A Novel Semisupervised Regression Method for Online Total Nitrogen Detection Using Double Structural Sparse Feature Extraction

To solve the problem of insufficient labeled samples in online total nitrogen (TN) detection, a novel semisupervised learning (SSL) method called double regularized structure graph learning (DRSGL) was proposed, which can effectively extract useful features and support the TN detection equipment to establish accurate detection models with few labeled samples. Based on temporal and spectral informativity, a high-quality graph structure method was designed first, which utilizes the spectrum-temporal prior knowledge hidden in the spectrum and enhances the efficiency of the graph model. Then, considering the double sparsity of TN spectrum samples, a double structural sparse feature selection method ((DSFS)-F-2) was invented accordingly, which can excavate useful information from both spectral and temporal dimensions. Finally, to address the insufficient problem of labeled samples, an adaptive SSL method was constructed by combining graph learning strategy and (DSFS)-F-2 and applied to the TN rapid detection prototype, which can iteratively select important features and update graph structure of samples correspondingly. According to the experimental results based on practical application, DRSGL can effectively solve the problem brought by the insufficiency of labeled samples, which can utilize only 20% labeled samples to establish an accurate detection model satisfied national detection standard with a relative error lower than 10%.