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Liu W, Zhang L, Karrar E, Wu D, Chen C, Zhang Z, Li J. A cooperative combination of non-targeted metabolomics and electronic tongue evaluation reveals the dynamic changes in metabolites and sensory quality of radish during pickling. Food Chem 2024; 446:138886. [PMID: 38422641 DOI: 10.1016/j.foodchem.2024.138886] [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: 12/03/2023] [Revised: 02/18/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
Pickled radish is a traditional fermented food with a unique flavor after long-term preservation. This study analyzed the organoleptic and chemical characteristics of pickled radish from different years to investigate quality changes during pickling. The results showed that the sourness, saltiness, and aftertaste-bitterness increased after pickling, and bitterness and astringency decreased. The levels of free amino acids, soluble sugars, total phenols, and total flavonoids initially decreased during pickling but increased with prolonged pickling. The diversity of organic acids also increased over time. Through non-targeted metabolomics analysis, 349 differential metabolites causing metabolic changes were identified to affect the quality formation of pickled radish mainly through amino acid metabolism, phenylpropane biosynthesis and lipid metabolism. Correlation analysis showed that L*, soluble sugars, lactic acid, and acetic acid were strongly associated with taste quality. These findings provide a theoretical basis for standardizing and scaling up traditional pickled radish production.
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Affiliation(s)
- Wenliang Liu
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China
| | - Lingyu Zhang
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Xiamen 361021, China
| | - Emad Karrar
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Xiamen 361021, China
| | - Daren Wu
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Xiamen 361021, China
| | - Chaoxiang Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Xiamen 361021, China
| | - Zhengxiao Zhang
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Xiamen 361021, China
| | - Jian Li
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Engineering Technology Research Center of Marine Functional Food, Xiamen 361021, China.
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Cao Y, Xu M, Chen Q, Wu D, Lu J, Cai G. Potential nutritional and functional matters in yeast culture prepared by soybean meal fermentation. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 38963133 DOI: 10.1002/jsfa.13713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/20/2024] [Accepted: 06/16/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Yeast culture (YC) is a product fermented on a specific medium, which is a type of postbiotic of anaerobic solid-state fermentation. Although YC has positive effects on the animal growth and health, it contains a variety of beneficial metabolites as dark matter, which have not been quantified. In the present study, liquid chromatography-tandem mass spectrometry is employed to identify the unknown metabolites. Following their identification, the important chemicals are quantified using HPLC-diode array detection methods. RESULTS Non-targeted metabolomics studies showed that 670 metabolites in total were identified in YC, of which 23 metabolites significantly increased, including organic acids, amino acids, nucleosides and purines, isoflavones, and other substances. The chemical quantitative analysis showed that the contents of succinic acid, aminobutyric acid, glutamine, purine and daidzein increased by 84.42%, 51.07%, 100%, 68.85% and 4.60%, respectively. CONCLUSION Therefore, the use of non-targeted metabolomics combined with chemical quantitative analysis to reveal the nutritional and functional substances of YC could help to elucidate the postbiotic mechanism and provide theoretical support for the regulation of the directional accumulation of beneficial metabolites. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Yazhuo Cao
- School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, China
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, China
| | - Minwei Xu
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Qiong Chen
- Department of Plant Sciences, North Dakota State University, Fargo, ND, USA
| | - Dianhui Wu
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, China
| | - Jian Lu
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, China
| | - Guolin Cai
- School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, China
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, China
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Liu Y, Xiao X, Wang Z, Shan X, Liu G, Wei B. Metabolomic analysis of black sesame seeds: Effects of processing and active compounds in antioxidant and anti-inflammatory properties. Food Res Int 2024; 176:113789. [PMID: 38163704 DOI: 10.1016/j.foodres.2023.113789] [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: 10/17/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
Black sesame seeds (BSS) have been recognized as a functional food due to their nutritional and therapeutic value for many years. In China, BSS is traditionally processed and consumed through two methods, namely, nine steaming nine sun-drying and stir-frying. The present study aimed to evaluate the effects of these processing techniques on the antioxidant and anti-inflammatory activities of BSS. UPLC-QTOF/MS was used for untargeted metabolomics to analyze the composition changes. The results indicated that the different samples had good antioxidant and anti-inflammatory activities, but thermal treatment reduced their activities. Untargeted metabolomics identified a total of 196 metabolites. Molecular docking studies targeting proteins associated with inflammation (iNOS) demonstrated that compounds acting as inhibitors were significantly reduced under both treatments. These results indicate that both nine steaming nine sun-drying and stir-frying lead to substantial loss of antioxidant, anti-inflammatory, and bioactive metabolites in BSS, which provides an important reference for its rational utilization.
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Affiliation(s)
- Yu Liu
- School of Pharmacy, China Medical University, No.77 Puhe Road, Shenyang 110122, PR China
| | - Xia Xiao
- School of Pharmacy, China Medical University, No.77 Puhe Road, Shenyang 110122, PR China
| | - Ziwei Wang
- School of Pharmacy, China Medical University, No.77 Puhe Road, Shenyang 110122, PR China
| | - Xiao Shan
- School of Pharmacy, China Medical University, No.77 Puhe Road, Shenyang 110122, PR China
| | - Guojie Liu
- Department of Chemistry, School of Forensic Medicine, China Medical University, No.77 Puhe Road, Shenyang 110122, PR China.
| | - Binbin Wei
- School of Pharmacy, China Medical University, No.77 Puhe Road, Shenyang 110122, PR China.
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Wang Y, Wang G, Zhao Y, Wang C, Chen C, Ding Y, Lin J, You J, Gao S, Pang X. A deep learning model for predicting multidrug-resistant organism infection in critically ill patients. J Intensive Care 2023; 11:49. [PMID: 37941079 PMCID: PMC10633993 DOI: 10.1186/s40560-023-00695-y] [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: 08/31/2023] [Accepted: 10/12/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients. METHODS This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively. CONCLUSIONS This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.
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Affiliation(s)
- Yaxi Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Gang Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Yuxiao Zhao
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Cheng Wang
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Chen Chen
- School of Nursing, Qingdao University, No. 38 Dengzhou Road, Qingdao, 266021, China
| | - Yaoyao Ding
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Jing Lin
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Jingjing You
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China
| | - Silong Gao
- Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
| | - Xufeng Pang
- Department of Hospital-Acquired Infection Control, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China.
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