Evaluation of 1D convolutional neural network in estimation of mango dry matter content

This study empirically validates prior claims regarding the superior performance of a Convolutional Neural Network (CNN) model for estimating mango Dry Matter Content (DMC) using Near Infrared (NIR) spectroscopy. The Partial Least Squares (PLS), Artificial Neural Network (ANN), and CNN models employed in the previous publications were compared on an equal footing, i.e., employing the same training and test data, with consideration of the effect of other practices employed in those studies, i.e., outlier removal, training set partitioning, sample ordering, and spectral pretreatment and augmentation. A new benchmark RMSEP of 0.77 %FW was achieved, being statistically significant (P<0.05) different than the previously published best RMSEP for the same independent test set. This CNN model was also shown to be more robust when tested on a new season of fruit than optimised ANN and PLS models, with RMSEPs of 1.18, 2.62, and 1.87, and bias of 0.16, 2.36 and 1.56 %FW, respectively. The combination of model type and data augmentation was important, with the CNN model only slightly outperforming the ANN model when using only a second derivative pretreatment. This requirement highlights the need for chemometric input to model development. The quantification of the sensitivity of neural network model training to use of differing seeds for pseudo-random sequence generation is also recommended. The standard deviation in RMSEP of 50 ANN and CNN models trained with differing random seeds was 0.03 and 0.02 %FW, respectively.