Flavonoids are natural compounds with diverse structures. This type of nature product is considered to possess a wide range of health beneficial effects. Different skeleton structures and substituent groups lead to different Raman spectral features. In this work, we developed three Raman spectrum analysis methods based on artificial intelligence to classify 18 flavonoids. Firstly, applying principal component analysis (PCA) as dimension reduction method, we compress the 1300cm-1 -1600cm-1 spectral band into several important variables. The results obtained by the preprocessing methods were combined with K-Nearest Neighbor algorithm (KNN), support vector machine (SVM) for classification. Secondly, the combination of relevant features was taken by advanced machine learning method of random forest (RF). In terms of the accuracy of the results, all the methods achieved acceptable classification accuracy, which was almost over 84% on the test set. The experimental results demonstrated that the Raman spectroscopy study based on corresponding unique vibration mode exhibited application prospects in chemical structure classification and pharmacological activity prediction.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.