Feedback Convolutional Network for Intelligent Data Fusion Based on Near-Infrared Collaborative IoT Technology

Abstract

Near-infrared (NIR) data containing spectral response information for detecting target composition are sparsely implied in spectral frequency sequence. Spectral feature information should be extracted using computer-oriented chemometric methods. An Internet of Things (IoT) framework constructed with NIR calibration platform needs some advanced algorithm architectures to realize intelligent analysis. A feedback convolutional neural network (CNN) architecture, including three repeated segments of convolution, pooling, and flattening, is designed in this article for multiple extraction of spectral features from one-dimensional NIR data. An error-feedback iteration mechanism is proposed in the model training process to optimize convolution filters of each segment. Multisegment features are fused successively to ease the sparse information issue. Fusion data are further used to train the calibration models with a parametric-scaling fully connected network to determine the suitable numbers of hidden and output nodes. The adaptive network structure has the advantage of obtaining optimal prediction results from fused feature data. The proposed feedback CNN architecture based on feature information fusion is applied to the NIR rapid quantitative detection of selenium content in paddy rice samples. Experimental results showed that the fusion of multisegment features can enhance the ability of spectral information extraction. The optimal model based on fused feature data performs better than models based on separate feature data of each segment. The feedback convolutional network for information fusion can be applied in the NIR collaborative IoT framework for rapid detection spectroscopy to ensure high-confidence NIR analysis in the artificial intelligence performance of IoT.