Simple dilated convolutional neural network for quantitative modeling based on near infrared spectroscopy techniques

Abstract

A simple deep convolutional neural network architecture had been developed and applied to the establishment of a quantitative model based on near infrared spectroscopy techniques. The network architecture only contained general convolutional layers and dilated convolutional layers. The determination coefficient was used as a criterion to stop the training process of the neural network. Three data sets were used to test the performance of the neural network. The major results are (1) quantitative model can be established within one million iterations; (2) the quantitative model established on one spectrometer can be applied to other spectrometers of same manufacturer and even the same kind of spectrometer of different manufacturer; (3) simultaneous quantitative analysis of four components in grain samples. This study provided a new strategy to apply techniques of deep neural network in quantitative analysis.