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虫优化算法和基于机器学习的乳球菌基因组分析:预测发酵牛奶的电子感觉特性
Jinhui Dai1,2, Weicheng Li3,4,5,6, Gaifang Dong1,2
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, China.
Foods (Basel, Switzerland)
|July 13, 2024
概括
这项研究预测发酵牛奶的发酵.
科学领域:
- 食品科学与技术 食品科学与技术
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
背景情况:
- 发酵乳制品在全球食品行业中至关重要,而乳球菌 (Lactococcus lactis) 在风味的发展中发挥着关键作用.
- 目前评估发酵剂的方法是缓慢的,昂贵的和主观的,阻碍了优化理想的风味配置文件.
- 电子鼻舌技术提供客观评估,但通常仅限于实验室环境.
研究的目的:
- 开发一种用于发酵牛奶电子感官特征的预测模型.
- 利用Lactococcus lactis的基因组数据与机器学习相结合,用于风味预测.
- 加强发酵剂的选和选择,以提高乳制品质量.
主要方法:
- 作为预测模型的基础,使用了Lactococcus lactis的基因组数据.
- 虫优化器 (DBO) 算法与十种机器学习方法一起使用.
- 使用多轮特征选择和回归来优化模型性能.
主要成果:
- 与多轮特征选择和回归相结合的DBO算法显著改善了预测模型的性能.
- 该模型在10倍交叉验证中在所有电子感官表型中实现了超过0.895的R平方值.
- 在电子感官表型和共同选择的特征数量之间观察到正相关性,表明有效识别关键的生物驱动因素.
结论:
- 为预测发酵牛奶的电子感官特征而建立了一种新,高效和具有成本效益的方法.
- 该模型有助于选和优化Lactococcus lactis菌株的理想风味属性.
- 这种方法支持乳制品行业的创新,提高产品质量和市场竞争力.
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