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使用太赫兹时域光谱学 (THz-TDS) 对小麦质质量进行区分

Shuyan Peng¹, Shengkun Wei², Guoyong Zhang³

¹College of Medical Information, Chongqing Medical University, Chongqing 400016, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|November 23, 2024

概括

这项研究使用太赫兹时域光谱 (THz-TDS) 和机器学习来准确识别小麦质强度. 算法优化支向量机 (SSA-SVM) 模型在区分高,中,低质小麦类型方面实现了100%的准确性.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

小麦质量对于确定小麦的最终用途和市场价值至关重要.
食品行业需要准确和非破坏性的方法来评估质强度.
太赫兹时域光谱 (THz-TDS) 提供了材料表征的潜力.

研究的目的:

开发和验证一种使用THz-TDS识别小麦质强度的非破坏性方法.
为了比较不同化学测量和机器学习模型对小麦质歧视的性能.
建立一种可靠的方法来区分具有不同质含量的小麦.

主要方法:

使用太赫兹时域光谱 (THz-TDS) 来从小麦样本中收集光谱数据.
从时间域数据计算了光学参数,包括折射率和吸收系数光谱.
使用竞争适应重权取样 (CARS) 选择了特征频率,并使用支持矢量机器 (SVM),反向传播神经网络 (BPNN),改进的卷积神经网络 (改进的CNN) 和算法优化支持矢量机器 (SSA-SVM) 进行了模型开发.

主要成果:

在具有不同质含量的小麦样本之间观察到折射率和吸收系数光谱的显著差异.
折射率光谱显示出更明显的差异.
该SSA-SVM模型实现了最高的歧视性能,准确率为100%.

结论:

THz-TDS与化学测量和机器学习技术相结合,提供了一种高效和准确的方法,用于非破坏性地区分小麦质强度.
在这种应用中,SSA-SVM模型表现出了卓越的性能.
这种方法为工业小麦的质量评估和生产过程提供了宝贵的理论和实践基础.

关键词:

化学测量方法化学测量方法机器学习是机器学习.频谱学是一种光谱学.太赫兹时间域光谱学 (THz-TDS)小麦小麦小麦小麦的小麦.

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