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Du T, Xu Y, Xu X, Xiong S, Zhang L, Dong B, Huang J, Huang T, Xiao M, Xiong T, Xie M. ACE inhibitory peptides from enzymatic hydrolysate of fermented black sesame seed: Random forest-based optimization, screening, and molecular docking analysis. Food Chem 2024; 437:137921. [PMID: 37944395 DOI: 10.1016/j.foodchem.2023.137921] [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: 08/25/2023] [Revised: 10/12/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
In this study, black sesame seeds were fermented by Lactobacillus Plantarum NCU116 and then hydrolyzed using acid protease to improve Angiotensin-I-converting enzyme (ACE) inhibitory activity. The random forest-particle swarm optimization (RF-PSO) model was applied to predict the ACE inhibitory activity during the hydrolysis process based on the experimental data. After separating by adsorption chromatography, gel filtration chromatography, and reversed phased-high performance liquid chromatography and then screening in silico method, eight peptides were identified from fermented black sesame seed hydrolysates as ITAPHW, SLPNYHPSPR, QYLPR, IRPNGL, YHNAPIL, LSYPR, GFAGDDAPRA, and LDPNPRSF with IC50 values of 51.69 μM, 146.67 μM, 655.02 μM, 752.60 μM, 1.02 mM, 2.01 mM, 1.97 mM, and 3.43 mM, respectively. ITAPHW and SLPNYHPSPR exhibited high antioxidant activity and inhibited the ACE activity in a non-competitive pattern. Molecular docking revealed that the strong ACE inhibition of ITAPHW and SLPNYHPSPR is probably attributed to the interaction with Zn2+ of ACE.
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Affiliation(s)
- Tonghao Du
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
| | - Yazhou Xu
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
| | - Xiaoyan Xu
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
| | - Shijin Xiong
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
| | - Linli Zhang
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
| | - Biao Dong
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
| | - Jinqing Huang
- Institute of Agricultural Products Processing, Jiangxi Academy of Agricultural Sciences, No. 602 Nanlian Road, Nanchang 330200, China
| | - Tao Huang
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China; International Institute of Food Innovation, Nanchang University, Luozhu Road, Xiaolan Economic and Technological Development Zone, Nanchang 330052, China
| | - Muyan Xiao
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China; International Institute of Food Innovation, Nanchang University, Luozhu Road, Xiaolan Economic and Technological Development Zone, Nanchang 330052, China
| | - Tao Xiong
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China; State Key Laboratory of Food Science and Resources, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China.
| | - Mingyong Xie
- School of Food Science and Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China; State Key Laboratory of Food Science and Resources, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi 330047, China
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Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA. Application of Artificial Intelligence in Food Industry—a Guideline. FOOD ENGINEERING REVIEWS 2021. [PMCID: PMC8350558 DOI: 10.1007/s12393-021-09290-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.
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Affiliation(s)
- Nidhi Rajesh Mavani
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Jarinah Mohd Ali
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Suhaili Othman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, 43400 Selangor, Malaysia
| | - M. A. Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Haslaniza Hashim
- Department of Food Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Norliza Abd Rahman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
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Ye Z, Yang W, Yang Y, Ouyang D. Interpretable machine learning methods for in vitro pharmaceutical formulation development. FOOD FRONTIERS 2021. [DOI: 10.1002/fft2.78] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
| | - Wenmian Yang
- State Key Laboratory of Internet of Things for Smart City University of Macau Macau China
| | - Yilong Yang
- School of Software Beihang University Beijing China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
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