Little is known regarding changes in cell wall structural molecules (lignin, cellulose and hemicellulose) as plant roots decompose, despite their importance for soil organic matter (OM) formation. The objectives of this study were to quantify changes in root composition during 270 d incubations often important grain and forage crops utilizing forage fiber analysis and to characterize the changes in cell wall composition and structure using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Large, species-dependent variation was observed in the extent of root tissue decomposition over time, ranging from 82.5% of initial mass for alfalfa to 21.5% for switchgrass. Fiber analysis revealed that initial rapid decomposition increased lignin concentration and cellulose concentration while hemicellulose declined, whereas all three moieties degraded proportionally thereafter. Similar trends were found in the ratios between the DRIFTS diagnostic peaks for lignin, cellulose and the carbonyls of hemicellulose and wax components. Spectra illustrated changes during decomposition, particularly in more extensively decomposed roots. Features potentially indicative of suberin preservation were found in the region between 2800 cm(-1) and 3000 cm(-1). Examination of the region between 1000 cm(-1) and 1300 cm(-1) revealed possible change in hemicellulose structure. The results illustrate the effect of differences in cell wall composition and structure during root decomposition and expand understanding of the role of roots in soil OM dynamics. Variability in root degradation and change in cell wall composition among species demonstrate that characterization of a broad range of individual species is necessary to predict root contributions to soil C. Published by Elsevier Ltd.

Diffuse reflectance spectroscopy based on near-infrared radiation [near-infrared reflectance spectroscopy (NIRS)] has become an important method for analyzing agricultural products, where large numbers of measurements are needed to detect variations in product composition that impact market value (i.e., protein in grains and food/feed composition) or to monitor and measure spatial and temporal variation in environmental parameters (i.e., soil carbon and other soil properties). Diffuse reflectance spectroscopy, using Fourier transform mid-infrared [diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS)], has also been shown to be capable of rapid quantitative analysis of agricultural products and environmental samples. Through a process termed "chemometrics," information contained in the entire spectrum is related to the property of interest by the use of multivariate statistical techniques such as stepwise regression, principle component analysis, or partial squares least regression. Mathematical models produced by these techniques form the calibration models that are used to predict properties of unknown samples. With the development of robust calibrations, NIRS and DRIFTS have the ability to analyze samples rapidly and simultaneously for multiple properties with virtually no consumables and minimal sample preparation.

Chapter 8 Qualitative Analysis Richard Kramer, Richard Kramer Applied Chemometrics, Inc., Sharon, Massachusetts, USASearch for more papers by this authorJerry Workman Jr., Jerry Workman Jr. Argose Incorporated, Waltham, Massachusetts, USASearch for more papers by this authorJames B. Reeves III, James B. Reeves III ANRI, ARS, USDA, Beltsville, Maryland, USASearch for more papers by this author Richard Kramer, Richard Kramer Applied Chemometrics, Inc., Sharon, Massachusetts, USASearch for more papers by this authorJerry Workman Jr., Jerry Workman Jr. Argose Incorporated, Waltham, Massachusetts, USASearch for more papers by this authorJames B. Reeves III, James B. Reeves III ANRI, ARS, USDA, Beltsville, Maryland, USASearch for more papers by this author Book Editor(s):Craig A. Roberts, Craig A. RobertsSearch for more papers by this authorJerry Workman Jr., Jerry Workman Jr.Search for more papers by this authorJames B. Reeves III, James B. Reeves IIISearch for more papers by this author First published: 01 January 2004 https://doi.org/10.2134/agronmonogr44.c8Citations: 1Book Series:Agronomy Monographs AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary This chapter addresses three types of qualitative analysis, namely discriminant analysis, spectral searching, and spectral interpretation. It explains the basic concepts of discriminant analysis and provides an introduction to some of the most commonly used discriminant techniques. When near-infrared (NIR) spectroscopy is used qualitatively to determine whether a particular sample is similar to or different from samples belonging to one or more distinct groups, discriminant analysis techniques are used. Spectral searching requires a computer program to match the spectrum of the unknown against an existing database. One potential advantage of spectral searching vs. conventional discriminant analysis is that discriminant techniques generally require at least several samples of each potential product in order to determine the group membership criteria. While spectral interpretation in the NIR may never be as advanced or as easy a science as is true for mid-infrared spectra, utilization of spectral knowledge can be very helpful in calibration development. Near-Infrared Spectroscopy in Agriculture, Volume 44 RelatedInformation

