将Vis-NIR和NIR高光谱成像技术与数据融合策略相结合,以快速且无损地确定亚麻中的多种营养素质
Dongyu Zhu1, Junying Han1, Chengzhong Liu1
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China.
概括
近红外 (NIR) 和融合的光谱数据有效地决定了亚麻的营养质量,优于可见近红外 (Vis-NIR) 光谱. 这为评估亚麻质量和选高产品种提供了一种新的,非破坏性的方法.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 亚麻的质量取决于蛋白质,油,牛油酸,亚麻酸和亚麻酸含量.
- 需要准确和高效的方法来评估亚麻种的质量和生殖质查.
研究的目的:
- 整合可见近红外 (Vis-NIR) 和近红外 (NIR) 超光谱成像,以确定亚麻的关键营养成分.
- 开发和比较营养质量评估的定量预测模型.
- 为了可视化亚麻中营养成分的空间分布.
主要方法:
- 使用可见近红外 (Vis-NIR) 和近红外 (NIR) 超光谱成像.
- 应用了数据预处理和特征选择方法.
- 开发了包括PLSR,PCR,SVR和MLR在内的定量预测模型.
主要成果:
- 对于所有五个质量指数,NIR和融合光谱数据显示出比Vis-NIR更优异的性能.
- 尼尔光谱仪在油,酸和酸预测方面取得了高精度.
- 聚合光谱数据为蛋白质和酸含量提供了很好的预测.
结论:
- NIR和聚变光谱数据集对于不破坏性,多组分的亚麻的营养评价是有效的.
- 这种方法为未来的亚麻种质量评估和育种计划提供了一个有希望的替代方案.
- 空间分布图提供了对内部营养成分含量变化的洞察.
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