持续性
蛋白质纯化
生化工程
人口
过程(计算)
萃取(化学)
工艺工程
农业工程
环境科学
计算机科学
工程类
生物
化学
生态学
生物化学
人口学
色谱法
社会学
操作系统
作者
Chinwendu Rachel Eze,Ebenezer Miezah Kwofie,Peter Adewale,Edmond Lam,Michael Ngadi
标识
DOI:10.1016/j.ifset.2022.103199
摘要
Globally, the demand for protein continues to exceed its supply due to the geometric increase in population. For sustainability reasons, the high demand is leading to a shift in interest from animal-based protein, towards consuming high plant protein, sourced mainly from legumes and pulses. Traditional methods for protein extraction have been deployed to ensure availability and convenient utilization of these novel protein sources. Dry and wet fractionation are the two main categories of traditional protein extraction methods, with dry fractionation being more sustainable and frequently used. However, the repercussions trailing these methods keep encouraging research towards greener extraction pathways to ensure more protein availability and sustainability. The key drivers of this transition are increased yield, quality, uniformity and process eco-friendliness. Microwave, ultrasonic energy, pulse electric field, hydrodynamic cavitation extraction and tribo-electrostatic separation, among other novel technologies, appear to be the most likely techniques to dominate in the next few decades. This prospection hinges on available data of their performance efficiency in pulse / legume protein extraction and observed relatively higher yield potentials, protein quality, demand for less water, solvents and energy. The latter inarguably favors the food-energy-water nexus and could contribute considerably to environmental sustainability. Nonetheless, available knowledge cannot suffice effective and sustainable deployments of these techniques in legume protein extraction. Therefore, the gap on process performance and possible optimization of process parameters of these novel technologies deserves urgent attention. The product-process efficiency is highly critical for better understanding of the chances for process scale-up. This has been identified as the present stage of research in this domain.
科研通智能强力驱动
Strongly Powered by AbleSci AI