Plant roots, particularly the constituents of root cell walls (hemicellulose, cellulose and lignin) are important contributors to soil organic matter. Little is known about the cell wall composition of many important crop species or compositional changes as roots decay. The objectives of this study were to quantify changes in root cell wall composition during a four week laboratory incubation by forage fiber analysis and characterize those changes using diffuse reflectance infrared fourier transform spectroscopy (DRIFTS). The roots of six important crop, forage and native grass species were incubated at 25°C and sampled weekly. Alfalfa lost 78% of initial mass over four weeks, while the remaining species lost between 19% and 38%. For all species the majority of this loss occurred during Week 1, and only alfalfa mass loss was significant (P<0.05) each week. The trends observed for whole root decomposition were paralleled by the decomposability of root cell walls. Significant changes in hemicellulose, cellulose and lignin concentrations over time were only observed in alfalfa roots. Significant changes in decomposability of these constituents was likewise only observed in alfalfa, with cellulose the most decomposable fraction, followed by hemicellulose and lignin. Analysis by DRIFTS supported the fiber analysis results and revealed important changes in root cell wall composition. The disappearance of peaks due to starch in the perennial alfalfa and switchgrass roots following Week 1 helped to explain the greater initial mass loss in both of these species relative to the roots of the annuals. The spectral data also illustrated the resistance of alfalfa lignin to decomposition, the preservation of carbonyl compounds and the degradation of readily decomposed proteins. Finally, changes potentially indicative of wax compound preservation were found in the DRIFTS spectra of alfalfa even though the amount of wax was too small to quantify by fiber analysis. This research study reveals differences in the rate at which crop roots decompose and important changes that can occur in readily decomposable roots over relatively short time scales. These results provide valuable information contributing to the understanding and prediction of short term soil organic matter dynamics which will help to predict possible impact of management changes or soil disturbance on soil health and productivity as well as long term organic C stabilization and the potential for C sequestration.

This chapter covers instrument performance of near-infrared (NIR) spectroscopy. The purpose of instrument validation is to determine if a NIR instrument is performing according to specifications and to provide the means to correct any instrument misalignment or malfunction if within the domain of the user. The chapter discusses the various tests that are performed on a routine basis to assure consistent instrument performance. There are two general areas within which the tests can be classified: those based strictly on physical measurements of instrument performance and those based on the utilization of test samples and calibrations previously developed for the instrument in question. The basis of wavelength accuracy validation is comparing the instrument's spectra of a known standard with the accepted values for that standard. The wavelength values being either provided by a supplier of the standard, by a standardizing agency such as the National Institutes of Standards and Testing, or as published in various sources.

Foundries around the world discard millions of tons of sand each year even though they can be beneficially used in manufactured soils and geotechnical applications. Despite their usefulness as an aggregate replacement, some environmental authorities are concerned over potential negative impacts associated with residual organic binders in waste foundry sands (WFSs). In this study, chemically bound molding and core sands were obtained from aluminum, bronze and iron foundries that used alkyd urethane, phenolic urethane, Novolac, and natural organic binders. The aim was to use mid-infrared (MIR) spectrometry to assess binder changes within the sands during the casting process. Bands associated with C H stretching were detected in most WFSs. Mid-infrared spectra and total carbon data demonstrated that organic binders closest to the molten metal interface and subjected to the highest casting temperatures exhibited the most thermal degradation. Our results also provided preliminary evidence that MIR spectroscopy could potentially be used as a method to quantify residual binder in WFSs. Published by Elsevier B.V.

Three advanced technologies to measure soil carbon (C) density (g C m−2) are deployed in the field and the results compared against those obtained by the dry combustion (DC) method. The advanced methods are: a) Laser Induced Breakdown Spectroscopy (LIBS), b) Diffuse Reflectance Fourier Transform Infrared Spectroscopy (DRIFTS), and c) Inelastic Neutron Scattering (INS). The measurements and soil samples were acquired at Beltsville, MD, USA and at Centro International para el Mejoramiento del Maíz y el Trigo (CIMMYT) at El Batán, Mexico. At Beltsville, soil samples were extracted at three depth intervals (0–5, 5–15, and 15–30 cm) and processed for analysis in the field with the LIBS and DRIFTS instruments. The INS instrument determined soil C density to a depth of 30 cm via scanning and stationary measurements. Subsequently, soil core samples were analyzed in the laboratory for soil bulk density (kg m−3), C concentration (g kg−1) by DC, and results reported as soil C density (kg m−2). Results from each technique were derived independently and contributed to a blind test against results from the reference (DC) method. A similar procedure was employed at CIMMYT in Mexico employing but only with the LIBS and DRIFTS instruments. Following conversion to common units, we found that the LIBS, DRIFTS, and INS results can be compared directly with those obtained by the DC method. The first two methods and the standard DC require soil sampling and need soil bulk density information to convert soil C concentrations to soil C densities while the INS method does not require soil sampling. We conclude that, in comparison with the DC method, the three instruments (a) showed acceptable performances although further work is needed to improve calibration techniques and (b) demonstrated their portability and their capacity to perform under field conditions.

