Mid-infrared Diffuse Reflectance Spectroscopy Research Articles

Combining mid-infrared spectroscopy and machine learning to estimate nutrient content in plant tissues of yerba mate (Ilex paraguariensis A. St. Hil.)

Mid-infrared diffuse reflectance spectroscopy with machine learning methods has been studied worldwide for estimating nutrient content in plant tissue of numerous plant species. Therefore, this study aimed to evaluate the best combination of spectral preprocessing and machine learning methods for estimating the concentration of chemical elements in leaf tissues of Ilex paraguariensis. From 2019 to 2021, 111 samples were collected from farms throughout Rio Grande do Sul State in southern Brazil. The total concentration of 11 nutrients (N, P, K, Ca, Mg, B, S, Mn, Fe, Cu, and Zn) and the spectra recorded in the mid-infrared range (2500– 18,000 nm) were obtained. The original spectrum underwent detrending, standard normal variate, and Savitzky-Golay derivative (SGD) spectral preprocessing. Multivariate partial least squares regression and support vector machine models were used to calibrate the spectra based on the nutrient concentrations obtained from the conventional analytical method. The models’ performance was evaluated using the coefficient of determination, the ratio of performance to the interquartile range, and the root mean square error of prediction. The support vector machine model combined with SGD preprocessing resulted in a more accurate prediction of the 11 elements studied in leaf tissue samples.

Diffuse reflectance mid-infrared spectroscopy is viable without fine milling

While diffuse reflectance Fourier transform mid-infrared spectroscopy (mid-DRIFTS) has been established as a viable low-cost surrogate for traditional soil analyses, the assumed need for fine milling of soil samples prior to analysis is constraining the commercial appeal of this technology. Here, we reevaluate this assumption using a set of 2380 soil samples collected across North American agricultural soils. Cross-validation indicated that the best preprocessing (standard normal variate) and model form (memory-based learning) resulted in very good and nearly identical predictions for the <2 mm preparation and fine-milled preparation of these soils for total organic carbon (TOC), clay, sand, pH and bulk density (BD). Application of larger models built from the USDA NRCS mid-DRIFTS library also resulted in minimal performance differences between the two sample preps. Lower predictive performance of the existing library was attributed to less-than-perfect spectral representativeness of the library. Regardless of model form, there was very little variability between replicates of the <2 mm prep, suggesting that the lack of fine milling did not lead to more heterogeneous subsamples. Additionally, there was no relationship between residual error and soil texture, implying these results should be robust across most soil types. Overall, in agreement with other recent findings, these results suggest that routine scanning of standard <2 mm preparation does not degrade predictive performance of mid-DRIFTS-based inference systems. With good standard operating procedures including quality control and traditional analysis on a small percent of samples, mid-DRIFTS can become a routine tool in commercial soil laboratories.

Using mid-infrared diffuse reflectance spectroscopy to investigate the dynamics of soil aggregate formation in a clay soil

Soil organic carbon is a crucial parameter for assessing soil quality, and it significantly contributes to soil aggregation. However, the influence of different organic carbon components and groups on the formation of soil water-stable aggregates has not been thoroughly investigated. To examine it, a clay soil amended with two different types of organic matter (OM) at four input levels for 84 days. The ratio of peak area (RPA) of the mid-infrared (MIR) spectra was used to investigate the dynamic of specific compounds and soil aggregation during the decomposition of OM. The results showed three important aspects. Firstly, the amount and nature of OM input strongly influenced the formation of organic compounds during incubation, as well as the degree of soil aggregation. Furthermore, we observed that the C groups identified by MIR spectroscopy were highly dynamic. Thus, the approach of using a band of soil MIR spectra to infer the stability of a C group can be misleading. Secondly, the low C/N of alfalfa stimulated more microbial activity with 1.1 to 2.3 times more cumulative respiration than high C/N barley straw. The rapid degradation of alfalfa immediately formed water-stable aggregates and subsequently, the macroaggregates were reorganized by microbial activities and physical disturbance. In contrast, the slow decomposition of barley straw built up aggregates gradually in a hierarchical manner and in proportion to the amount of OM added. Alfalfa amendment caused a significant increase in the mean weight diameter (MWD) and a strong correlation with the RPA in the range of 1747–1500 cm−1 (r = 0.78). While aggregation due to barley straw treatment led a stronger relationship between MWD and RPA of 3000–2800 cm−1 (r = 0.89). The results suggest MIR spectra can be used to understand the dynamics of OM that build stable aggregates.

