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This study is supposed to measure the varied pyrolytic parameters of corn and wheat straw rapidly using Near Infrared Spectroscopy (NIRS). The pyrolytic parameters include starting temperature, ending temperature, mass loss ratio, devolatilization peak rate and the corresponding temperature and activation energy. The pyrolytic parameters are calculated using Thermogravimetric Analysis (TGA) data. Then the Near Infrared (NIR) spectra of sample are collected using PerkinElmer (PE) spectrometer. Finally, the prediction model was developed using chemometrics method with the help of Matlab. The results show NIRS is feasible to predict the total mass loss ratio, starting temperature, ending temperature and peak temperature perfectly under all of the three different heating rates, with the relative standard deviation (RSD) being smaller than 2.86%. The NIRS can predict the mass loss ratio and peak rate well under all of the three different heating rates, with the RSD being smaller than 9.62%. The accuracy of the models of heating rate being 20 °C/min is apparently lower. In addition, the NIRS models can predict mean activation energy (Ea) and the Ea for the conversion ratio from 0.3 to 0.6 very well, with the RSD value being smaller than 10%.

After half a century of development, near infrared spectroscopy has now reached a relatively mature position. It is widely used in agriculture, food, petrochemical, and pharmaceutical fields and plays an increasingly important role in industrial and agricultural production and commercial trade. The development of Standards based on NIR spectroscopy represents the degree of recognition of the technology and applications. This article reviews domestic and global near infrared spectroscopy Standards in order to collate the myriad of existing standards in one listing.

This study compared characteristics of different animal manure and examined non-parametric multivariate analysis tools’ suitability for their data mining. This can provide data and methodology support for scientific research and utilization of animal manure raw materials’ characteristics. Distribution profile testing, statistical calculation, and Spearman correlation analysis—using characteristics of 788 animal manure samples of layer, broiler, pig, dairy, and beef, with fertilizer nutrient compositions, proximate compositions, ultimate compositions, and calorific values—were conducted. Latent associations between different animal manure types’ characteristics were examined through five non-parametric multivariate analyses. All samples’ physicochemical characteristics samples showed different non-normal distributions except potassium. Volatile matter (VM), fixed carbon (FC), ash, carbon, hydrogen, oxygen, and higher/lower heating value (HHV/LHV) were correlated, and nitrogen was positively correlated with phosphorus, potassium, and sulfur. Non-parametric principal component analysis (PCA), non-parametric exploratory factor analysis (EFA), hierarchical cluster analysis (HCA), and non-metric multidimensional scaling (NMDS) obtained similar results: VM, FC, carbon, hydrogen, oxygen, HHV, and LHV had associated attributes (“energy utilization”); phosphorus, potassium, ash, nitrogen, and sulfur had intrinsic associated attributes (“fertilizer utilization”). Animal manure characteristics should be mined and analyzed using non-parametric statistical analysis methods. Non-parametric PCA, non-parametric EFA, HCA, and NMDS are suitable for this purpose.

Producing organic fertilizer by aerobic composting is an effective way to solve the livestock manure pollution problem and to achieve economic utilization of the valuable resource. To control the composting process effectively and ensure the quality of such organic fertilizers, it is necessary to quantify the key parameters and provide timely feedback of their changes during the composting process. In the industrial field, the traditional laboratory analysis is being transferred into process analysis. This study explored the application of real-time and field monitoring of the key parameters in the industrial trough composting process using handheld near infrared (NIR) spectroscopy and evaluated its ability to accurately predict these changes. The results showed that the handheld NIR could accurately detect moisture content (MC), total nitrogen (TN), total carbon (TC), the carbon/nitrogen (C/N) ratio, organic matter (OM) and electrical conductivity (EC) during the trough composting process, with excellent predictions for MC, good predictions for TN and OM, approximate predictions for TC, C/N ratio and EC. Changes in NIR-predicted values and measured values were consistent as the composting process progressed. The handheld NIR sensor shows good potential for real-time and field monitoring of the composting process and organic fertilizer quality assurance.

