在超临界流体中提取的优化,通过响应表面方法和人工神经网络与遗传算法相结合,获得Pouteria lucuma种子油
Alex Chauca-Cerrutti1, Marianela Inga1, José Luis Pasquel-Reátegui2
1Facultad de Industrias Alimentarias, Universidad Nacional Agraria La Molina, Lima, Peru.
Frontiers in chemistry
|December 25, 2024
概括
使用二氧化碳超临界流体 (SCF) 提取Lucuma种子油 (LSO) 是一种高效和可持续的方法. 这种绿色替代品产生的高品质油含有有益化合物,性能优于传统的索克斯莱特提取.
科学领域:
- 食品科学与技术 食品科学与技术
- 绿色化学 绿色化学
- 生物技术是生物技术.
背景情况:
- 卢库马 (Pouteria lucuma) 的加工产生了富含生物活性化合物的废物.
- 卢库马种子 (LS) 含有有价值的植物和豆腐,它们是强大的抗氧化剂.
- 目前的提取方法可能存在环境缺陷.
研究的目的:
- 使用超临界流体 (SCF) 技术提取卢库马种子油 (LSO).
- 优化SCF提取参数,以获得高产量和高质量.
- 评估LSO质量,并将SCF与传统方法进行比较.
主要方法:
- 卢库马种子的特征 (LS).
- 使用Box-Behnken设计,响应表面方法 (RSM) 和带有遗传算法的人工神经网络 (ANN+GA) 的SCF提取参数 (温度,压力,CO2流量) 的优化.
- 将SCF提取的LSO与Soxhlet提取的油进行比较.
主要成果:
- 最佳的SCF参数被确定为45°C,300bar和6mL/分钟的CO2流量.
- 在石油总含量中获得了97.35%的提取产量.
- RSM和ANN+GA模型显示出非常好的合适性 (R2值为0.9891和0.9999). ANN+GA显示出优越的可变性解释.
- 与索克斯莱特法相比,SCF提取的LSO表现出优越的酸度,和折射指数.
- 用SCF提取的LSO含有更高的单和多不和脂肪酸.
结论:
- 超临界二氧化碳提取是一种高效且可持续的绿色替代液态氧化物提取方法.
- 通过SCF技术,可以获得高质量的lucuma种子油,具有理想的特性.
- 与传统的提取技术相比,这种方法最大限度地减少了环境影响.
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