通过基于NIRS的多特征预测建模,对马的营养概况进行分析
Manju Kumari1, Siddhant Ranjan Padhi1, Mamta Arya2
1ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Scientific reports
|May 15, 2025
概括
近红外光谱 (NIRS) 提供了一种快速的方法来评估马 (Macrotyloma uniflorum) 中的营养成分. 这项研究开发了精确的蛋白质,粉和其他关键特征的NIRS模型,使得有效的生殖质查成为可能.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 生物技术是生物技术.
背景情况:
- 马 (Macrotyloma uniflorum) 是一个未被充分利用的,营养丰富的豆类,对发展中国家的人类营养至关重要.
- 传统的方法来评估马体的营养特征是耗时和劳动密集的,阻碍了大规模的生殖质查.
- 需要快速和高效的技术来评估马种子体的营养成分.
研究的目的:
- 开发和验证近红外光谱学 (NIRS) 预测模型,用于马体中关键的营养成分.
- 建立一种快速,高通量选方法,用于马基克细菌体评估.
- 识别特征特异的马体基因质,用于开发健康食品和改善作物生产.
主要方法:
- 采用了139个马克克拉姆接入的多样化集合,用于参考数据生成.
- 开发了蛋白质,粉,TSS,和植物酸的预测模型,使用修改部分最小平方 (MPLS) 回归.
- 应用光谱预处理 (SNV-DT) 和针对衍生品,差距选择和光滑的优化模型,使用RSQ和RPD统计数据进行评估.
主要成果:
- NIRS模型对蛋白质 (RSQ=0.701,RPD=1.85),粉 (RSQ=0.987,RPD=4.03),TSS (RSQ=0.800,RPD=4.06),醇 (RSQ=0.778,RPD=2.15) 和植物酸 (RSQ=0.730,RPD=1.88) 的预测准确度很好.
- 统计分析证实了开发的NIRS模型的可靠性和强度.
- 该研究通过快速的,多特征评估方法,实现了对前育种应用的高预测准确性.
结论:
- NIRS提供了一个快速而精确的替代品,用于马的营养分析的传统方法.
- 开发的NIRS模型适用于大型马基克细菌体和市场样本的高通量选.
- 这种方法可以更容易地识别出优质的马基克细菌质,用于增强食品和农业发展.
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