The term biochar refers to materials with diverse chemical, physical and physicochemical characteristics that have potential as a soil amendment. The purpose of this study was to investigate the P sorption/desorption properties of various slow biochars and one fast pyrolysis biochar and to determine how a fast pyrolysis biochar influences these properties in a degraded tropical soil. The fast pyrolysis biochar was a mixture of three separate biochars: sawdust, elephant grass and sugar cane leaves. Three other biochars were made by slow pyrolysis from three Amazonian tree species (Lacre, Inga and Embauba) at three temperatures of formation (400 degrees C, 500 degrees C, 600 degrees C). Inorganic P was added to develop sorption curves and then desorbed to develop desorption curves for all biochar situations. For the slow pyrolysis, the 600 oC biochar had a reduced capacity to sorb P (4-10 times less) relative to those biochars formed at 400 degrees C and 500 degrees C. Conversely, biochar from Inga desorbed the most P. The fast pyrolysis biochar, when mixed with degraded tropical mineral soil, decreased the soil's P sorption capacity by 55% presumably because of the high soluble, inorganic P prevalent in this biochar (909mg P/kg of biochar). Phosphorus desorption from the fast pyrolysis biochar/soil mixture not only exhibited a common desorption curve but also buffered the soil solution at a value of ca. 0.2mg/L. This study shows the diversity in P chemistry that can be expected when biochar is a soil amendment and suggests the potential to develop biochars with properties to meet specific objectives.

Infrared spectroscopy has transformed soil property quantification by enabling low-cost, high-throughput analysis of soils, enabling mapping and monitoring of this non-renewable resource. However, less evaluated are newly emerging indicators of soil health. Furthermore, as soil spectral libraries expand in size, commonly employed linear models such as partial least squares regression (PLSR) may be challenged by the number and diversity of spectra. Artificial neural networks (ANN) are an emerging deep learning approach that can offer advantages in quantification of soil properties by utilizing non-linear relationships among spectra and soil components. We compared ANN versus PLSR models for predicting an increasingly used soil health indicator, permanganate oxidizable C (POXC), as well as more routinely predicted soil variables (e.g., clay, soil organic C [SOC]), across a gradient of soil organic matter furnished by a deforestation chronosequence in Kenya (n = 144). Candidate ANN architectures were first methodologically evaluated and described to identify best-practices for the application of ANN to soil spectroscopy. Predictions by the resulting ANN relative to PLSR were similar or slightly improved for routinely measured variables that represent soil organic matter (SOC, C:N) and physical properties (clay, silt, sand, bulk density). The accuracy of POXC predictions were similar for ANN (RMSE 102 mg kg−1) and PLSR (RMSE 106 mg kg−1). However, models drew on shared but also distinct wavenumbers, indicating differential use of information in soil infrared spectra by non-linear versus linear chemometric models. Even in relatively small spectral datasets of similar soil types expected to favor PLSR, ANN shows comparable predictive performance. To help guide future applications of ANN in soil spectroscopy, we propose a systematic procedure to select ANN model hyperparameters.

Biochar is the solid residue produced by the pyrolysis of any bio-organic material under low, or no, oxygen conditions and has generated considerable interest as a means to sequester carbon in, and improve the quality of, soils. However, the exact properties of biochar depend on its composition, which in turn depends on the composition of the starting material and the temperature and conditions under which the biochar is produced. Mid-infrared spectroscopy offers an excellent and rapid method for characterizing both the starting materials and the resulting biochar. Results using diffuse reflection infrared Fourier transform spectroscopy (DRIFTS) have shown that spectral changes can be easily correlated with the production temperature and that DRIFTS offers a rapid method for biochar characterization. It was demonstrated that as the temperature increases biochars become increasingly more aromatic and carbonaceous in nature. We also showed that biochars are spectrally very similar to kerogens and coals; therefore, the methods and knowledge developed from decades of studies on these materials should greatly improve our understanding of biochar composition and effects in soil. This work indicates that rapid characterization using DRIFTS can be used to predict the nature of biochar and to determine the production conditions needed to produce a so-called "Designer Biochar" which will have properties of benefit to soil quality as well as sequestering carbon.