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.

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 Cu

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.

A new method for rapidly detecting of total chlorogenic acids (CGAs) in plants by surface-enhanced Raman spectroscopy (SERS) based on reusable Cu

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.

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 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.

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.

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 thorough assessment of the microstructural changes and synergistic effects of hydrothermal and/or ultrafine grinding pretreatment on the subsequent enzymatic hydrolysis of corn stover was performed in this study. The mechanism of pretreatment was elucidated by characterizing the particle size, specific surface area (SSA), pore volume (PV), average pore size, cellulose crystallinity (CrI) and surface morphology of the pretreated samples. In addition, the underlying relationships between the structural parameters and final glucose yields were elucidated, and the relative significance of the factors influencing enzymatic hydrolyzability were assessed by principal component analysis (PCA). Hydrothermal pretreatment at a lower temperature (170 °C) combined with ultrafine grinding achieved a high glucose yield (80.36%) at a low enzyme loading (5 filter paper unit (FPU)/g substrate) which is favorable. The relative significance of structural parameters in enzymatic hydrolyzability was SSA > PV > average pore size > CrI/cellulose > particle size. PV and SSA exhibited logarithmic correlations with the final enzymatic hydrolysis yield.

Livestock and poultry manures are important biomass resources that can produce biogas by pyrolysis. Manures contain varied components and alkali metal contents, which may increase the complexity of pyrolysis. The influence of alkali metals in raw manure on the pyrolysis characteristics and the properties of the released gaseous product between different livestock and poultry manures were compared by thermogravimetric analysis coupled with Fourier-transform infrared spectroscopy and mass spectrometry (TGA–FTIR–MS). Five livestock and poultry manures showed different pyrolysis characteristics caused by the different components and alkali metal contents. The high alkali metal contents of swine manure (SM), broiler litter (BL), and layer chicken litter (LL), each of which showed one pyrolysis peak, catalytically reduced the activation energy; dairy manure (DM) and beef manure (BM) from cattle showed two overlapped pyrolysis peaks. The second release peaks of CH4 for SM, BL, and LL were more intense than those of DM and BM, corresponding to the lower absorption intensities of C O and C O C/C C for SM, BL, and LL compared to those of DM and BM. The FTIR spectra of raw manures and biochars obtained at different final pyrolysis temperatures showed that the decreased absorptions of characteristic peaks in the biochars corresponded to the variations in the gaseous products. SM released the highest volume of H2 and a high CH4 yield, indicating that SM is more suitable for biogas production by pyrolysis gasification than other animal manures.

The objectives of the current study were to explore the correlation between Raman spectroscopy and lipid characteristics and to assess the potential of Raman spectroscopic methods for distinguishing the different sources of animal-originated feed based on lipid characteristics. A total of 105 lipid samples derived from five animal species have been analyzed by gas chromatography (GC) and FT-Raman spectroscopy. High correlations (r2>0.94) were found between the characteristic peak ratio of the Raman spectra (1654/1748 and 1654/1445) and the degree of unsaturation of the animal lipids. The results of FT-Raman data combined with chemometrics showed that the fishmeal, poultry, porcine and ruminant (bovine and ovine) MBMs could be well separated based on their lipid spectral characteristics. This study demonstrated that FT-Raman spectroscopy can mostly exhibit the lipid structure specificity of different species of animal-originated feed and can be used to discriminate different animal-originated feed samples.

