Near-infrared spectral expansion method based on active semi-supervised regression.

The utilization of near-infrared (NIR) spectroscopy, in conjunction with chemometric techniques, has been widely used in a variety of sectors, including agricultural production and pharmaceutical production. Nevertheless, the laborious and arduous procedure of gathering samples and evaluating their physicochemical properties leads to relatively limited training set sizes for modeling. This problem severely limits the optimization and practical application of NIR spectrum analysis models. The Safer Active Semi-Supervised Sample Augmentation Learning Model (Safer-AS3A) proposed in this paper tries to address the problem by incorporating active learning (AL) and semi-supervised learning (SSL) techniques. Experiments were conducted on two sets of publicly available NIR spectral datasets, and the Safer-AS3A model was compared to other models with similar characteristics. The experimental results indicate that the Safer-AS3A model proposed in our study outperforms comparable models in terms of accuracy and robustness when dealing with scenarios having a limited number of labeled samples. Furthermore, after the training set was expanded with the Safer-AS3A model, the Partial least squares regression (PLSR), Bayesian ridge regression (BRR), and Support vector regression (SVR) models on the Diesel dataset improved their R 2 on the test set by 5.923 %, 3.018 %, and 7.331 %, respectively, compared to the models using only the labeled sample set. On the other hand, the Ridge regression (RR), BRR, and SVR models on the test set on the Shoot dataset improved the R 2 by 4.169 %, 4.449 %, and 11.597 %, respectively. Overall, the Safer-AS3A model can effectively expand the NIR spectral dataset and considerably improve the performance of the NIR spectral analysis model. Using the AL method, the SSL method, and the co-training method together, a novel and effective method is presented for generating high-quality pseudo-labels. This method opens up new avenues for enhancing the efficiency and precision of NIR spectrum analysis. It also provides novel perspectives on sample diversification and prospective applications in other disciplines. [Display omitted] • A deep active learning method is developed to analyze near-infrared spectroscopy data. • The stability principle and voting mechanism are added to the confidence evaluation of pseudo-labeling. • The co-training method effectively alleviates the problem of insufficient labeled spectral samples. [ABSTRACT FROM AUTHOR]