番茄红素提取工艺的响应面法和人工神经网络模型优化
Optimization of Lycopene Extraction Process Using Response Surface Methodology and Artificial Neural Network Model
王玉州 1卢函 2凯迪日耶·玉素普 1赵丽凤3
作者信息
- 1. 喀什大学生命与地理科学学院,新疆 喀什 844000;喀什大学新疆帕米尔高原生物资源与生态重点实验室,新疆 喀什 844000
- 2. 新疆和田学院生地学院,新疆 和田 848000
- 3. 喀什大学化学与环境科学学院,新疆 喀什 844000;喀什大学新疆特色药食用植物资源化学实验室,新疆 喀什 844000
- 折叠
摘要
番茄红素是一种天然类胡萝卜素,有抗氧化、抗癌、降脂和提高免疫的作用,在保健品开发中有着广泛的应用.为提高番茄红素的综合利用提取技术,对不同提取条件下番茄中番茄红素得率的影响进行研究,利用响应面法(RSM)和一种人工神经网络模型(ANN),对番茄红素的超声辅助提取工艺进行了建模和优化.结果表明,番茄红素最佳提取条件为提取温度42.6℃,提取时间56.7 min,超声功率350 W,液料比9 mL/g,番茄红素得率最高为67.73 ug/g.
Abstract
Lycopene is a natural carotenoid with antioxidant,anti-cancer,lipid-lowering,and immune-boosting properties,widely used in the development of health products.To enhance the comprehensive utilization and extraction techniques for lycopene,a study was conducted to investigate the effects of various extraction conditions on lycopene yield from tomatoes.Using response surface methodology(RSM)and an artificial neural network model(ANN),the ultrasonic-assisted extraction process for lycopene was systematically modeled and optimized.The study found that the optimal extraction conditions for lycopene were an extraction temperature of 42.6℃,extraction time of 56.7 minutes,ultrasonic power of 350W,and a liquid-to-material ratio of 9mL/g,with the highest lycopene yield being 67.73µg/g.Compared to RSM,the ANN model demonstrated better fitting capability and optimization effects in the extraction of lycopene.The results of this study provide a basis for the extraction process and influencing factors of lycopene using ultrasound-assisted extraction.
关键词
番茄红素/提取工艺/响应面法/神经网络模型Key words
lycopene/extraction process/response surface methodology/artificial neural network model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据