基于拉曼光谱识别技术的不同肥料条件下,大米粉在盐和区域的变化的表征
Zhipeng Li1, Zhuang Miao1, Changming Li1
1Key Laboratory of Spectral Detection Science and Technology, School of Physics, Changchun University of Science and Technology, Changchun, 130000, China.
Scientific reports
|March 19, 2025
概括
拉曼光谱和机器学习可以在不同的施肥下准确地识别米粉的质量. 使用MSC预处理的支持矢量机 (SVM) 实现了近100%的分类准确性,使得质量评估快速.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 米的营养质量受到粉含量的影响.
- 盐的土壤条件和各种各样的施肥做法会影响米粉水平.
- 准确评估米粉含量对于质量评估至关重要.
研究的目的:
- 根据使用光谱数据的受精处理方法对大米进行分类.
- 评估不同机器学习模型在米质分析中的有效性.
- 为了确定施肥技术与米粉质量之间的相关性.
主要方法:
- 拉曼光谱法被用来收集大米的光谱数据.
- 应用了数据预处理技术,包括规范化,多重散射校正 (MSC),标准正常变量和Savitzky-Golay过.
- 使用了三种机器学习模型:支持向量机 (SVM),前神经网络和k-最近邻居分类.
主要成果:
- 多重散射校正 (MSC) 显著提高了所有模型的分类准确性,接近100%.
- 支持矢量机器 (SVM) 模型与前神经网络和k-最近邻相比表现出更高的性能.
- 使用MSC预处理的SVM模型在预测粉含量方面取得了很高的准确性 (R2=0.93,RMSE=0.04%,MAE=0.20%).
结论:
- 拉曼光谱与机器学习相结合,提供了一种强大的方法来识别大米质量.
- 在提高分类的光谱数据可靠性方面,MSC预处理非常有效.
- 这项研究为基于受精影响的快速,准确的水质量评估提供了基础.
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