Deployment strategy of multiple miniaturized near-infrared spectrometers based on spectral transfer for characterizing soil organic matter and nitrogen.

Developing timely, convenient, and low-cost methods for high-frequency characterization of soil nutrients is necessary for implementing precise soil nutrient management. With the current availability of numerous calibration models of laboratory benchtop near-infrared (NIR) spectrometers for rapid soil nutrient characterization and the appearance of low-cost, convenient miniaturized NIR spectrometers, this study proposes an efficient deployment strategy to address model failure due to inter-device variation based on spectral transfer. The strategy involves using Direct Standardization (DS) to migrate the spectra from multiple miniaturized NIR spectrometers with a laboratory benchtop NIR spectrometer and then directly applying the existing calibration models of the laboratory benchtop instrument to the transferred spectra for soil nutrient analysis. The results indicated that the DS method successfully transferred the spectra of miniaturized devices to be consistent with the spectra of the laboratory benchtop instrument. The soil organic matter (SOM) predictions using the transferred spectra and the calibration models of the laboratory benchtop instrument were even more accurate than those using the respective models developed for each miniaturized devices, with root mean square error (RMSE) of 0.177 %, 0.177 %, and 0.150 %, respectively, while the performances of total nitrogen (TN) predictions were comparable to those using the respective models, with RMSE of 0.013 %, 0.012 %, and 0.010 %, respectively. Bland-Altman plots demonstrated good consistency between the strategy proposed in this study and the strategy of developing respective models for each miniaturized device, with no difference in predictions for the independent validation set compared to the laboratory benchtop instrument. This study proved the feasibility of deployment strategy of multiple miniaturized NIR spectrometers based on spectral transfer, offering a new solution for high-frequency on-site soil nutrient characterization.

A new spectral simulating method based on near-infrared hyperspectral imaging for evaluation of antibiotic mycelia residues in protein feeds.

Antibiotic mycelia residues (AMRs) contain antibiotic residues. If AMRs are ingested in excess by livestock, it may cause health problems. To address the current problem of unknown pixel-scale adulteration concentration in NIR-HSI, this paper innovatively proposes a new spectral simulation method for the evaluation of AMRs in protein feeds. Four common protein feeds (soybean meal (SM), distillers dried grains with solubles (DDGS), cottonseed meal (CM), and nucleotide residue (NR)) and oxytetracycline residue (OR) were selected as study materials. The first step of the method is to simulate the spectra of pixels with different adulteration concentrations using a linear mixing model (LMM). Then, a pixel-scale OR quantitative model was developed based on the simulated pixel spectra combined with local PLS based on global PLS scores (LPLS-S) (which solves the problem of nonlinear distribution of the prediction results due to the 0%-100% content of the correction set). Finally, the model was used to quantitatively predict the OR content of each pixel in hyperspectral image. The average value of each pixel was calculated as the OR content of that sample. The implementation of this method can effectively overcome the inability of PLS-DA to achieve qualitative identification of OR in 2%-20% adulterated samples. In compared to the PLS model built by averaging the spectra over the region of interest, this method utilizes the precise information of each pixel, thereby enhancing the accuracy of the detection of adulterated samples. The results demonstrate that the combination of the method of simulated spectroscopy and LPLS-S provides a novel method for the detection and analysis of illegal feed additives by NIR-HSI.

In-situ registration subtraction image segmentation algorithm for spatiotemporal visualization of copper adsorption onto corn stalk-derived pellet biochar by micro-computed tomography.

The non-homogeneous structure and high-density ash composition of biochar matrix pose significant challenges in characterizing the dynamic changes of heavy metal adsorption onto biochar with micro-computed tomography (Micro-CT). A novel in-situ registration subtraction image segmentation method (IRS) was developed to enhance micro-CT characterization accuracy. The kinetics of Cu(II) adsorption onto pellet biochar derived from corn stalks were tested. Respectively, the IRS and traditional K-means algorithms were used for image segmentation to the in-situ three-dimensional (3D) visual characterization of the Cu(II) adsorption onto biochar. The results indicated that the IRS algorithm reduced interference from high-density biochar composition, and thus achieved more precise results (R2 = 0.95) than that of K-means (R2 = 0.72). The visualized dynamic migration of Cu(II) from surface adsorption to intraparticle diffusion reflexed the complex mechanism of heavy metal adsorption. The developed Micro-CT method with high generalizability has great potential for studying the process and mechanism of biochar heavy metal adsorption.

