| 5 | Bioactive peptides in fermented foods and their application:a critical review显示文摘Bioactive peptides are released during the production of fermented dairy,vegetables,fruits,legumes,fish,and meat prod-ucts.The proteolytic specificity of lactic acid bacteria,Bacillus spp.,yeasts,and mold,apart from their ability to synthesize bioactive peptides,plays an important role in the generation of specific bioactive peptides in traditional fermented foods.Controlled fermentation using defined starter strains has been explored for the development of bioactive peptides enriched novel fermented foods with potential functionality.Bioactive peptides enriched foods exert diverse health benefits,such as antioxidant,antihypertensive,antidiabetic,and immunomodulatory effects.Bioactive peptides can be used as alternatives to synthetic compounds due to negligible side effects and high valuation in the nutraceutical and functional food market.However,challenges associated with the identification,quantification,organoleptic properties,and bioavailability of bioac-tive peptides need to be addressed before exploiting the potential of bioactive peptides in the functional food industry.In the present review,we have discussed the production of bioactive peptides in diverse fermented foods.Structural and sequence specificity of peptides and their effect on the expression of distinct health beneficial effects have been described.The potential of utilizing these bioactive peptides for the development of novel functional fermented foods is discussed.Recent advances in peptide identification,quantification,debittering of peptides,and increasing peptide bioavailability have been explained. | Rounak Chourasia Loreni Chiring Phukon Md Minhajul Abedin Srichandan Padhi Sudhir P.Singh Amit Kumar Rai | 2023 | Systems Microbiology and Biomanufacturing2023，3，1： | 0 |
| 6 | Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection显示文摘The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost. | Qasem Al-Tashi Tareq M Shami Said Jadid Abdulkadir Emelia Akashah Patah Akhir Ayed Alwadain Hitham Alhussain Alawi Alqushaibi Helmi MD Rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili | 2023 | Computer Systems Science & Engineering2023，47，11： | 0 |
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