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.

Micro-aerobic conditions based on membrane-covered improves the quality of compost products: Insights into fungal community evolution and dissolved organic matter characteristics.

This study investigated the effects of micro-aerobic conditions on fungal community succession and dissolved organic matter transformation during dairy manure membrane-covered composting. The results showed that lignocellulose degradation in the micro-aerobic composting group (AC: oxygen concentration < 5 %) was slower than that in the static composting group (SC: oxygen concentration < 1 %), but the dissolved organic carbon in AC was greatly increased. The degree of aromatic polymerization was higher in AC than in SC. But the carboxyl carbon and alcohol/ether biodegradations were faster in SC than in AC, which promoted carbon dioxide and methane emissions, respectively. The relative abundances of pathogenic and dung saprotrophic fungi in AC were 44.6 % and 10.59 % lower than those in SC on day 30, respectively. Moreover, the relative abundance of soil saprotrophs increased by 5.18 % after micro-aerobic composting. Therefore, micro-aerobic conditions improved the quality of compost products by influencing fungal community evolution and dissolved organic matter transformation.

Rapid and simultaneous detection of multiple illegal additives in feed and food by SERS with reusable Cu2O-Ag/AF-C3N4 substrate.

Illegal additives can bring the economic benefit, resulting in the continuous irregularities in the use of illegal additives. In this study, a method for rapid, sensitive, and simultaneous detection of multiple illegal additives including enrofloxacin, malachite green, nitrofurazone, and Sudan Ⅰ in feed and food samples by surface-enhanced Raman spectroscopy (SERS) with Cu2O-Ag/AF-C3N4 composite substrate was developed. A Cu2O-Ag/AF-C3N4 composite substrate was prepared by reacting Cu2O modified by AF-C3N4 nanosheets with AgNO3 solution. The substrate has a limit of detection (LOD) of 1.29 × 10-6 mg/L, a good linear relationship of between 10-6 and 10-2 mg/L, and an R2 value of 0.95 for Rhodamine B detection. Furthermore, the substrate showed high uniformity and reproducibility, with relative standard deviations (RSD) of 6.74% and 4.85%, respectively. Adding AF-C3N4 nanosheets not only increased the enhancement effect of the substrate, which was 4.4 times of that before addition, but also endowed it with good self-cleaning characteristics owing to its excellent photocatalytic activity. The substrate can be reused, with over 80% of the original Raman signal strength remaining after four repeat uses. The SERS based on the above substrate was used to detect the illegal additives, the LOD of enrofloxacin, malachite green, nitrofurazone, and Sudan Ⅰ can reach 4.67 × 10-4 mg/L, 2.57 × 10-5 mg/L, 5.7 × 10-7 mg/L and 6.92 × 10-5 mg/L. The results reveal that this substrate has great application potential in feed and food safety.

A novel analytical strategy for discriminating antibiotic mycelial residue adulteration in feed based on ATR-IR and microscopic infrared imaging.

The Antibiotic mycelial residue (AMR) contains antibiotic residue, there are safety risks if it is used illegally in feed. This study investigated the feasibility of qualitative identification of AMR in protein feed and self-prepared feed based on attenuated total reflection mid-infrared spectrum (ATR-IR) and microscopic infrared imaging. Cottonseed meal (CM), soybean meal (SM), distillers dried grains with solubles (DDGS), nucleotide residue (NR), oxytetracycline residue (OR) and streptomycin sulfate residue (SR) and two self-prepared feed (broiler and pig) were used as research objects. The results showed that there were characteristic peaks at 1614 cm-1, 1315 cm-1, 779 cm-1, 514 cm-1 in the ATR-IR spectra of AMR, which were related to calcium oxalate hydrate. After detection, the content of total calcium and calcium oxalate in AMR were higher than those in protein feed. ATR-IR can quickly realize the qualitative discrimination of pure material samples. The combination of ATR-IR and partial least squares discriminant analysis (PLSDA) was effective in discriminating AMR from CM and SM with a single component (the classification errors were 0), but it cannot meet the discrimination of AMR from the fermented protein feed (such as DDGS and NR, the classification errors were 0.10 and 0.12) and self-prepared feed with complex components. Compared with ATR-IR, microscopic infrared imaging was less affected by the sample complexity. Multi-component samples belong to physical mixing and will not affect the infrared spectra of each component. Therefore, microscopic infrared imaging combined with effective information extraction algorithms such as cosine similarity can distinguish OR in the fermented protein feed and self-prepared feed. The above results showed that the advantages of ATR-IR and microscopic infrared imaging were complementary, which provided a new idea for the discrimination analysis of illegal feed additives.

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.