zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1107Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESДифференциация пластмасс сочетанием методов спектроскопии комбинационного рассеяния света и машинного обученияDifferentiation of Plastics by Combining Raman Spectroscopy and Machine LearningYangY.YangY.

Ланьчжоу, Ганьсу

Lanzhou, Gansu

ZhangW.ZhangW.

Ланьчжоу, Ганьсу

Lanzhou, Gansu

WangZh.WangZh.

Ланьчжоу, Ганьсу

Lanzhou, Gansu

LiaqatY.LiY.

Ланьчжоу, Ганьсу

Lanzhou, Gansu

liyuee@lzu.edu.cnШкола информатики и инженерии Ланьчжоуского университетаSchool of Information Science and Engineering at Lanzhou University202228072022894596596Copyright © Yang Y., Zhang W., Wang Z., Liaqat Y., 20222022Yang Y., Zhang W., Wang Z., Liaqat Y.Yang Y., Zhang W., Wang Z., Li Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/1107

Классификация образцов пластмасс выполнена на основе объединения КР-спектроскопии с машинным обучением. Конфокальная КР-система с длиной волны возбуждения 532 нм использована для сбора КР-спектральных данных образцов, анализ главных компонент применен для выделения их признаков. Проведено сравнение прогностических моделей классификации пластмасс на основе трех алгоритмов машинного обучения. Показана удовлетворительная классификация 11 видов пластмасс  с помощью рассматриваемых моделей. 

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. 

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The authors declare that there are no conflicts of interest present.