首页|不同产地葛根药材的高光谱结合人工神经网络鉴别

不同产地葛根药材的高光谱结合人工神经网络鉴别

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
采用高光谱结合人工神经网络(ANN)方法建立了不同产地葛根药材的鉴别方法.采集6种不同产地葛根药材的高光谱数据,使用Savitzky-Golay平滑滤波对原始光谱数据预处理,结合人工神经网络方法建立葛根产地鉴别模型.结果表明,与未经预处理的光谱数据模型准确率相比,Savitzky-Golay平滑滤波后建立的ANN模型识别测试集分类准确率达到99.00％.因此,高光谱技术结合人工神经网络能够实现快速、准确地鉴别葛根产地,是一种很有前景的葛根药材鉴别方法.
Hyperspectral Combined with Artificial Neural Network to Identify Pueraria Lobata Medicinal Materials from Different Origins
The hyperspectral combined with artificial neural network(ANN)method was used to establish the identification method of Pueraria lobata from different origins.The hyperspectral data of six kinds of Pueraria lobata medicinal materials from different origins were collected,and the original spectral data were preprocessed by Savitzky-Golay smooth filtering,and the origin identification model of Pueraria lobata was es-tablished by artificial neural network method.The results show that compared with the accuracy of the unpreprocessed spectral data model,the classification accuracy of the ANN model established after Savitzky-Golay smoothing filtering reaches 99.00％.The results showed that hyper-spectral technology combined with artificial neural network could quickly and accurately identify the origin of Pueraria lobata,which was a promising method for identifying Pueraria lobata.

PuerariaHyperspectralArtificial Neural NetworksOrigin Identification

郭毅秦、焦龙、娄俊豪、沈瑞华、钟汉斌、熊迅宇

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西安石油大学 化学化工学院,陕西 西安 710065

葛根 高光谱 人工神经网络 产地鉴别

国家自然科学基金项目陕西省教育厅青年创新团队建设科研计划项目陕西省教育厅青年创新团队建设科研计划项目大学生创新创业训练计划项目川庆钻探公司-西安石油大学致密油气藏勘探开发研究中心科技项目西安石油大学科研创新团队

21172300321JP09722JP064202210700010CQXA-2023-052019QNKYCXTD17

2024

10.3969/j.issn.1004-275X.2024.04.21

云南化工
云南省化工研究院 云天化集团有限责任公司 云南煤化工集团有限公司 云南省化学化工学会

云南化工

影响因子：0.272
ISSN：1004-275X
年,卷(期)：2024.51(4)
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