China
- Collaborative Innovation Center of Provincial and Ministerial co‐construction for Marine Food Deep Processing Dalian Polytechnic University Dalian, 116034 China
| | - Xue Sang
- College of Food Science and Engineering Dalian Ocean University Dalian 116023 China
- Dalian Key Laboratory of Marine Bioactive Substances Development and High Value Utilization Dalian 116023 China
- Liaoning Provincial Marine Healthy Food Engineering Research Centre Dalian, 116023 China
- Liaoning Provincial Aquatic Products Analyzing, Testing and Processing Technology Scientific Service Centre Dalian China
- Collaborative Innovation Center of Provincial and Ministerial co‐construction for Marine Food Deep Processing Dalian Polytechnic University Dalian, 116034 China
| | - Qiancheng Zhao
- College of Food Science and Engineering Dalian Ocean University Dalian 116023 China
- Dalian Key Laboratory of Marine Bioactive Substances Development and High Value Utilization Dalian 116023 China
- Liaoning Provincial Marine Healthy Food Engineering Research Centre Dalian, 116023 China
- Liaoning Provincial Aquatic Products Analyzing, Testing and Processing Technology Scientific Service Centre Dalian China
- Collaborative Innovation Center of Provincial and Ministerial co‐construction for Marine Food Deep Processing Dalian Polytechnic University Dalian, 116034 China
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