End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network

Abstract

During the near‐infrared spectroscopy analysis process, modeling the quantitative relationship between the collected spectral information and target components is an important procedure. Before using the traditional modeling methods, it is often necessary to select the most featured wavelengths and eliminate those uninformative wavelengths. However, the wavelength selection algorithms can not only increase the model complexity but also may contain some adjustable parameters, which need the users to have more expertise knowledge and experiences. To solve this problem, this paper proposed a novel end‐to‐end quantitative analysis modeling method for near‐infrared spectroscopy based on convolutional neural network (CNN), which directly takes the whole range of collected raw spectral information as input without wavelength selection. The public corn NIR dataset was taken as example to validate the efficiency of proposed method. The experimental results showed that, firstly, if all the whole range of raw spectral information was taken as the input of modeling, the generalized performance of CNN outperforms the traditional methods, and the difference is statistically significant; secondly, if the traditional methods were combined with wavelength selection algorithms, their generalized performances were similar to CNN model; there is no statistical difference. The results indicated that applying the deep learning methods (take CNN as representative) to establish the quantitative analysis model of near‐infrared spectroscopy is easy to use and has more potential popularize values.