The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery.

BACKGROUND: The advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop's condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements. RESULTS: The results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The [Formula: see text] of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The [Formula: see text] of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition. CONCLUSIONS: Our work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

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.

Natural variation of DROT1 confers drought adaptation in upland rice.

Upland rice is a distinct ecotype that grows in aerobic environments and tolerates drought stress. However, the genetic basis of its drought resistance is unclear. Here, using an integrative approach combining a genome-wide association study with analyses of introgression lines and transcriptomic profiles, we identify a gene, DROUGHT1 (DROT1), encoding a COBRA-like protein that confers drought resistance in rice. DROT1 is specifically expressed in vascular bundles and is directly repressed by ERF3 and activated by ERF71, both drought-responsive transcription factors. DROT1 improves drought resistance by adjusting cell wall structure by increasing cellulose content and maintaining cellulose crystallinity. A C-to-T single-nucleotide variation in the promoter increases DROT1 expression and drought resistance in upland rice. The potential elite haplotype of DROT1 in upland rice could originate in wild rice (O. rufipogon) and may be beneficial for breeding upland rice varieties.

New rapid detection method of total chlorogenic acids in plants using SERS based on reusable Cu2O-Ag substrate.

A new method for rapidly detecting of total chlorogenic acids (CGAs) in plants by surface-enhanced Raman spectroscopy (SERS) based on reusable Cu2O-Ag substrate was developed in this study. The Cu2O-Ag substrate prepared by the in-situ growth method had high uniformity with peak intensity relative standard deviation (RSD) of 5.27%, repeatability with peak intensity RSD of 3.58%, and sensitivity with an analytical enhancement factor of 1.27 × 105 for detecting CGAs. Furthermore, the substrate had excellent reusability, after it was reused for seven cycles, the signal strength of CGAs was still above 80% of initial. Compared with the standard method of high-performance liquid chromatography (HPLC), the SERS method can successfully analyze the contents of total CGAs in plants, such as Stevia rebaudiana leaves, coffee beans, Lonicera japonica leaves, and Eucommia ulmoides flowers, with recovery rate from 93.26% to 112.65%, and the limit of detection was 0.13 µg/mL. The total CGAs content of Stevia rebaudiana leaves samples detected by HPLC and SERS have good consistency with R = 0.9760 and RMSE = 3286 mg/kg. Furthermore, the SERS method only needed less than 1 min, one standard and reusable substrate in this study to analyze, which can further reduce the cost of method analysis. Therefore, the SERS method with the appropriate substrate can provide a rapid, accurate, and economic way to detect the total CGAs in plants.

Exploring the impact of biochar on antibiotics and antibiotics resistance genes in pig manure aerobic composting through untargeted metabolomics and metagenomics.

This study investigated the effect of biochar on antibiotics and antibiotic resistance genes (ARGs) during aerobic composting of pig manure. First, the composition and content of antibiotics in the manure were determined qualitatively and quantitatively. Biochar promoted the degradation of these antibiotics (oxytetracycline, chlortetracycline, and tetracycline). The relative abundance (RA) of antibiotic-resistant bacteria carrying ARGs accounted for about 29.32% of the total bacteria. Firmicutes and Actinomycetes were dominant phylum-level bacteria at the early and late stages of composting, respectively. Biochar decreased the total RA of ARGs by 16.83%±4.10%. tetW and tetL, closely related to tetracycline resistance, were significantly diminished during aerobic composting, and biochar was able to promote this removal. Biochar enhanced RAs of Mycobacterium tuberculosis kasA mutant. RAs of ARGs related to antibiotic efflux pumps, such as baeS and arlS, remained at a high level. Conclusively, biochar promotes degradation of antibiotics and removal of ARGs.

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 particle scale micro-CT approach for 3D in-situ visualizing the Pb (II) adsorption in different crop residue-derived chars.