The objective of the present study was to explore the effective spectral bands related to lipid characteristics in spectra of raw animal-derived feedstuff and figure out which marked spectral regions (single or combined) contributed more to species discrimination. A total of 82 meat and bone meals, including porcine, poultry, bovine, ovine, and fish, were studied. Raw materials, extracted lipid, and defatted samples were simultaneously analyzed and calculated using Fourier transform infrared (FT-IR) spectroscopy in combination with chemometric methods. Taking the spectra of lipid as references, five marked spectral regions considered the main lipid characteristic regions were found in the raw animal-derived feedstuff spectra. In the study, single and combined marked spectral bands were investigated and proved to have better performance than the whole spectra of raw terrestrial animal-derived feedstuff and fishmeal. For the discrimination of five animal species, the regions of 1800–1650 cm–1, 1500–1330 cm–1, 1260–1060 cm–1, and 790–640 cm–1 presented better results; for the classification of three categories, the regions of 3100–2800 cm–1, 1800–1650 cm–1, and 1500–1330 cm–1 showed the best results.

Online near infrared reflectance spectroscopy combined with characteristic wavelength variables was used to establish a fast and nondestructive analytical method for the classification of fish meal produced in China and Peru. In this study, 117 fish meal samples (47 from China and 70 from Peru) were scanned in the spectral range of 1000–2500 nm by the online near infrared spectroscopy instrument applied on the conveyor belt. The K–S (Kennard–Stone) method was used for the division of samples into calibration and validation sets. Principal component analysis and partial least square discriminant analysis were applied to classify fish meal samples. The results showed that the discrimination accuracies with calibration and validation set samples were 100% and 89.74%, respectively, for the partial least square discriminant analysis model using the full spectrum after the optimimal spectral pre-treatment. Then competitive adaptive reweighted sampling (CARS) was used to select the characteristic wavelength variables for partial least square discriminant analysis model analysis, and the discrimination accuracy for the validation set increased to 94.87%. All the results indicated that online near infrared spectroscopy combined with characteristic wavelength variables could be used for discriminating fish meal samples produced in different places, which offers feed purchasers an effective, reliable, and real-time analysis method for the identification and authentication of the commercial fish meal product.

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.

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.

International audience; A local-based method for near-infrared spectroscopy predictions, the local partial least squares regression on global PLS scores (LPLS-S), is proposed in this work and compared with the usual local PLS (LPLS) regression approach. LPLS-S is based on the idea of replacing the original spectra with a global PLS score matrix before using the usual LPLS. This is done with the aim of increasing the speed of the calculations, which can be an important parameter for online applications in particular, especially when implemented on large databases. In this study, the performance of the two local approaches was compared in terms of efficiency and speed. It could be concluded that the root-mean-square error of prediction of LPLS and LPLS-S were 1.1962 and 1.1602, respectively, but the calculation speed for LPLS-S was more than 20 times faster than for the LPLS algorithm.

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.

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 quality and safety of soybean meal is a key matter for the livestock breeding and food industries, since it is one of the most important and widely used protein feed raw materials. As driven by commercial interests, new illegal adulterants which are unknown to consumers and regulators emerge constantly. In order to make up for the inadequacy of traditional detection methods, a novel non-targeted adulterant screening method based on a near-infrared microscopy spectral library of soybean meal is proposed. This study focused on the feasibility of non-targeted screening methods for the detection of adulteration in soybean meal. Six types of non-protein nitrogen were taken as examples and partial least squares discriminant analysis was employed to verify the feasibility of this novel method. The results showed that the non-targeted screening method could screen out adulterations in soybean meal with satisfactory results.

Knowledge of the compositional properties of crop residues feedstocks is important for process control and handling of co-products and waste from energy utilization. In this study, 1076 crop residues were collected from 326 sites across China. Chemical analysis of five organic chemical compositions, four proximate compositions, five ultimate compositions and eight mineral elements were conducted. Energy potentials of crop residues in China were quantified based on their compositional characterization. Results showed that the compositional characteristics of wheat straw, rice straw, corn stover, rape stalk and cotton stalk were shown to be significant different ( p p p

