20.500.12556/RUL-147235 Near-infrared spectroscopy and machine learning for accurate dating of historical books Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic data combined with three supervised machine learning methods to predict the publication year of paper books dated between 1851 and 2000. These methods provide different accuracies; however, we demonstrate that the underlying processes refer to common spectral features. Regardless of the machine learning method used, the most informative wavelength ranges can be associated with C−H and O−H stretching first overtone, typical of the cellulose structure, and N−H stretching first overtone from amide/protein structures. We find that the expected influence of degradation on the accuracy of prediction is not meaningful. The variance-bias decomposition of the reducible error reveals some differences among the three machine learning methods. Our results show that two out of the three methods allow predictions of publication dates in the period 1851−2000 from NIR spectroscopic data with an unprecedented accuracy of up to 2 years, better than any other non-destructive method applied to a real heritage collection. degradation environmental modeling infrared light infrared spectroscopy materials analizna kemija strojno učenje spektroskopija infrardeča spektroskopija datiranje kulturna dediščina knjige true false true Angleški jezik Slovenski jezik Članek v reviji 2023-06-27 13:49:42 2023-06-27 13:49:44 2023-06-30 08:35:19 0000-00-00 00:00:00 2023 0 0 Str. 12305–12314 iss. 22 Vol. 145 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 543.422.3-74:004:85:691.1 1520-5126 10.1021/jacs.3c02835 153177603 jacs.3c02835.pdf jacs.3c02835.pdf 1 0DA6F15D4CD45926A0991A4D700D05B1 9266338a52741f26ef00cbae55399d2bf4caef8f91c0543fcdb7babc2222edca 97842afd-14e0-11ee-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=170734 https://pubs.acs.org/doi/10.1021/jacs.3c02835 0 5155be99-14e0-11ee-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=170733 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0