基于二维关联光谱 (2D-COS) 结合残余神经网络 (ResNet) 的 Gentian 相关物种的识别
Xunxun Wu1, Xintong Yang1, Zhiyun Cheng1
1School of Biomedical Sciences, Huaqiao University, Quanzhou 362021, China.
Molecules (Basel, Switzerland)
|July 14, 2023
概括
准确识别属物种对于临床使用至关重要. 这项研究引入了一种新的方法,将二维相关谱 (2D-COS) 与残余神经网络 (ResNet) 结合起来,用于精确的草药识别,实现100%的准确性.
科学领域:
- 草药是一种草药.
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 种在传统中医中广泛使用,因为它有各种健康益处.
- 精确的物种鉴定是关键的,因为不同种之间的形态相似性.
- 传统的识别方法可能耗时且容易出现错误.
研究的目的:
- 开发和验证一种新,准确和高效的识别种的方法.
- 为了利用先进的光谱和机器学习技术来验证草本真实性.
- 为了提高基于的传统医学的可靠性.
主要方法:
- 红外 (IR) 光谱学被用来收集来自7个物种的173个样本的光谱数据.
- 一维光谱数据被转化为二维相关性光谱 (2D-COS) 图像 (同步,异步,整合).
- 使用残余神经网络 (ResNet) 模型来分析2D-COS图像,并进行特征带提取以进行优化.
主要成果:
- 与异步和整合图像相比,同步的2D-COS图像对ResNet模型具有更好的适应性.
- 用单个特征带 (17501100 cm−1) 优化ResNet模型导致了最佳的融合.
- 优化的模型在训练,测试和外部验证方面实现了完美的精度 (1.0),最小损失值为0.155.5.
结论:
- 2D-COS和ResNet的结合方法提供了一个非常有效和准确的解决方案来识别种.
- 这种方法显著提高了传统医学中草药认证的可靠性和效率.
- 该研究强调了先进的光谱和人工智能技术在草药产品质量控制方面的潜力.
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