揭开塑造甘生物和非生物因素的生物和非生物因素 多丰富:通往人工智能驱动作物管理的门户
Ana L S Oliveira1, Maria João Carvalho1, Poliana Silva1
1CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua Diogo Botelho 1327, 4169-005 Porto, Portugal.
Antioxidants (Basel, Switzerland)
|January 22, 2024
概括
优化甘草的聚烯提取涉及到特定的收获条件. 在干旱季节在瓜里巴收获的CU0618品种产生了最高的多含量和抗氧化能力.
科学领域:
- 农业科学 农业科学
- 生物化学 生物化学
- 数据科学数据科学数据科学
背景情况:
- 甘吸管 (Saccharum officinarum) 富含多,对于天然成分来说非常有价值.
- 草中的多变异性受生物应激和植物特征的影响,使提取复杂化.
研究的目的:
- 调查五个因素对甘吸管多多样性的影响.
- 使用响应表面方法 (RSM) 和人工神经网络 (ANN) 来优化多提取条件.
主要方法:
- 研究了生产面积,昆虫侵袭,植物年龄,收获季节和品种的影响.
- 采用RSM和ANN来优化提取和预测理想的收获条件.
- 利用二次多项式模型进行预测.
主要成果:
- 鉴定CU0618品种,在瓜里巴收获 (干季,第7个收获,13.81%的虫入侵),以高基酸,基酸和黄含量为最佳.
- 从确定的最佳条件中获得高抗氧化能力.
- ANN模型表现出比RSM更高的预测精度.
结论:
- 特定的甘吸管收获条件显著影响多产量和成分.
- 人工神经网络显示出预测和增强多提取过程的巨大潜力.
- 人工智能驱动的方法可以推进作物管理,以生产有价值的天然成分.
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