Diffuse reflectance mid infra-red spectroscopy combined with machine learning algorithms can differentiate spectral signatures in shallow and deeper soils for the prediction of pH and organic matter content

Precision and sustainable agriculture requires information about soil pH and organic matter (OM) content at higher spatial and temporal scales than current agronomic sampling and analytical methods allow. This study examined the accuracy of spectral models using high throughput screening (HTS) in diffuse reflectance mode in mid Infra-red (MIR)/DRIFT combined with machine learning algorithms to predict soil pH(CaCl2) and %OM in shallow and deeper topsoils compared to laboratory methods. Models were developed from an archive of samples taken on a 4 km2 grid from the northern half of Ireland (Terra Soil project), which includes 18,859 samples (9,396 shallow + 9,463 deeper). The application of Cubist models showed that for different depths there are minor different spectral group associations with pH and %OM values. These differences resulted in a loss of accuracy in the extrapolation of the topsoil model to predict values from deeper topsoils or vice versa. Therefore we recommend the use of samples from both depths to build a calibration model.The proposed methodology was able to determine %OM and pH using a unique multivariate regression model for both depths, with RMSEP values of 1.12 and 0.89 %; RPIQ values of 42.34 and 38.48; R2val of 0.9989 and 0.9993 for %OM determinations in shallow and deeper topsoils, respectively. For pH determinations the RMSEP values obtained were 0.25 and 0.34; RPIQ values of 6.04 and 4.94; R2val 0.9385 and 0.8954. Both regression models are classified as excellent predictions models, yielding RPIQ values >4.05 for shallow and deeper topsoils. The results demonstrated the high potential of HTS-DRIFT combined with machine learning algorithms as a rapid, accurate, and cost-effective method to build large soil spectral libraries, displaying predicted results similar to two separate soil laboratory methods (pH and LOI).

The effect of excimer laser irradiation on mid-IR spectra of mineral mixtures for remote sensing

We examined the influence of micrometeorite bombardment simulated with an excimer laser, which irradiated mixtures of minerals with an energy density between 1.9 J/cm2 and 3.3 J/cm2. As starting material for the mixtures, we selected a natural olivine (Fo91) and a natural pyroxene (En87). Olivine and pyroxene are important rock-forming minerals of Earth's upper mantle and also occur on other terrestrial planetary bodies. The minerals were used to prepare a compositional range of mixtures that were pressed into pellets and examined especially by diffuse reflectance mid-infrared spectroscopy to identify changes caused by micrometeorite impacts as one tracer of space weathering on airless bodies such as Mercury or Moon. Spectral mid-IR measurements were carried out on untreated and treated samples in high vacuum. For comparison, the untreated samples are investigated in low vacuum under various phase angles. Especially the spectral variation between 7 μm–14 μm is of interest as the most important mid-IR features of rock-forming minerals as the Christiansen feature, Reststrahlen bands, and Transparency feature are located in this spectral region.As a result, we found that the mid-IR spectra of the powder pellets show signs of agglutination after irradiation. Here, olivine demonstrates a larger effect on irradiation than pyroxene, whereby pyroxene in mixtures can mask the presence of olivine in the mid-IR spectra after micrometeorite bombardment. We also observed a shift of the CF to longer wavelengths (shorter wavenumbers) after irradiation, and from high to low vacuum, a modification more prominent in olivine than in pyroxene. Therefore, this study shows that the identification and quantification of minerals in mixtures solely on the base of the CF location of the respective material can lead to an overestimation of modal abundances, which is important for the interpretation of data coming back from space missions, e.g., from the MERTIS experiment onboard BepiColombo.

Using Diffuse Reflectance Spectroscopy as a High Throughput Method for Quantifying Soil C and N and Their Distribution in Particulate and Mineral-Associated Organic Matter Fractions

