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Differentiation of Plastics by Combining Raman Spectroscopy and Machine Learning
Abstract
We combined Raman spectroscopy with machine learning for the classification of 11 plastic samples. A confocal Raman system with an excitation wavelength of 532 nm was used to collect the Raman spectral data of plastic samples and principal component analysis was used for feature extraction. The prediction models of plastic classification based on three machine learning algorithms are compared. The results show that all three machine learning algorithms are able to classify 11 plastics well. This indicates that the combination of Raman spectroscopy and machine learning has great potential in the rapid and nondestructive classification of plastics.
Keywords
Raman spectroscopy,
plastic classification,
principal component analysis,
random forest,
support vector machine,
neural networks,
machine learning
Supporting agencies: This work was supported by the National Natural Science Foundation of China (grant No. 61405083) and the Fundamental Research Funds for the Central Universities (grant No. 2018-129).
About the Authors
Y. Yang
School of Information Science and Engineering at Lanzhou University
China
China
Lanzhou, Gansu
W. Zhang
School of Information Science and Engineering at Lanzhou University
China
China
Lanzhou, Gansu
Zh. Wang
School of Information Science and Engineering at Lanzhou University
China
China
Lanzhou, Gansu
Y. Li
School of Information Science and Engineering at Lanzhou University
China
China
Lanzhou, Gansu
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Review
For citations:
Yang Y., Zhang W., Wang Zh., Li Y. Differentiation of Plastics by Combining Raman Spectroscopy and Machine Learning. Zhurnal Prikladnoii Spektroskopii. 2022;89(4):596.
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