In the past decade, several major food safety crises originated from problems with feed. Consequently, there is an urgent need for early detection of fraudulent adulteration and contamination in the feed chain. Strategies are presented for two specific cases, viz. adulterations of (i) soybean meal with melamine and other types of adulterants/contaminants and (ii) vegetable oils with mineral oil, transformer oil or other oils. These strategies comprise screening at the feed mill or port of entry with non-destructive spectroscopic methods (NIRS and Raman), followed by post-screening and confirmation in the laboratory with MS-based methods. The spectroscopic techniques are suitable for on-site and on-line applications. Currently they are suited to detect fraudulent adulteration at relatively high levels but not to detect low level contamination. The potential use of the strategies for non-targeted analysis is demonstrated.

The objective of present study is mainly to compare the discrimination capabilities of near-infrared Fourier transform Raman (FT-Raman, λex~1064 nm) and visible-Raman (vis-Raman, λex~532 nm) spectroscopy for animal fats. All animal fats were analysed by gas chromatography and then the spectra were collected using FT-Raman and vis-Raman spectroscopy. Although vis-Raman spectra partly presented fluorescence interference, the resulting Raman spectra still had higher signal-to-noise ratio than the corresponding FT-Raman spectra. Animal fats from different species produced obvious differences in peak intensity ratios at 1653/1745 and 1653/1441 cm−1, as well as shoulders at 1298 and 1264 cm−1. Chemometric analysis demonstrated that vis-Raman spectroscopy possessed better discrimination capability for animal fat compared to FT-Raman spectroscopy. According to the chemical structure-related basis, the differing degrees of unsaturated fatty acid and relative cis/trans fatty acid contents could be sufficiently reflected in Raman spectra and were the important factors for the successful species discrimination of animal fat.

Low temperature pyrolysis is effective to improve the fuel quality of wheat straw and corn stover. To determine the pyrolysis condition and fully utilizing the solid product (bio-char), the fuel characteristics of the bio-char should be measured. It costs a lot of time and money to finish all the measurement, which really retards the development of the biomass industry. This study is supposed to immediately predict the proximate analysis parameters, ultimate analysis parameters and heat value of sixty-five bio-char using NIRS. The results show the fuel characteristics of the bio-char vary in a very big range. The NIRS models can predict the VM, FC, ash, HHV, oxygen, carbon, hydrogen contents of the bio-char accurately. The Rc and Rcv2 value are all bigger than 0.92 and the RPD are bigger than 3.59. The NIRS models can also tell the high or low content of moisture and nitrogen. Only for the sulphur, more work need to do to improve the accuracy of the NIRS models. Predicting the fuel characteristic of the bio-char only by scanning the NIR spectrum, which costs only 10 seconds, will really be helpful for the biomass industry.

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.

Understanding the biochemical heterogeneity of plant tissue linked to crop straw anatomy is attractive to plant researchers and researchers in the field of biomass refinery. This study provides an in situ analysis and semiquantitative visualization of major components distribution in internodal transverse sections of wheat straw based on Fourier transform infrared (FTIR) microspectroscopic imaging, with a fast non-negativity-constrained least squares (fast NNLS) fitting. This paper investigates changes in biochemical components of tissue during stages of elongation, booting, heading, flowering, grain-filling, milk-ripening, dough, and full-ripening. Visualization analysis was carried out with reference spectra for five components (microcrystalline cellulose, xylan, lignin, pectin, and starch) of wheat straw. Our result showed that (a) the cellulose and lignin distribution is consistent with that from tissue-dyeing with safranin O-fast green and (b) the distribution of cellulose, lignin, and starch is consistent with chemical images for characteristic wavelength at 1432, 1507, and 987 cm

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.

