  We consider determining the optimal stopping time for the glue curing of wood
panels in an automatic process environment. Using the near-infrared
spectroscopy technology to monitor the manufacturing process ensures
substantial savings in energy and time. We collect a time series of curves from
a near-infrared spectrum probe consisting of 72 spectra and aim to detect an
optimal stopping time. We propose an estimation procedure to determine the
optimal stopping time of wood panel compression and the estimation uncertainty
associated with the estimated stopping time. Our method first divides the
entire data set into a training sample and a testing sample, then iteratively
computes integrated squared forecast errors based on the testing sample. We
then apply a structural break detection method with one breakpoint to determine
an estimated optimal stopping time from a univariate time series of the
integrated squared forecast errors. We also investigate the finite-sample
performance of the proposed method via a series of simulation studies.
