1D-CNN_regression

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 anR2of 0.947 and0.627 scores respectively for UP and UPM.