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.