开发一种替代的In Vitro Rumen发酵预测模型
Xinjie Wang1, Jianzhao Zhou1, Runjie Jiang1
1College of Electric and Information, Northeast Agricultural University, Harbin 150038, China.
Animals : an open access journal from MDPI
|January 23, 2024
概括
机器学习模型准确地模拟了体外乳头发酵过程,预测了甲和酸的产生. 这些模型有助于优化奶牛的饮食,并选减少甲排放的料选择.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 计算生物学 计算生物学
背景情况:
- 体发酵对于反动物的消化和甲生产至关重要.
- 需要精确模拟体外发酵,以优化动物营养和减少对环境的影响.
- 饮食中的营养成分显著地影响了新鲜发酵的参数.
研究的目的:
- 开发和验证机器学习模型,以模拟体外乳头发酵过程.
- 通过使用不同的混合配给量来量化 rumen 甲和酸生产.
- 为了建立一个替代方法来预测乳头发酵参数.
主要方法:
- 饮食营养成分组合 (NDF,ADF,CP,DM) 被用作输入参数.
- 建立了三个预测模型:人工神经网络 (ANN),遗传算法-BP (GA-BP) 和支持矢量机器 (SVM).
- 模型性能使用R平方 (R2) 和根平均平方误差 (RMSE) 进行评估,并经过独立验证.
主要成果:
- 这三种模型都表现出类似的模拟精度,与测量数据保持一致 (R2 ≥0.83).
- 甲模型实现了RMSE ≤ 1.85mL/g,而酸模型实现了RMSE ≤ 2.248 mmol/L.
- 模型在一个独立的验证实验中显示出强大的概括能力.
结论:
- 基于机器学习的体外模型为量化发酵参数提供了宝贵的工具.
- 这些模型可以指导奶牛的饮食优化,并提高养效率.
- 这些模型有助于快速选用于减少甲的料选项.
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