InSituAnalyze: A Python Framework for Multicomponent Synchronous Analysis of Spectral Imaging.

Spectral imaging is visualization of high precision and high sensitivity and suitable for analyzing the spatial distribution of complex materials. While providing rich and detailed information, it makes higher demands on feature extraction and information mining of high-dimensional data. For the convenience of further utilization, our research team has developed a Python framework for the multicomponent synchronous analysis of spectral imaging based on a characteristic band method and fast-NNLS algorithm, helping to handle spectrum data from complex samples and gaining semiquantitative information on the sample on the scale of pixel based on target components. With the help of the easy-to-use framework, users are leading to choose suitable pretreatment methods for images and spectra, extract spatial information on tissues/structures account of multispace, and conduct analysis on target components in an intuitive and timesaving way. The sophisticated functional architecture also makes the framework expedited to add algorithms and supported data formats.

Wheat straw biochar as an additive in swine manure Composting: An in-depth analysis of mixed material particle characteristics and interface interactions.

In recent research, biochar has been proven to reduce the greenhouse gases and promote organic matter during the composting. However, gas degradation may be related to the microstructure of compost. To investigate the mechanism of biochar additive, composting was performed using swine manure, wheat straw and biochar and representative solid compost samples were analyzed to characterize the mixed biochar and compost particles. We focused on the microscale, such as the particle size distributions, surface morphologies, aerobic layer thicknesses and the functional groups. The biochar and compost particle agglomerations gradually became weaker and the predominant particle size in the experiment group was < 200 µm. The aerobic layer thickness (Lp) was determined by infrared spectroscopy using the wavenumbers 2856 and 1568 cm-1, which was 0-50 µm increased as composting proceeded in both groups. The biochar increased Lp and facilitated oxygen penetrating the compost particle cores. Besides, in the biochar-swine manure particle interface, the aliphatic compound in the organic components degraded and the content of aromaticity increased with the composting process, which was indicated by the absorption intensity at 2856 cm-1 decreasing trend and the absorption intensity at 1568 cm-1 increasing trend. In summary, biochar performed well in the microscale of compost pile.

A novel in-situ quantitative profiling approach for visualizing changes in lignin and cellulose by stained micrographs.

There is a strong need for low-cost lignocellulosic composition simultaneous localization methodologies to benefit deeper understandings of crop stalk morphology. This study developed a robust quantitative safranin O-fast green staining-based optical microscopy imaging methodology for in-situ simultaneously generating digital profiles of lignin and cellulose in stalk tissues. Foreground extraction and dye residue removal of stained images were adapted. The ratios of normalized red (R), green (G), and blue (B) channel signal intensity, R/B and G/B, were defined as quantitative indicators of lignin and cellulose, respectively. The method was validated on model rice with known bioinformatics, and the results were consistent with those of fluorescence microscopy and immunogold labeling methods. The high-definition spatial in-situ simultaneous profiles of lignin and cellulose in alkali-treated maize stalk tissues and their variations were visualized. This low-cost, cell-scale method is expected to contribute to new discoveries in many areas of biomass refining and plant science.