It is crucial to develop a new characterization method to provide insight into the complex adsorption mechanism of crop residue-derived char. This study established a novel 3D in-situ visualization method for qualitative and semi-quantitative characterizing Pb (II) adsorption profiles in crop residue-derived char particles. First, coconut shell activated carbon, rice husk biochar, and wheat biochar after Pb (II) adsorption was used for X-ray micro-CT imaging. Then, the K-means clustering algorithm was developed for segmenting the volume image of samples, and the optimized segmentation thresholds for the 3 samples were 6000HU, 7000HU, and 1300HU, respectively. The rendered images for qualitative illustrating the adsorption profile of Pb (II) were presented. Finally, based on the derived quantitative formula, the Pb (II) distribution in the biochar particle was presented for the first time. This method provided a new perspective and methodology for analysis and simulations of the adsorption behavior of heavy metals onto chars.

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.

Complementarity of FT-IR and Raman spectroscopies for the species discrimination of meat and bone meals related to lipid molecular profiles.

The objective of this study is to realise the successful species discrimination of meat and bone meals (MBMs) based on the complementarity of FT-IR and Raman spectra. The spectral variation of typical lipid profiles on FT-IR and Raman spectra of MBMs as well as the chemical structure-related principle of FT-IR and Raman spectroscopies related to lipid characteristics were investigated. Lipids from MBMs were separately collected by FT-IR and Raman spectroscopes, which illustrated both spectra (1800 ~ 900 cm-1) presented different typical lipid peaks. The combination of FT-IR and Raman spectra contributed to establish the more reliable and robust species discrimination model compared to single FT-IR or Raman spectra due to more detailed and integrated molecular vibration information. Degree of unsaturation and cis/trans fatty acid contents were considered the important chemical structure-related factors for ideal species discrimination. Complementation of FT-IR and Raman spectra performed synergistic enhancement to the species discrimination with diverse contributions.

Metagenomic and q-PCR analysis reveals the effect of powder bamboo biochar on nitrous oxide and ammonia emissions during aerobic composting.

To investigate the emission mechanism of ammonia (NH3) and nitrous oxide (N2O) during aerobic composting and the influence of powder bamboo biochar (PBB) on this process, this paper conducted a systematic study on the nitrogen-transforming functional microbial community, including functional genes, microbial structure and metabolism pathways. PBB reduced N2O and NH3 emissions by 1.25%-8.72% and 10.4%-11.8%, respectively. The quantitative PCR results indicated that the reduced N2O emission by PBB were mainly related to denitrifying genes (nirS, nirK, nosZ, and narG). The metagenome results demonstrated that Nitrosococcus was the main genus that could oxidize ammonia to nitrite decreased by PBB. The PBB significantly affected the nitrogen metabolism pathway, reduced the activity of glutamate dehydrogenase to inhibit the formation of NH4+ to reduce NH3 emission. The higher N2O emission in the control group was also related to the higher relative contents of hydroxylamine reductase and nitrite reductase.

The composition characteristics of different crop straw types and their multivariate analysis and comparison.

The heterogeneity and complex composition of crop straw are some of the main obstacles to its scientific and efficient industrial utilization. To thoroughly reveal and identify the composition of different crop straw types and their latent attributes, in this study, 784 straw samples of rice, wheat, corn, rape and cotton were collected. Based on the large sample size, 18 composition characteristics, including chemical composition, proximate composition, ultimate composition, and heating values, were adopted to determine the profiles of the crop straw composition characteristics. Correlation analysis and 7 different types of multivariate analysis were applied and compared. The results indicated that among the 18 characteristics, hemicellulose, water-soluble carbohydrates, crude proteins, phosphorus, fixed carbon, hydrogen, nitrogen, and sulfur had non-normal distributions. Spearman method was a more suitable correlation analysis approach for the crop straw characteristics than Pearson method. The results of the different multivariate analysis methods were reflected in the different classification attributes of water-soluble carbohydrates, phosphorus, hydrogen and sulfur. Non-parametric principal component analysis and non-parametric exploratory factor analysis provided consistent results. The characteristics could be divided into 4 categories of intrinsic associated attributes, namely, (1) lignin, volatile matter, fixed carbon, carbon, hydrogen, higher heating value, and lower heating value; (2) potassium, ash, and sulfur; (3) cellulose, hemicellulose, moisture, and oxygen; and (4) water-soluble carbohydrates, crude proteins, phosphorus, and nitrogen, which exhibited combustion positive, combustion negative, biochemical conversion, and nutritional property, respectively. The study results provide data and methodology support for the development of crop straw utilization strategies.

Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: The example of non-protein nitrogen.

The melamine scandal indicates that traditional targeted detection methods only detect the specifically listed forms of contamination, which leads to the failure to identify new adulterants in time. In order to deal with continually changing forms of adulterations in food and feed and make up for the inadequacy of targeted detection methods, an untargeted detection method based on local anomaly detection (LAD) using near infrared (NIR) imaging was examined in this study. In the LAD method, with a particular size of window filter and at a 99% level of confidence, a specific value of Global H (GH, modified Mahalanobis distance) can be used as a threshold for anomalous spectra detection and quantitative analysis. The results showed an acceptable performance for the detection of contaminations with the advantage of no need of building a 'clean' library. And, a high coefficient of determination (R2LAD = 0.9984 and R2PLS-DA = 0.9978) for the quantitative analysis of melamine with a limit of detection lower than 0.01% was obtained. This indicates that the new strategy of untargeted detection has the potential to move from passive to active for food and feed safety control.

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.

Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging.

Near-infrared microscopy (NIRM) technology can analyze different components within a sample while also obtaining spatial information about the sample. No rapid detection methods are available for effectively identifying antibiotic mycelia residues (AMRs) in protein feeds materials to date. In this study, the feasibility of using NIRM to identify AMRs (oxytetracycline residue, streptomycin sulfate residue and clay colysin sulfate residue) mixed in cottonseed meals was studied. The samples were scanned by NIRM, then the spectra of images were analyzed by principal component analysis (PCA) to select characteristic bands for further identification with one-class partial least squares analysis (OCPLS). The results showed that: a) AMRs were effectively identified in cottonseed meal; b) screening characteristic bands and increasing the spectral number of the calibration set improved the identification results of the model; and c) the sensitivity, specificity, accuracy and class error of the method were 100%, 95.93%, 99.01% and 2.03%, respectively.

The influence of manure feedstock, slow pyrolysis, and hydrothermal temperature on manure thermochemical and combustion properties.

Slow pyrolysis and hydrothermal carbonization (HTC) of organic wastes for char preparation has been proved as an effective way for livestock manure management. Livestock manure chars were prepared by slow pyrolysis (400, 500, 600 °C) and hydrothermal carbonization (180, 210, 240 °C) at different reaction temperatures. The influences of manure type and reaction condition to element content, calorific value, char yield, energy yield, and combustion characteristic were investigated. The results illustrate that thermochemical process can strongly affect the properties of pyrolytic char and hydrochar. Compared to pyrolytic char, the hydrochar had higher heating value, higher energy yield, and lower ash content with respect to the same feedstock. The livestock manure type could also influence the properties of biochars/hydrochars. Hydrochars from swine manure, broiler litter, and layer chicken litter achieved the highest energy yield of 65.5%, 56.9%, and 64.4% at 210 °C. Dairy cattle manure and beef cattle manure displayed higher energy yield and higher comprehensive combustibility index than other manures. Furthermore, HTC can narrow the weight loss temperature range in differential thermogravimetric curve of manures. Therefore, HTC is considered as a more effective approach in carbonizing animal manure for solid biofuel compared to slow pyrolysis.

A novel FTIR discrimination based on genomic DNA for species-specific analysis of meat and bone meal.

Fourier transform infrared (FTIR) spectroscopy was applied for the species-specific identification of meat and bone meal (MBM), based on genomic DNA characteristics. A total of 51 source-reliable MBM samples, including porcine, poultry, bovine, and ovine MBM, were analysed. Genomic DNA was extracted using an optimized procedure for FTIR scanning. The results showed that a series of discriminatory FTIR spectral bands were closely related to DNA characteristics of MBM. The spectral intensity difference at 1651 cm-1 was identified as a key peak for discriminating ruminant from non-ruminant MBM. Combining FTIR data with chemometrics, a two-step protocol for discriminant analysis was established. An initial identification model of porcine, poultry, and ruminant MBM and a second model of bovine and ovine MBM were established. The results obtained using two models showed that the correct classification rate was 100%. This method could assist governments in confirming the authenticity of species to ensure feed safety.