The objective of this study was to evaluate whether near-infrared reflectance spectroscopy (NIRS) or mid-infrared reflectance spectroscopy (MIRS) could be used to determine the composition of algal turf scrubber samples. We assayed a set of algal turf scrubber (ATS) samples (n = 117) by NIRS, MIRS, and conventional means for ash, total sugar, mono-sugar, total N, and P content. A subset of these samples (n = 64) were assayed by conventional means, MIRS, and NIRS for total lipid and total fatty acid content. We developed calibrations using all the samples and a one-out cross-validation procedure under partial least-squares regression. This process was repeated using 75% of randomly selected samples to develop the calibration and the remaining samples as an independent test set. Results using the entire sample set demonstrated that NIRS and MIRS can accurately determine ash (r (2) = 0.994 and 0.995, respectively) and total N (r (2) = 0.787 and 0.820, respectively) content, but not phosphorus, total sugar, or mono-sugar content in ATS samples. Results using the 64 sample subset indicated that neither NIRS nor MIRS can accurately determine lipid or total fatty acid content in ATS samples.

The influence of soil aggregation as a means to protect soil organic carbon (SOC) from mineralization is unclear in very sandy soils. The dominant forest cover types in the Lower Coastal Plain of the US where sandy surface soils prevail are loblolly pine (Pinus taeda) and slash pine (Pinus elliottii var elliottii). The purpose of this study was to investigate the role that aggregation plays in C incorporation and sequestration in very sandy soils of the Lower Coastal Plain found under loblolly and slash pine ecosystems. Thirteen forest stands (seven loblolly pine; six slash pine) were used for this investigation. A sonic dismembrator was used to apply dispersive energy in order to destroy aggregates. The use of sonic energy was shown to be a valid tool for studying aggregates in sandy soils. The data showed that aggregates do not protect ASOC from mineralization in these very sandy soils. Loblolly pine surface mineral horizons accumulated 131% more TSOC than slash pine soil horizons. Slash pine soils had a 27% higher specific mineralization rate than loblolly pine soils; and Diffuse Reflectance Fourier Transform spectra (DRIFTS) showed that soils under loblolly pine were more aromatic than those under slash pine - and became more aromatic as mineralization proceeded. Due to their dominance in the Lower Coastal Plain of the US, pine ecosystems play an important role in the conversion of atmospheric CO2 into the TSOC pool. However, soil aggregation should not be considered a mechanism to protect SOC in these very sandy soils when modeling soil carbon dynamics, even though slash pine systems show a slightly greater capacity to develop aggregates. (C) 2011 Elsevier B.V. All rights reserved.

For many decades, near-infrared spectroscopy (NIRS) has been used to determine the composition of animal feedstuffs and grains. More recently, mid-infrared spectroscopy (mid-IR) has also been examined for similar determinations. These spectroscopic methods offer the potential for rapid and accurate determination of organic constituents, such as fiber components and protein, of forages, by-products, and grains at reduced cost and greatly increased speed (minutes instead of hours or days). Because they are nonchemical in nature, they result in a large reduction (90% or more) in the chemical wastes associated with standard chemical-based assay methods. The same components of interest for biofuel production (cellulose, hemicellulose, lignin, starch, protein, oil, etc.) are those that have already been determined by NIRS/mid-IR for evaluating grains and animal feedstuffs. Therefore, these techniques would appear to be a natural match for evaluating feedstocks for biofuels, and the literature shows that efforts in this direction are being successfully tested and instituted. For this discussion, an overview of where such efforts are and the potential for NIRS/mid-IR in producing biofuels will be covered. For example, while there are similarities between the needs of the biofuels industry and the analysis of animal feedstuffs, there are also both practical and technical differences between the two that will likely impact how NIRS/mid-IR is developed for biofuels. As an example, grain analysis for protein is performed on a large scale by government agencies such as the Canadian Grain Commission and U. S. Grain Inspection Service, while at least in the United States, animal feedstuff analysis is performed by state or independent laboratories for individual farmers. For biofuels, this might well result in most analysis being performed by the large corporations converting the feedstocks to biofuels, as opposed to the individual producer having analysis performed at an independent laboratory. Similarly for animal feedstuffs, measurements of fiber (neutral detergent fiber or NDF, acid detergent fiber or ADF, and lignin) and protein are carried out. These fiber measurements often consist of more than one type of fiber component with some being computed by difference (hemicellulose = NDF - ADF) and are empirical at best. Whether such empirical estimates will be sufficient for assessing biofuels or whether new spectroscopic methods for directly measuring the components of interest (cellulose, etc.) will need to be developed is a question to be answered when components other than starch for ethanol or oil for biodiesel become common.