Large-scale quantification of soil organic carbon (C) and nitrogen (N) stocks and their distribution between particulate (POM) and mineral-associated (MAOM) organic matter is deemed necessary to develop land management strategies to mitigate climate change and sustain food production. To this end, diffuse reflectance mid-infrared spectroscopy (MIR) coupled with partial least square (PLS) analysis has been proposed as a promising method because of its low labor and cost, high throughput and the potential to estimate multiple soil attributes. In this paper, we applied MIR spectroscopy to predict C and N content in bulk soils, and in POM and MAOM, as well as soil properties influencing soil C storage. A heterogeneous dataset including 349 topsoil samples were collected under different soil types, land use and climate conditions across the European Union and the United Kingdom. The samples were analyzed for various soil properties to determine the feasibility of developing MIR-based predictive calibrations. We obtained accurate predictions for total soil C and N content, MAOM C and N content, pH, clay, and sand (R2&amp;gt; 0.7; RPD&amp;gt;1.8). In contrast, POM C and N content were predicted with lower accuracies due to non-linear dependencies, suggesting the need for additional calibration across similar soils. Furthermore, the information provided by MIR spectroscopy was able to differentiate spectral bands and patterns across different C pools. The strength of the correlation between C pools, minerals, and C functional groups was land use-dependent, suggesting that the use of this approach for long-term soil C monitoring programs should use land-use specific calibrations.

Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

AbstractIn mid‐infrared diffuse reflectance (MIR) soil spectroscopy, grinding is one major step that can have pronounced effects on spectra and model calibrations. The reported literature on the effects of fine grinding on spectroscopic model performance have been inconsistent, likely in part because of limitations in sample set and model calibrations in previous studies. This study was focused on answering the question whether fine grinding is necessary for MIR spectroscopy in order to minimize model uncertainty. The main goal of this study was to compare model performance with and without fine grinding for eight soil properties using two different modeling techniques: partial least squares regression (PLS) and artificial neural networks (ANN). Approximately 500 soil samples were extracted from a large MIR spectral library in the United States to obtain spectra at non‐fine ground (NG, &lt;2 mm,) and fine‐ground (FG, &lt;0.18 mm,) states. Performance of calibration models built using subsets of the 500 FG and 500 NG spectra were compared with models built using the entire FG spectral library (n &gt; 40,000). All the model calibrations and validations were repeated 100 times to evaluate the uncertainty of the model performances. The results showed that PLS performed similar to ANN for the smaller dataset, but the best model performance was obtained with the FG full spectral library with ANN models. Predictions on the FG spectra always outperformed predictions on the NG spectra in terms of goodness‐of‐fit and variance of statistics. Overall, this study confirmed the importance of fine grinding to ensure the best MIR spectroscopic model performance.

Diffuse Reflectance Mid Infra-Red Spectroscopy Combined with Machine Learning Algorithms Can Differentiate Spectral Signatures in Shallow and Deeper Soils for the Prediction of pH and Organic Matter Content

Diffuse Reflectance Mid Infra-Red Spectroscopy Combined with Machine Learning Algorithms Can Differentiate Spectral Signatures in Shallow and Deeper Soils for the Prediction of pH and Organic Matter Content

Factors affecting in situ analysis of total petroleum hydrocarbons in contaminated soils by using a mid-infrared diffuse reflectance spectroscopy

The hand-held mid-infrared diffuse reflectance infrared Fourier transform (MIR-DRIFT) spectrometer was used to assess the applicability of on-site and real time monitoring of total petroleum hydrocarbons (TPH) in contaminated soils during site characterization and remediation. Field measurement devices (MIR-DRIFT and turbidimetric screening test kits) were used to analyze reference soils with concentration ranging from 713 to 54790 mg/kg and compared with the results by a gas chromatography/mass spectrometry method (GC/MS). In situ field measurement of 147 petroleum-contaminated soil samples from 11 contaminated sites was correlated with laboratory-determined soil TPH levels by GC/MS. The concentrations of TPH by MIR-DRIFT were significantly correlated to the concentrations of TPH by GC/MS. Detection of TPH by the MIR spectrometer was not affected by the weathering effects of diesel-contaminated soils. Soils contaminated by mixed fuels with high content of gasoline constituents may cause the potential interference in MIR measurement. In field practice, interference may be attributed to soil moisture, soil organic matter, and soil texture. Soil moisture below 5% is required to reduce variation of infrared beam reflected from high level of surface liquid. When measuring the contaminated soil with a high organic matter content, the results may be overestimated due to the possible effects of surface reflection and interference. Clay and partial silty clay soils were not suitable for MIR spectrometer detection due to a potential shielding effect to reduce the infrared radiation absorbed by TPH. Future research is warranted to reduce the variation caused by soil texture and heterogeneity in TPH prediction.

Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy

Assessing soil organic carbon (SOC) stocks is a methodological issue for SOC monitoring at regional scale but crucial for global agendas of SOC sequestration to mitigate climate change and reduce food insecurity. The ‘4 per mille Initiative: Soils for Food Security and Climate’, highlighted agricultural soil as a major lever for climate action and the need to assess SOC stock at different spatial and temporal scale. Infrared spectroscopy appeared as a promising tool to address this methodological issue.This work aimed to evaluate the potential of visible-near-infrared (VNIR) and mid-infrared (MIR) spectroscopic measurement methods to predict SOC stock and its variables (SOC content and bulk density) in tropical volcanic soils of ‘La Réunion’ island. The diversity of agricultural soils of ‘La Réunion’ was captured in the sample set (n = 95) with soil orders such as Andosols, Cambisols and Ferralsols. Partial least squares regressions (PLSR) with leave-one-out cross validation were used to build prediction models.With RPD higher than 2, the present study showed good prediction accuracy of models by MIR and VNIR spectroscopy of SOC content, bulk density and SOC stock for measurements in the laboratory or in the field. Accurate and direct SOC stock predictions were achieved on dried and sieved soil samples with MIR spectroscopy (RPD = 2.25; R2cv = 0.80; RMSEcv = 0.69 KgC m−2) and VNIR spectroscopy (RPD = 2.74; R2cv = 0.87; RMSEcv = 0.61 KgC m−2) but also directly on cores in the field with VNIR spectroscopy (RPD = 3.29; R2cv = 0.91; RMSEcv = 0.51 KgC m−2). This unexpected ability to predict directly SOC stocks by infrared spectroscopy can be partly explained by the high SOC content coupled with the large variation of SOC content and bulk density, providing a large range for those variables, and then a higher predictability.Yet these results questioned the underlying drivers of the bulk density and SOC stock, both being largely physical parameters supposed to be hardly predictable by infrared spectroscopy.Analyses of mean spectra and regression coefficients, combined with amorphous product (Alo, Feo, Sio, Alp, Fep) prediction models, demonstrated that these were detected in the spectra and were the drivers of SOC stock accurate predictions by infrared spectroscopy. Short-range ordered minerals, especially allophanes, appeared as proxy of the bulk density, and amorphous products such as Alp and Fep indicated the presence of organo-mineral complexes involved in SOC storage.Such promising results for SOC stock predictions from near- and mid-infrared volcanic soil spectra, confirmed that VNIR and MIR diffuse reflectance spectroscopy are an appropriate tool, rapid, low cost and non-destructive, to study SOC stocks in tropical volcanic soils. Upscaling of SOC stocks across the agricultural soils of the island is now just a step ahead, following external validation with additional data to validate the robustness of the prediction models.

Spectral responses to labile organic carbon fractions as useful soil quality indicators across a climatic gradient

Abstract Light fraction (LF) and permanganate-oxidizable C (POXC) demonstrate high reliability as indicators for monitoring soil functioning in response to changes in soil organic carbon (SOC). However, mechanisms affecting the amount and composition of labile fractions and their relationship with SOC content at regional scales have not been thoroughly studied. The aim of this study was to examine the spectral features associated with these labile organic matter fractions in samples collected from 75 sites under different soil types, land use and climatic conditions in Chile. Topsoil was analyzed for total C and N content, aggregate stability, and texture. Additionally, the spectral properties of LF material and whole soils were analyzed using diffuse reflectance mid-infrared spectroscopy (MidIR). Our results show that LF shared a similar spectral composition but with different band intensities across climatic regimes. LF spectra were associated with O-alkyl C in cool and rainy areas, whereas a relative accumulation of aromatic structures was found in warmer areas. Whole soils spectra showed that SOC, POXC and aggregability were related to the prevalence of aliphatic and polysaccharides compounds in colder areas. While in warm arid areas, the stabilization of aliphatic compounds was found to be related to clay minerals. Furthermore, we found that POXC and SOC content were closely related and changes in POXC were affected by variations in climate conditions. The understanding of spectral features linked to labile SOC fractions on at larger geographical scale will contribute to the development of sustainable land management options for the prevention of land degradation in the context of adaptation to climate change.

What are the effects of soil treatment procedures (sterilization by γ-irradiation and solvent-assisted spiking) on DDE bioaccumulation by earthworms?

Ionizing γ-irradiation and solvent-assisted spiking are frequently applied to eliminate microbial activity and to induce hydrophobic organic compounds (HOCs) into soil, respectively, when studying the accumulation of chemicals in terrestrial organisms. However, the side-effects that may arise from these treatments on soil-HOC interaction and, subsequently, the kinetics and extents of bioaccumulation are not thoroughly understood. To this end, the accumulation of 1,1-dichloro-2,2-bis(p-chlorophenyl)etylene (p,p’-DDE) by Eisenia andrei was studied in sterilized or unsterilized and freshly spiked (FS) or historically contaminated (HC) soils in parallel with an analysis of aliphatic and hydrophilic soil organic matter (SOM) moieties using mid-infrared diffuse reflectance spectroscopy (DRIFT-S). Irradiation did not impart significant changes on spectral SOM descriptors. In contrast, earthworm inhabitation increased the relative presence of aliphatic moieties to a greater extent than hydrophilic ones, reaching or exceeding pre-treatment levels. Overall, effects on SOM chemistry can be ranked as earthworms > spiking > irradiation. Corresponding changes at the bioaccumulation level were observed for the FS soil (i.e., a 27% reduction in bioaccumulation upon sterilization) but not for the HC soil. This implies that in contrast to the interactions between aged p,p’-DDE and sterilized HC soil, the interactions established between freshly added p,p’-DDE and sterilized FS soil were altered by γ-irradiation-induced secondary effects alone or in combination with earthworm inhabitation. Thus, although the soil treatment processes studied here should not drastically impact compound bioaccumulation, they should be considered in mechanistic studies where the qualitative and quantitative aspects of compound-soil (organic matter)-earthworm interactions are at the centre of attention.

The use of mid-infrared diffuse reflectance spectroscopy for acid sulfate soil analysis

Good management of sulfide minerals and sulfuric acid in Acid Sulfate Soils (ASS) requires cost-effective rapid analytical data for their characterisation. However, the determination of properties in ASS samples using traditional laboratory techniques is expensive and time consuming. Excessive delays in analysis risks sample changes from oxidation. Mid-infrared (MIR) spectroscopy with multivariate regression offers a quicker and cheaper surrogate. This manuscript reports the prediction of some of the following key soil parameters in ASS characterisation using benchtop (Perkin Elmer) and handheld (ExoScan) diffuse reflectance MIR Fourier transform (DRIFT) spectrometers: Total Organic Carbon (TOC), Titratable Actual Acidity (TAA), Extractable Sulfate Sulfur (ESS), Reduced Inorganic Sulfur (RIS), Retained Acidity (RA), Acid Neutralising Capacity (ANC), and Lime Calculation (LC). Three sets of representative ASS soil profiles, comprising 132 samples from hyposulfidic, hypersulfidic and sulfuric materials, and covering a wide range of environments in South Australia were scanned under laboratory conditions. These were combined with reference laboratory data in partial least squares regression (PLSR) calibration models. The calibrations were validated by leave-one-out cross validation, with a further test set available for validation. Predictions with coefficient of determination (R2) > 0.75, were obtained for TOC (0.95), TAA (0.88), RIS (0.86), LC (0.76) and ANC (0.76), but models for ESS (0.66) and RA (0.41) were less satisfactory. The handheld spectrometer performed similarly to the benchtop spectrometer in terms of PLSR prediction accuracies with the potential for in-field sampling. Results thus confirmed the possibility of using MIR spectroscopy for the rapid and cost-effective characterisation of ASS.

Evaluation of spectral pretreatments, spectral range, and regression methods for quantitative spectroscopic analysis of soil organic carbon composition

ABSTRACTAlthough there is an increasing interest in using infrared spectroscopy for the simple, rapid, and inexpensive prediction of soil organic carbon content, few studies have used this technique to measure organic carbon chemistry. In this paper, based on both near-infrared and mid-infrared diffuse reflectance spectroscopy, we compared the use of instrumentation, spectral pretreatment, and regression method for the prediction of three parameters related to organic carbon content, one related to isotopic composition, and five related to organic carbon chemistry. A total of 140 soil samples collected from seven oriental oak forest sites across East China were used as the data set for the calibration-validation procedure. Calibrations using sample set partitioning based on joint x-y distances method significantly outperformed those using Kennard-Stone method. Compared to models using linear method (i.e., partial least squares), those using non-linear regression method (i.e., support vector machines) greatly improved the prediction precision of the alkyl-to-O-alkyl ratio and performed slightly better for the other organic carbon chemical compositions. Instrumentation had a large effect as mid-infrared models had higher average prediction accuracies than near-infrared models. We finally proposed a model using second derivative preprocessing, joint x-y distances based sample set partitioning, mid-infrared spectra, and support vector machines regression to quantify organic carbon chemistry in this study. The results are helpful for the further study of soil composition measurement.

