Monday February 1st, 2021 – 14:30
Dagmawi Delelegn Tegegn
https://meet.google.com/fxa-xpxw-aew
Abstract:
Advances in Near-infrared (NIR) spectroscopy technology led to an
increase of interest in its applications in various industries due to its
powerful non-destructive quantization tool. In this work, we used a
one-dimensional CNN to determine simultaneously quantities of organic materials
in a mixture using their NIR infrared spectra. The coefficient of determination
(R2) and the root mean square error (RMSE) is used to test the performance of
the model. We used six materials to make pairwise combinations with distinct
quantities of each pair. We obtained 13 different pairwise mixtures, afterward,
their near-infrared spectrum profiles is extracted. The model predicted for
each mixture their percentage of composition with a result of 0.9955 R2 and
RMSE 0.0199. Furthermore, we examined the performance of our model when
predicting unseen composition percentages with unseen mixtures. To do so, two
scenarios are carried out by filtering the training and testing set: the first
one where we test on unseen composition percentage (UP) of mixtures, and the
second one where we test on unseen composition percentage of unseen mixtures
(UPM). The model achieved an R2 of 0.947 and 0.627 scores respectively for UP
and UPM.