响应面优化亚麻荠饼粕多酚提取工艺及其抑菌和抗氧化活性研究

JING Bingnian,WEI Lei,WANG Wei,YU Liqin,ZHU Jie,XIE Xiaoyang,LIU Yuqing,FAN Yi

China Food Additives（2022）

Cited 1|Views19

Abstract

以多酚得率为评价指标,单因素试验结合响应曲面分析法优化亚麻荠饼粕多酚提取工艺参数,并分别采用倍比稀释法、ABTS自由基清除能力测定法及总抗氧化性能测定法评价其体外抑菌和抗氧化活性.结果表明:在乙醇浓度45％、料液比1:45g/mL、超声温度68℃、超声时间40min的最佳提取条件下,亚麻荠饼粕多酚的得率达3.78％±0.02％,与理论预测值比较接近.亚麻荠饼粕多酚提取物对大肠杆菌、化脓性链球菌和鼠伤寒沙门氏菌具有强烈的抑制作用,MIC和MBC均在6.25mg/mL以下;亚麻荠饼粕多酚提取物具有很强的ABTS自由基清除能力及还原Fe3+的能力.优化后的亚麻荠饼粕多酚超声辅助提取工艺稳定可行,且其提取物具有较强的抑菌活性和抗氧化能力.

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper