激光诱导击穿光谱
人工智能
规范化(社会学)
模式识别(心理学)
预处理器
计算机科学
光学(聚焦)
特征提取
材料科学
激光器
光学
物理
人类学
社会学
作者
Jiyu Peng,Longfei Ye,Weiyue Xie,Yifan Liu,Lin Mu,Wenwen Kong,Zhijia Zhao,Fei Liu,Jing Huang,Fei Zhou
出处
期刊:Optics Letters
[The Optical Society]
日期:2023-06-26
卷期号:48 (13): 3567-3567
被引量:1
摘要
In this Letter, a rapid origin classification device and method for Baishao ( Radix Paeoniae Alba ) slices based on auto-focus laser-induced breakdown spectroscopy (LIBS) is proposed. The enhancement of spectral signal intensity and stability through auto-focus was investigated, as were different preprocessing methods, with area normalization (AN) achieving the best results—increasing by 7.74%—but unable to replace the improved spectral signal quality provided by auto-focus. A residual neural network (ResNet) was used as both a classifier and feature extractor, achieving higher classification accuracy than traditional machine learning methods. The effectiveness of auto-focus was elucidated by extracting LIBS features from the last pooling layer output using uniform manifold approximation and projection (UMAP). Our approach demonstrated that auto-focus could efficiently optimize the LIBS signal, providing broad prospects for rapid origin classification of traditional Chinese medicines.
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