期刊
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
卷 172, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.sab.2020.105969
关键词
LIBS; Honey; Machine learning; Classification; Food quality
类别
资金
- Greek national funds through the Public Investments Program (PIP) of General Secretariat for Research and Technology (GSRT), under the Emblematic Action The bee routes [2018S.01300000]
- project HELLAS-CH - Operational Programme Competitiveness, Entrepreneurship and Innovation (NSRF 2014-2020) [MIS 5002735]
- European Union (European Regional Development Fund)
In the present work Laser Induced Breakdown Spectroscopy (LIBS) is employed for the classification of honey samples assisted by different machine learning algorithms. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) and Random Forest Classifiers (RFCs) were used for the treatment of the LIBS spectroscopic data, while the advantages and the suitability of each statistical analysis technique is discussed. It was found that the spectral lines of the main inorganic constituents of honey: Ca, Mg, Na and K are the most important for classification purposes. In all cases, excellent classification results were obtained, attaining remarkable accuracies exceeding 95%. The present results suggest the potential use of the LIBS technique assisted by machine learning algorithms for honey classification based on its floral origin, providing an easy to use and efficient methodology able to perform real time quality control.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据