Antibiotic mycelial residues (AMRs) added to animal feeds easily lead to drug resistance that affects human health and environment. However, there is a lack of effective detection methods, especially a fast and convenient detection technology, to distinguish AMRs from other components in animal feeds. To develop effective detection methods, two types of global Mahalanobis distance (GH) algorithms based on near-infrared microscopy (NIRM) imaging are proposed. The aim of this study is to investigate the feasibility of using NIRM imaging to identify AMRs in soybean meals. We prepared 15 mixed samples containing 5% AMRs using three types of soybean meals and four types of AMRs. The GH algorithm was used to identify non-soybean meals among the mixed samples. The hierarchical cluster analysis was employed to verify the recognition accuracy. The results indicate that use of the GH algorithm could identify soybean meals with AMR at a level as low as 5%.

In this study, 137 corn distillers dried grains with solubles (DDGS) samples from a range of different geographical origins (Jilin Province of China, Heilongjiang Province of China, USA and Europe) were collected and analysed. Different near infrared spectrometers combined with different chemometric packages were used in two independent laboratories to investigate the feasibility of classifying geographical origin of DDGS. Base on the same dataset, one laboratory developed a partial least square discriminant analysis model and another laboratory developed an orthogonal partial least square discriminant analysis model. Results showed that both models could perfectly classify DDGS samples from different geographical origins. These promising results encourage the development of larger scale efforts to produce datasets which can be used to differentiate the geographical origin of DDGS and such efforts are required to provide higher level food security measures on a global scale.

In this study, 838 representative animal manure samples were collected from 552 sites across China. Chemical analyses of 19 parameters, including the proximate analysis, ultimate analysis, heating values and some essential mineral elements, were conducted on the samples. The energy potential of animal manure in China was quantified based on the chemical analysis. The results showed that the chemical characteristics of the different types of animal manure were highly variable. Principal component analysis results demonstrated that the closest compositional characteristics were between dairy manure and beef manure. Several strong and important associations, namely phosphorus–magnesium, phosphorus–copper, phosphorus–zinc, copper–zinc, calcium–potassium, fixed carbon–calcium, volatile matter–iron and ash–volatile matter–fixed carbon–carbon–hydrogen–higher heating value–lower heating value, were identified. The calculated outputs of total potential annual thermal energy, syngas and methane yields from the five types of animal manure in China were 4400.63 TJ, 983.40 × 10 9 m 3 and 188.89 × 10 9 m 3 , respectively. The air gasifier energy conversion efficiency ( t = 850 °C, equivalence ratio (ER) = 0.3) for different animal manures was in the range of 66.80–83.22%.

Crop stalks are an important source of lignocellulosic biomass, and have been broadly developed as one kind of renewable resources. Tissue compositional heterogeneity linked to crop stalks anatomy is attractive to plant researchers and researchers in the field of the biomass-refinery. This study is to develop a high-efficiency and rich-information characterization technique based on Fourier transform infrared (FTIR) microspectroscopic imaging for achieving an insight into the crop stalks, in which corn and cotton are taken for example, by creatively “seeing” crop stalks tissue structure in a chemical sense. Chemical images of cellulose, hemicelluloses and lignin that can be intuitively linked to tissue structure were produced based on FTIR microspectroscopic imaging with diagnostic spectral peaks. The principal components analysis was then applied to reveal the major spectral variances among different tissues. For cotton stalk transverse section, lignin is highly distributed in the xylem, while cellulose is uniformly distributed, and hemicellulose is highly distributed in the pith, in contrast to lignin. For corn stalk internodal transverse section, lignin is concentrated in the mechanical tissues, and again, cellulose is relatively uniformly distributed, while hemicellulose is concentrated in the parenchymal cells, in contrast to lignin. The results show that FTIR microspectroscopic imaging is an ideal technique for analyzing the chemical characterization linked to tissue structure in crop stalk transverse section.