There has been an increasing interest in the use of mid-infrared spectroscopy for the quantitative analysis of soils. Understanding the spectral bands can be beneficial both for understanding the basis of the quantitative analysis and soil composition and structure in general. Unfortunately, at least from the standpoint of the organic composition of soils, the spectra of most soils are dominated by the spectra of inorganic fractions such as clays, silica, carbonates, etc. One method used to accentuate the signature of the organic fraction has been to ash soil samples and then use spectral subtraction, in theory leaving only the spectral signature of the organic fraction. The objective of this work was to investigate exactly how accurate/practical this procedure is and is derived from recent comments from reviewers. Results examining silt, clays, silica, calcium carbonate, soils and materials such as cellulose and lignin show that, without considerable prior knowledge of a soil sample, spectral interpretation in most regions of the mid-infrared is highly interpretive/subjective at best. While the region for CH absorptions (3000–2800 cm− 1) is free of interferences, except for carbonates which can be removed by acidification, and the region between 1750–1600 cm− 1 can be interpreted despite the presence of strong silica bands, interpretation of the remaining regions of the mid-infrared are deemed very subjective. This is largely, but not entirely, due to the fact that many types of clay undergo extreme spectral changes from ashing and materials with seemingly similar formulas such as kaolinite (Al2Si2O5(OH)4) and pyrophyllite (Al2Si4O10(OH)2) change differently when ashed. Thus unless one knows the exact clay/mineral composition of the soil in question and the effects of ashing on said clay/mineral, accurately interpreting the ash subtracted spectra is nearly impossible. Also, even subtracting silica was found to be difficult due to changing specular effects even with KBr diluted samples. From a practical standpoint, one must conclude that the routine use of ash subtraction is not practical for soils as too much needs to be known to judge the results.

Near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) have been used for quantitative and/or qualitative analysis of a wide range of materials. The objective of this study was to investigate the potential of MIRS and NIRS for following the degradation of bio-based food utensils during composting. Polylactide (PLA)-based forks lost 34% of their initial mass and were reduced to small friable fragments after 7 weeks of composting. NIRS and MIRS spectra of forks that were incubated for 7 weeks were nearly identical to spectra of untreated forks. NIRS and MIRS were more useful in following the degradation of a starch/polypropylene (PP) polymer. Spectral results demonstrated that the starch component degraded during composting and that the PP component was recalcitrant. These results confirm that MIRS and NIRS are useful in determining the composition of biobased materials. However, the spectra did not provide useful information about the extent of PLA polymer degradation.

“Eureqa (pronounced ‘eureka’) is a software tool for detecting equations and hidden mathematical relationships in your data” made available by the Cornell Creative Machines Lab. ( http://creativemachines.cornell.edu/eureqfa ). Based on the use of evolutionary genetic programming, the program is capable of testing a wide variety of mathematical functions, and combinations thereof, to find relationships between data. Functions include the standard functions used in multi-linear regression (MLR; +, –, /, *, k) plus others, such as exponentials, roots, trigonometric etc. The objective of this investigation was to determine if this program might be useful in the investigation and development of spectroscopic calibrations. Two sets (173 or 241 samples) of forages (hays) and by-products (hulls, stalks etc.), some of which had been chemically treated with sodium chlorite to increase digestibility, were studied using Eureqa. Data for six analytes were available, but based on previous work using partial least squares regression, only crude protein, a well-determined analyte, and lignin, a much poorly determined analyte, were examined. Results indicated that standard spectral pre-treatments such as normalisation, mean centring, variance scaling, multiplicative scatter correction and derivatives might be beneficial in more rapidly obtaining the best calibrations, but were not necessary unless needed for data scaling. While overall results were comparable to those from partial least squares, but never as quite as good, several aspects of the program led to the conclusion that it is a wonderful exploration and learning tool for spectroscopy, even if one is not interested in developing MLR-based calibrations. As just one example, the results for all samples are displayed for each equation as developed and any of the best dozen or so can be displayed with a simple mouse click. Thus, it is easy to see how the developing equation effects the predicted versus actual fit, outliers etc. in nearly real time. Similarly, the many function fitting options (different error measures, both least- and non-least squares) allow one to explore and see how these affect both the final results and equation evolution. In conclusion, Eureqa is an exceedingly interesting and useful program, both for developing MLR-based calibrations and for studies of calibration development in general.

