VGG16 is a CNN architecture that has attracted significant attention for its effectiveness when working with images. Its architecture is composed of 16 layers, including 13 convolutional layers and 3 fully connected layers, using small (3 × 3) filters that enhance feature extraction while keeping spatial hierarchies. Even though in the current work we are not dealing with image-based data, we take advantage of VGG16 spatial hierarchy and by reshaping it to a Conv1D (Table ) we are able to use our LE per particle (primary AM or CBD) dataset.