Contaminated meat and bone meal (MBM) in animal feedstuff has been the source of bovine spongiform encephalopathy (BSE) disease in cattle, leading to a ban in its use, so methods for its detection are essential. In this study, five pure feed and five pure MBM samples were used to prepare two sets of sample arrangements: set A for investigating the discrimination of individual feed/MBM particles and set B for larger numbers of overlapping particles. The two sets were used to test a Markov random field (MRF)-based approach. A Fourier transform infrared (FT-IR) imaging system was used for data acquisition. The spatial resolution of the near-infrared (NIR) spectroscopic image was 25 μm × 25 μm. Each spectrum was the average of 16 scans across the wavenumber range 7,000-4,000 cm(-1), at intervals of 8 cm(-1). This study introduces an innovative approach to analyzing NIR spectroscopic images: an MRF-based approach has been developed using the iterated conditional mode (ICM) algorithm, integrating initial labeling-derived results from support vector machine discriminant analysis (SVMDA) and observation data derived from the results of principal component analysis (PCA). The results showed that MBM covered by feed could be successfully recognized with an overall accuracy of 86.59% and a Kappa coefficient of 0.68. Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR spectroscopic images. This new approach enhances the identification of MBM using NIR spectroscopic imaging.

On-line measurement of crop straw fuel properties is important for its effective utilization. Realizing on-line measurement using near infrared spectroscopy (NIRS) is potential but challenging. The influence of NIRS on-line acquisition parameters such as focal distance, speed of the belt, integration time on the spectral repeatability was studied using two different spectrometers. The spectral repeatability was evaluated by root mean square ( RMS ) value. The results showed that integration time influenced spectral repeatability significantly for both of the spectrometers. The spectral repeatability increased following the increase of integration time. However, the influence on the spectral repeatability of focal distance and speed of the belt were different for the two different spectrometers. Focal distance and speed of the belt had a significant influence on the spectral repeatability only for spectrometer A. The spectral repeatability decreased following the increase of focal distance or speed of the belt.

This study was aimed at exploring the feasibility of detecting and quantifying melamine, and the structural analogue cyanuric acid, contamination in soybean meal, using line-scan near infrared (NIR) hyperspectral imaging spectroscopy (HIS). Soybean meal is one of the main ingredients used in the feed industry because it offers a complete protein profile. Each year, demand increases for soybean products and soya oil, the consumption of which is directly boosted by Chinese consumers' growing wealth, and for soybean meal, which is indirectly affected by the growing demand for meat. Recent cases of deliberate melamine contamination of soybean meal have been reported. This study focuses on the development of a methodology based on NIR–HIS for the acquisition, treatment and interpretation of images and spectra, as well as for the detection and quantification of melamine and cyanuric acid contamination in soybean meal. A total of 40 commercial soybean meal samples were collected, and 17 adulterated samples were prepared by adding different amounts of melamine/cyanuric acid to the samples, with concentrations varying between 0.5% and 5%. The spectral data were collected using line-scan NIR–HIS, and a qualitative model was created based on a principal-component analysis (PCA), whereas partial least-squares discriminant analysis was used to obtain a discrimination model and a semi-quantitative prediction of the content of contaminant. This study has permitted the detection of low levels of melamine and also revealed some limitations for the feasibility of quantifying melamine in soybean meal.

Conventional methods based on wet chemical analysis for investigating heavy metals in animal manure are relatively expensive and time-consuming. This study investigated the use of near infrared (NIR) spectroscopy to detect copper (Cu) in animal manure and then identified the spectral basis of the calibration. A total of 118 pig manure samples were collected from four provinces in China. Spectra were acquired in the range of 10,000 to 4000 cm−1 using a Fourier-transform near infrared spectrometry system. Results showed that the prediction of Cu concentration in pig manure by NIR spectroscopy is feasible ( r2 = 0.84, root mean square error of prediction= 198 mg kg−1, ratio of standard error to standard deviation = 2.4). Cu in pig manure can be detected by NIR spectroscopy because a high percentage of the Cu is complexed with CONH2 or CONHR functional groups of organic ligands such as protein, urea, amino acids and other amide compounds.