Pyrogenic carbon distribution in mineral topsoils of the northeastern United States

Due to its slow turnover rates in soil, pyrogenic carbon (PyC) is considered an important C pool and relevant to climate change processes. Therefore, the amounts of soil PyC were compared to environmental covariates over an area of 327,757km2 in the northeastern United States in order to understand the controls on PyC distribution over large areas. Topsoil (defined as the soil A horizon, after removal of any organic horizons) samples were collected at 165 field sites in a generalised random tessellation stratified design that corresponded to approximately 1 site per 1600km2 and PyC was estimated from diffuse reflectance mid-infrared spectroscopy measurements using a partial least-squares regression analysis in conjunction with a large database of PyC measurements based on a solid-state 13C nuclear magnetic resonance spectroscopy technique. Three spatial models were applied to the data in order to relate critical environmental covariates to the changes in spatial density of PyC over the landscape. Regional mean density estimates of PyC were 11.0gkg−1 (0.84Ggkm−2) for Ordinary Kriging, 25.8gkg−1 (12.2Ggkm−2) for Multivariate Linear Regression, and 26.1gkg−1 (12.4Ggkm−2) for Bayesian Regression Kriging. Akaike Information Criterion (AIC) indicated that the Multivariate Linear Regression model performed best (AIC=842.6; n=165) compared to Ordinary Kriging (AIC=982.4) and Bayesian Regression Kriging (AIC=979.2). Soil PyC concentrations correlated well with total soil sulphur (P<0.001; n=165), plant tissue lignin (P=0.003), and drainage class (P=0.008). This suggests the opportunity of including related environmental parameters in the spatial assessment of PyC in soils. Better estimates of the contribution of PyC to the global carbon cycle will thus also require more accurate assessments of these covariates.

Cell wall compositional changes during incubation of plant roots measured by mid-infrared diffuse reflectance spectroscopy and fiber analysis

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.

GEMAS: prediction of solid-solution phase partitioning coefficients (Kd) for oxoanions and boric acid in soils using mid-infrared diffuse reflectance spectroscopy.

The authors' aim was to develop rapid and inexpensive regression models for the prediction of partitioning coefficients (Kd), defined as the ratio of the total or surface-bound metal/metalloid concentration of the solid phase to the total concentration in the solution phase. Values of Kd were measured for boric acid (B[OH]3(0)) and selected added soluble oxoanions: molybdate (MoO4(2-)), antimonate (Sb[OH](6-)), selenate (SeO4(2-)), tellurate (TeO4(2-)) and vanadate (VO4(3-)). Models were developed using approximately 500 spectrally representative soils of the Geochemical Mapping of Agricultural Soils of Europe (GEMAS) program. These calibration soils represented the major properties of the entire 4813 soils of the GEMAS project. Multiple linear regression (MLR) from soil properties, partial least-squares regression (PLSR) using mid-infrared diffuse reflectance Fourier-transformed (DRIFT) spectra, and models using DRIFT spectra plus analytical pH values (DRIFT + pH), were compared with predicted log K(d + 1) values. Apart from selenate (R(2) = 0.43), the DRIFT + pH calibrations resulted in marginally better models to predict log K(d + 1) values (R(2) = 0.62-0.79), compared with those from PSLR-DRIFT (R(2) = 0.61-0.72) and MLR (R(2) = 0.54-0.79). The DRIFT + pH calibrations were applied to the prediction of log K(d + 1) values in the remaining 4313 soils. An example map of predicted log K(d + 1) values for added soluble MoO4(2-) in soils across Europe is presented. The DRIFT + pH PLSR models provided a rapid and inexpensive tool to assess the risk of mobility and potential availability of boric acid and selected oxoanions in European soils. For these models to be used in the prediction of log K(d + 1) values in soils globally, additional research will be needed to determine if soil variability is accounted on the calibration.

How does grinding affect the mid-infrared spectra of soil and their multivariate calibrations to texture and organic carbon?

