Abstract
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.
Highlights
Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular
In 2012, Shuifang Li et al used Raman spectroscopy combined with partial least squares linear discriminant analysis (PLS-LDA) to detect the feasibility of beet syrup in honey
The aim of this study is to propose a Raman spectroscopy combined with machine learning to identify honey adulteration
Summary
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 lowconcentration adulterated Suichang native honey. Suichang native honey, a special honey from Lishui County, Zhejiang Province, China, is famous for its amber color and unique taste It has been shortlisted in China’s National Geographical Indication Agricultural Products Protection Project List in 2021. Raman spectroscopy is a common method in the food measurement field[6,7] It has a wide range of applications in the detection and classification of honey adulteration. In 2012, Shuifang Li et al used Raman spectroscopy combined with partial least squares linear discriminant analysis (PLS-LDA) to detect the feasibility of beet syrup in honey. PLS-LDA to detect adulteration of fructose, glucose, sucrose, maltose, hydrolyzed inulin syrup in honey They observed a total accuracy of 96.54%5. PLS is widely used in spectral analysis[9–11]
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