A Monte Carlo resampling based multiple feature-spaces ensemble (MFE) strategy for consistency-enhanced spectral variable selection.

BACKGROUND: Variable selection has gained significant attention as a means to enhance spectroscopic calibration performance. However, existing methods still have certain limitations. Firstly, the selection results are sensitive to the choice of training samples, indicating that the selected variables may not be truly relevant. Secondly, the number of the selected variables is still too large in some situations, and modelling with too many predictors may lead to over-fitting issues. To address these challenges, we propose and implement a novel multiple feature-spaces ensemble (MFE) strategy with the least absolute shrinkage and selection operator (LASSO) method. RESULTS: The MFE strategy synergizes the advantages of LASSO regression and ensemble strategy, thereby facilitating a more robust identification of key variables. We demonstrated the efficacy of our approach through extensive experimentation on publicly available datasets. The results not only demonstrate enhanced consistency in variable selection but also manifest improved prediction performance compared to benchmark methods. SIGNIFICANT: The MFE strategy provided a comprehensive framework for conducting variable importance analysis, leading to robust and consistent variable selection. Furthermore, the improved consistency in variable selection contributes to enhanced prediction performance for spectroscopic calibration, making it more robust and accurate.

Fast Burst-Sparsity Learning-Based Baseline Correction (FBSL-BC) Algorithm for Signals of Analytical Instruments.

Baseline correction is a critical step for eliminating the interference of baseline drift in spectroscopic analysis. The recently proposed sparse Bayesian learning (SBL)-based method can significantly improve the baseline correction performance. However, it has at least two disadvantages: (i) it works poorly for large-scale datasets and (ii) it completely ignores the burst-sparsity structure of the sparse representation of the pure spectrum. In this paper, we present a new fast burst-sparsity learning method for baseline correction to overcome these shortcomings. The first novelty of the proposed method is to jointly adopt a down-sampling strategy and construct a multiple measurements block-sparse recovery problem with the down-sampling sequences. The down-sampling strategy can significantly reduce the dimension of the spectrum; while jointly exploiting the block sparsity among the down-sampling sequences avoids losing the information contained in the original spectrum. The second novelty of the proposed method is introducing the pattern-coupled prior into the SBL framework to characterize the inherent burst-sparsity in the sparse representation of spectrum. As illustrated in the paper, burst-sparsity commonly occurs in peak zones with more denser nonzero coefficients. Properly utilizing such burst-sparsity can further enhance the baseline correction performance. Results on both simulated and real datasets (such as FT-IR, Raman spectrum, and chromatography) verify the substantial improvement, in terms of estimation accuracy and computational complexity.

Sparse Bayesian learning for DOA estimation with mutual coupling.

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

On the SVMpath singularity.

This paper proposes a novel ridge-adding-based approach for handling singularities that are frequently encountered in the powerful SVMpath algorithm. Unlike the existing method that performs linear programming as an additional step to track the optimality condition path in a multidimensional feasible space, our new approach provides a simpler and computationally more efficient implementation, which needs no extra time-consuming procedures other than introducing a random ridge term to each data point. Contrary to the existing ridge-adding method, which fails to avoid singularities as the ridge terms tend to zero, our novel approach, for any small random ridge terms, guarantees the existence of the inverse matrix by ensuring that only one index is added into or removed from the active set. The performance of the proposed algorithm, in terms of both computational complexity and the ability of singularity avoidance, is manifested by rigorous mathematical analyses as well as experimental results.

[Kinetics on ethanethiol oxidation by potassium permanganate in drinking water].

Reaction kinetics of ethanethiol oxidation with potassium permanganate in water was studied, and the effect of ethanethiol oxidation in raw water under coagulation condition has been investigated. The results showed that the oxidation reaction of ethanethiol by potassium permanganate was very efficient, the removing effect could be more than 90%. The rate of ethanethiol decomposition by potassium permanganate in aqueous solution followed second-order kinetics. When potassium permanganate was excessive, pseudo-first-order rate and concentration of potassium permanganate followed the equation: k' = 0.025 [KMnO4] - 0.008. And then, the second-order reaction rate constant was k = 0.025 L/(min x mg). The removal of ethanethiol in raw water by potassium permanganate under coagulation condition was poorer than in pure water condition, So predicted concentration of potassium permanganate was lower than real concentration.

[Removal technology of typical odorant in drinking water].

To eliminate the odor in drinking water of one City in China, a study was performed on the typical odorant removal technology and removal processes. Its results showed that as typical odorants, ethanethiol was effectively removed by oxidation and geosmin and 2-MIB were removed by both oxidation and adsorption, but geosmin and 2-MIB's adsorption effect was better than oxidation. When thiol and thioether in raw water was less than 20 microg/L, furthermore, there was not any other odorant, potassium permanganate oxidation should be equipped with enhanced coagulations. When geosmin and 2-MIB in raw was less than 30 ng/L, activated carbon adsorption should be equipped with enhanced coagulations. When thiol and thioether in raw water was more than 20 microg/L, or geosmin and 2-MIB was more than 30 ng/L, ozone-activated carbon process should be added after the conventional water treatment process. When thiol and thioether in raw water was more than 150 microg/L, or geosmin and 2-MIB was more than 100 ng/L, preoxidation or adsorption of powder activated carbon at intake should be combined with ozone-activated carbon according to the odorant composing.