Mid-infrared (mid-IR) diffuse reflectance spectroscopy can be used to effectively analyse soil, but the preparation of soil samples by grinding is time consuming. Soil samples are usually finely ground to a particle size of less than 0.250 mm because the spectrometer’s beam aperture is approximately 1–2 mm in diameter. Larger particles can generate specular reflections and spectra that do not adequately represent the soil sample. Grinding soil to small particle sizes enables the diffuse reflectance of light and more representative sample measurements. Here, we report on research that investigates the effect that grinding to different particle sizes have on soil mid-IR spectra. Our aims were to compare the effect of grinding soil to different particle sizes (2.000 mm, 1.000 mm, 0.500 mm, 0.250 mm and 0.106 mm) on the frequencies of mid-IR spectra, and compare the effect of these particle sizes on the accuracy of spectroscopic calibrations to predict organic carbon, sand, silt and clay contents. Using the Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) National visible–near infrared database, we selected 227 soil samples from the National Soil Archive for our experiments, and designed an experiment whereby each soil sample was ground in triplicate to the different particle sizes. These ground samples were measured using an FT-IR spectrometer with a spectral range of 4000–600 cm–1. Grinding to particle sizes that are ≤2.000 mm reduces subsample variability. Smaller particle sizes produce finer and sharper absorption features, which are related to organic carbon, and clay and sand mineralogies. Generally, better predictions for clay, sand and soil organic carbon contents were achieved using soil that is more finely ground, but there were no statistically significant differences between predictions that use soil ground to 1 mm, 0.5 mm, 0.25 mm. Grinding did not affect predictions of silt content. Recommendations on how much grinding is required for mid-IR analysis should also consider the time, cost and effort needed to prepare the soil samples as well as the purpose of the analysis.

GEMAS: prediction of solid-solution partitioning coefficients (Kd) for cationic metals in soils using mid-infrared diffuse reflectance spectroscopy.

Partial least squares regression (PLSR) models, using mid-infrared (MIR) diffuse reflectance Fourier-transformed (DRIFT) spectra, were used to predict distribution coefficient (Kd) values for selected added soluble metal cations (Ag(+), Co(2+), Cu(2+), Mn(2+), Ni(2+), Pb(2+), Sn(4+), and Zn(2+)) in 4813 soils of the Geochemical Mapping of Agricultural Soils (GEMAS) program. For the development of the PLSR models, approximately 500 representative soils were selected based on the spectra, and Kd values were determined using a single-point soluble metal or radioactive isotope spike. The optimum models, using a combination of MIR-DRIFT spectra and soil pH, resulted in good predictions for log Kd+1 for Co, Mn, Ni, Pb, and Zn (R(2) ≥ 0.83) but poor predictions for Ag, Cu, and Sn (R(2) < 0.50). These models were applied to the prediction of log Kd+1 values in the remaining 4313 unknown soils. The PLSR models provide a rapid and inexpensive tool to assess the mobility and potential availability of selected metallic cations in European soils. Further model development and validation will be needed to enable the prediction of log K(d+1) values in soils worldwide with different soil types and properties not covered in the existing model.

Chemometric quality inspection control of pyrantel pamoate, febantel and praziquantel in veterinary tablets by mid infrared spectroscopy

This paper describes the development and validation of a new multivariate calibration method based on diffuse reflectance mid infrared spectroscopy for direct and simultaneous determination of three veterinary pharmaceutical drugs, pyrantel pamoate, praziquantel and febantel, in commercial tablets. The best synergy interval partial least squares (siPLS) model was obtained by selecting three spectral regions, 3715–3150, 2865–2583, and 2298–1733cm−1, preprocessed by first derivative and Savitzky–Golay smoothing followed by mean centering. This model was built with five latent variables and provided root mean square errors of prediction (RMSEP) equal or lower than 0.69mg per 100mg of powder for the three analytes. The method was validated according the appropriate regulations through the estimate of figures of merit, such as trueness, precision, linearity, analytical sensitivity, bias and residual prediction deviation (RPD). Then, it was applied to three different veterinary pharmaceutical formulations found in the Brazilian market, in a situation of multi-product calibration, since the excipient composition of these commercial products, which was not known a priori, was modeled by an experimental design that scanned the likely content range of the possible constituents. The results were verified with high performance liquid chromatography with diode array detection (HPLC–DAD) and high performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) and were in agreement with the predicted values at 95% confidence level. The developed method presented the advantages of being simple, rapid, solvent free, and about ten times faster than the HPLC ones.