人工智能
支持向量机
机器学习
算法
计算机科学
模式识别(心理学)
偏最小二乘回归
作者
Shuhan Hu,Hongyi Li,Chen Chen,Deyi Zhao,Bingyu Dong,Xiaoyi Lv,Kai Zhang,Yi Xie
标识
DOI:10.1038/s41598-022-07222-3
摘要
Zhejiang Suichang native honey, which is included in the list of China's National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky-Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.
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