The biodegradability of three types of bioplastic pots was evaluated by measuring carbon dioxide produced from lab-scale compost reactors containing mixtures of pot fragments and compost inoculum held at 58 °C for 60 days. Biodegradability of pot type A (composed of 100% polylactic acid (PLA)) was very low (13 ± 3%) compared to literature values for other PLA materials. Near infrared spectroscopy (NIRS) results suggest that the PLA undergoes chemical structural changes during polymer extrusion and injection molding. These changes may be the basis of the low biodegradability value. Biodegradability of pot types B (containing 5% poultry feather, 80% PLA, 15% starch), and C (containing 50% poultry feather, 25% urea, 25% glycerol), were 53 ± 2% and 39 ± 3%, respectively. More than 85% of the total biodegradation of these bioplastics occurred within 38 days. NIRS results revealed that poultry feather was not degraded during composting.

Biochars result from the pyrolytic processing of organic materials. There is an increasing interest in the production and use of biochars from agricultural wastes, to sequester C in the soil and improve soil quality. Near-infrared spectroscopy, which has been used for decades to determine the composition of the varying agricultural materials used in making biochars would thus appear to be an obvious method for analysing these materials. However, previous work on charred cellulose, lignin, pine bark and wood showed that, while near-infrared spectroscopy using a Fourier transform spectrometer could be used for quantitative analysis, spectral interpretation beyond the bands found in the 4000 cm−1 to 5000 cm−1 (2500–2000 nm) region was difficult, to near impossible, due to the nature of the char spectra produced, the high degree of baseline curvature and the tendency to be very noisy. The objective of this work was to re-examine the question of near infrared (NIR) spectroscopy and chars using biochars made from wheat straw charred at various temperatures for 3 h. Results indicated that using spectrometer settings which work well for other materials such as soils or forages, especially with a Fourier Transform spectrometer, are not satisfactory for working with biochars or similar materials such as coals. Results obtained with a scanning monochromator, which also scanned a larger sample area (25× to 100× as large), were far superior to those obtained with a Fourier spectrometer with a praying mantis-style diffuse reflectance device and similar scanning conditions. Although previous work has shown that quantitative analysis can be carried out using NIR spectra collected on the same Fourier transform instrument, under the same conditions, these results and those show that qualitative analysis or spectral interpretation in the NIR require a different setup/procedure to be used. Collecting more scans, and at a lower resolution, greatly improved the spectra, although heating the sample could be a problem. Using a different DRIFTS device, which allowed a significantly larger sample area to be scanned, could also be useful, but was not tested.

Roots are an important contributor of recalcitrant organic carbon compounds for soil organic matter formation, but little is known about the composition of many species. There is a need for techniques capable of rapidly assessing significant, but often overlooked, carbon sinks such as roots. Diffuse reflectance mid-infrared spectroscopy (DRIFTS) has great potential for the analysis and characterization of plant root composition. The objectives of this research were to compare the DRIFTS spectra of roots of different species using whole root samples and root fiber fractions and to identify spectral features indicative of important root macromolecules in order to evaluate the potential of DRIFTS to determine root composition. A wide variety of roots from agronomic and horticultural crops, ornamental plants, and native plants were collected and analyzed by DRIFTS. Samples were ground in a cyclone grinder to pass a 20 mesh screen and scanned without KBr dilution from 400 to 4000cm−1 using KBr as the background. In addition, traditional fiber analysis of a subset of roots and DRIFTS analysis of the resulting fiber fractions (neutral detergent fiber, acid detergent fiber, hemicellulose, cellulose, lignin and wax) were utilized to identify spectral features associated with those fractions. Results indicate that the roots of the same species are similar despite differences in climate, soil and fertilization, while important differences were noted between roots of different species. Tree root lignins appeared to be similar to their above ground counterparts based on comparison with published data. Root lignins for all studied species varied by species. Spectral analysis was consistent with chemical fiber analysis composition data and revealed features that may be indicative of root suberin content. Further research is necessary to confirm that these features are related to suberin. Overall, the results of this research demonstrate the potential of DRIFTS for the characterization of plant root composition and as a tool to rapidly screen large numbers of samples for more effective utilization of more time-consuming analytical procedures.

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ∼10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n=315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2>0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3×3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.