Saad Marwa, Bujok Sonia, Kruczała Krzysztof
Faculty of Chemistry, Jagiellonian University in Krakow, Gronostajowa 2, 30 - 387 Kraków, Poland.
Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, Niezapominajek 8, 30-239 Kraków, Poland.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Dec 5;322:124769. doi: 10.1016/j.saa.2024.124769. Epub 2024 Jul 2.
Vibrational spectroscopic techniques, such as Raman spectroscopy, as a non-destructive method combined with machine learning (ML), were successfully tested as a quick method of plasticizer identification in poly(vinyl chloride) - PVC objects in heritage collection. ML algorithms such as Convolutional Neural Network (CNN), Random Forest (RF), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA) were applied to the classification and identification of the most common plasticizers used in the case of PVC. The CNN model was able to successfully classify the five plasticizers under study from their Raman spectra with a high accuracy of (98%), whereas the highest accuracy (100%) was observed with the RF algorithm. The finding opens doors for the development of robust and economical tools for conservators and museum professionals for fast identification of materials in heritage collections.
振动光谱技术，如拉曼光谱，作为一种与机器学习（ML）相结合的无损方法，已成功作为一种快速识别遗产收藏中聚氯乙烯（PVC）物品中增塑剂的方法进行了测试。卷积神经网络（CNN）、随机森林（RF）、支持向量机（SVM）和线性判别分析（LDA）等机器学习算法被应用于PVC中最常用增塑剂的分类和识别。CNN模型能够从拉曼光谱中成功地对所研究的五种增塑剂进行分类，准确率高达98%，而RF算法的准确率最高，为100%。这一发现为保护人员和博物馆专业人员开发强大且经济的工具以快速识别遗产收藏中的材料打开了大门。
