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Soil analysis makes for developing precision agriculture and monitoring land quality, while the models available for spectroscopy-based chemometrics are constrained by limited samples from small areas. The paper proposed sample expansion and model construction based on spectral difference and content difference, realizing data augmentation and deep learning applied to original samples with limited numbers. The spectral subtraction based on maximum or minimum values exploited the maximum or minimum values to acquire the spectral difference and content difference, which provided a new data form for model construction. Keeping enhanced samples whose spectral difference and content difference were all zero was useful for improving model performance. Augmentation of all data or training data based on maximum or minimum values-based spectral subtraction, which sorted the contents and made them the maximum or minimum values in sequence, achieved sample expansion by the spectral difference and content difference. The model utilized the random vector functional link (RVFL) network, extreme learning machine (ELM), and one-dimensional convolutional neural network (1D CNN), which could predict the content of new samples through ensemble averaging when predicting content difference. The experimental result showed the model of the spectral subtraction based on maximum or minimum values had a similar performance to that of the original samples. Augmentation of all data improved model performance by only RVFL and ELM. Augmentation of training data verified 1D CNN was better than RVFL and ELM. The paper implements a new data augmentation method and applies CNN to original samples with inadequate numbers, which lays the foundation for an improved model and applying spectral preprocessing., Competing Interests: Declaration of competing interest Lushan Wan, Dong Xiao, Zhizhong Mao, Zhenni Li has patent #202311576119.9 pending to Northeastern University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R 2 ) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Heavy metal pollution in saline-alkali land has a significant impact on the ecological environment and human health. Rapid and accurate inversion of cadmium (Cd) element content in the saline-alkali land is important for environmental protection, saline-alkali soil improvement and conversion of saline-alkali land to cultivated land. Using traditional chemical detection methods to detect the content of heavy metal elements requires a long testing time and has the drawback of high prices. In this paper, we select the saline-alkali land of Zhenlai County as the study area and combine visible-NIR spectroscopy with machine learning models to invert the Cd content in the saline-alkali land. We preprocess the original reflection spectra using fractional order derivatives (FOD), then construct six three-band spectral indices (TBIs) and obtain the corresponding optimal band combination parameters by the optimal band combination (OBC) algorithm. To address the shortcomings of two-hidden-layer extreme learning machine (TELM), this paper introduces new weight parameters among the nodes of the first hidden layer, further extends it to multiple layers on this basis, and proposes the MSC-ELM model. The improved model is compared with several models, such as random forest (RF), partial least squares (PLS) and extreme learning machine (ELM). And the model performance is analyzed and compared by introducing several performance indicators, such as root mean square error (RMSE) and the ratio of the performance to interquartile (RPIQ). The experimental results show that the FOD transformation can eliminate the baseline drift and reduce the spectral noise. The constructed TBIs can effectively enhance the correlation with Cd content relative to the original single band, reduce redundant information and enhance the spectral features. The MSC-ELM model achieves better performance metrics compared to the other models and obtains the optimal prediction performance. This study provides an accurate and rapid method for the detection of Cd content in saline soil, which is important for the improvement and ecological recovery of saline soil., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Bacterial cellulose (BC), an important category of polysaccharides, was investigated as a texture improver in bakery products. This study focused on the changes in the conformational and thermal properties of gluten in the wheat dough system as affected by BC. Significant reductions in the free-SH content, fluorescence intensity, and surface hydrophobicity index (H 0 ) were observed as a result of the increased BC addition. The electrophoresis profile (SDS-PAGE) and size exclusion (SE-HPLC) revealed the variation in molecular weight distribution, and the increase in the content of the 40-91 kDa molecular weight was at the expense of a decrease in the amount of the corresponding 10-40 kDa. When 0.1 % BC was added, both the α-helix and β-sheet contents increased as a result of enhanced chemical interactions, thereby contributing to the gluten matrix with higher thermal stability. Further supplementation interfered with the current ordered gluten structure, which could be supported by the lower α-helix/β-sheet content ratio and the decreased degradation temperature (T d ) of gluten with 0.2 % BC. However, the observed decrease in the ratio of β-turns to β-sheets and weight loss at 600 °C indicated that a reconstructed gluten matrix induced by extra BC addition was formed to maintain the structural stability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Mussel-inspired chemistry (polydopamine) offers great opportunities to develop inexpensive and efficient process for many types of materials with complex shapes and functions in a mild and friendly environment. This paper describes a facile, yet green approach to synthesize polydopamine/silver (PDA/Ag) nanocomposite particles with a combination use of polydopamine chemistry and electroless metallization of Ag. In this approach, monodisperse spherical polydopamine particles are first synthesized by the oxidation and self-polymerization of dopamine (monomer) in an alkaline water-ethanol solution at room temperature, which are served as the active templates for secondary reactions due to the abundant catechol and amine groups on the surface. Subsequently, the silver precursor-[Ag(NH3)2](+) ions introduced are easily absorbed onto the surface of the PDA particles, and are immediately in situ reduced to metallic Ag nanoparticles with the help of these active catechol and amine groups. During the preparation, no additional reductants, toxic reagents and intricate instruments are needed. These as-synthesized PDA/Ag nanocomposite particles are ideal candidates for antibacterial application because they do not show significant cytotoxicity against HEK293T human embryonic kidney cells in the in vitro cytotoxicity assay, whereas demonstrate enhanced antibacterial abilities against Escherichia coli (Gram-negative bacteria) and Staphylococcus aureus (Gram-positive bacteria) in the antibacterial assays. Owing to their excellent cytocompatibilities and antibacterial activities, these PDA/Ag nanocomposite particles can be considered as the promising antibacterial materials for future biomedical applications., (Copyright © 2015 Elsevier B.V. All rights reserved.)