Mid-infrared Diffuse Reflectance Spectroscopy Research Articles

Prediction of the concentration of chemical elements extracted by aqua regia in agricultural and grazing European soils using diffuse reflectance mid-infrared spectroscopy

The aim of this study was to develop partial least squares (PLS) models to predict the concentrations of 45 elements in soils extracted by the aqua regia (AR) method using diffuse reflectance Fourier Transform mid-infrared (MIR; 4000–500cm−1) spectroscopy. A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were selected. From the full soil set, 1000 samples were randomly selected to develop PLS models. Cross-validation was used for model training and the remaining 3130 samples used for model testing. According to the ratio of standard deviation to root mean square error (RPD) of the predictions, the elements were allocated into two main groups; Group 1 (successful calibrations, 30 elements), including those elements with RPD⩾1.5 (the coefficient of determination, R2, also provided): Ca (3.3, 0.91), Mg (2.5, 0.84), Al (2.4, 0.83), Fe (2.2, 0.79), Ga (2.1, 0.78), Co (2.1, 0.77), Ni (2.0, 0.77), Sc (2.1, 0.76), Ti (2.0, 0.75), Li (1.9, 0.73), Sr (1.9, 0.72), K (1.8, 0.70), Cr (1.8, 0.70), Th (1.8, 0.69), Be (1.7, 0.66), S (1.7, 0.66), B (1.6, 0.63), Rb (1.6, 0.62), V (1.6, 0.62), Y (1.6, 0.61), Zn (1.6, 0.60), Zr (1.6, 0.59), Nb (1.5, 0.58), Ce (1.5, 0.58), Cs (1.5, 0.58), Na (1.5, 0.57), In (1.5, 0.57), Bi (1.5, 0.56), Cu (1.5, 0.55), and Mn (1.5, 0.54); and Group 2 for 15 elements with RPD values lower than 1.5: As (1.4, 0.52), Ba (1.4, 0.52), La (1.4, 0.52), Tl (1.4, 0.51), P (1.4, 0.46), U (1.4, 0.45), Sb (1.3, 0.46), Mo (1.3, 0.43), Pb (1.3, 0.42), Se (1.3, 0.40), Cd (1.3, 0.40), Sn (1.3, 0.38), Hg (1.2, 0.33), Ag (1.2, 0.32) and W (1.1, 0.19). The success of the PLS models was found to be dependent on their relationships (directly or indirectly) with MIR-active soil components.

Mid-infrared attenuated total reflectance spectroscopy for soil carbon and particle size determination

In soil analysis mid-infrared spectroscopy (MIR) is not as widely used as visible and near infrared spectroscopy (VisNIR), mainly due to (1) the need for sample preparation and (2) strong absorption leading to spectral distortion and total absorption. In this study we used attenuated total reflectance (ATR) as a sample presentation technique to obtain MIR spectra of neat soil samples to overcome the two aforementioned drawbacks. A set of diverse soil samples (N=270) were scanned with a Fourier Transform IR spectrometer (4000 to 400cm−1) on its diamond ATR crystal. The objective was to investigate the usefulness of ATR spectra in determining clay, sand, organic and inorganic C using partial least squares regression. Results showed that inorganic C and clay can be predicted very well with ATR, with validation R2 around 0.94 and Ratio of Performance to Deviation (RPD) around 4.0. Sand can be predicted satisfactorily also, with R2=0.88 and RPD slightly lower than 3.0. Organic C is predicted with R2=0.77 and RPD>2.1. Compared to the results of VisNIR models, significantly higher accuracy was obtained for clay, sand, and inorganic C, and a slight improvement was obtained for organic C. In our opinion, MIR-ATR can be a promising and powerful tool for soil characterization. It combines the advantages of both VisNIR (minimum sample preparation and high analysis throughput) and diffuse reflectance MIR (better model performance). Finally, as evidenced in our “leave-whole-field-out” experiment, the models developed with ATR spectra performed better on dissimilar samples and might have a larger scope of application than VisNIR.

The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soils

The aim of this study was to develop partial least-squares (PLS) regression models using diffuse reflectance Fourier transform mid-infrared (MIR) spectroscopy for the prediction of the concentration of elements in soil determined by X-ray fluorescence (XRF). A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were used for the development of models to predict concentrations of Al, As, Ba, Ca, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, K, La, Mg, Mn, Na, Nb, Ni, P, Pb, Rb, Sc, Si, Sr, Th, Ti, V, Y, Zn and Zr in soil using MIR spectroscopy. The results were compared with those obtained where MIR models were developed with the same soils but using the concentration of elements extracted with aqua regia (AR).The PLS models were cross-validated against the experimental log-transformed XRF values of all the elements. The calibration models were derived from a set of 1000 randomly selected calibration samples. The rest of the samples (3130) were used as an independent validation set. According to the residual predictive deviation (RPD), predictions were classified as follows: “Good quality”, Ca (2.9), Mg (2.5), Al (2.3), Fe (2.2), Ga (2.2), Si (2.1), Na (2.0); “Indicator quality”, V (1.9), Ni (1.9), Sc (1.9), K (1.8), Ti (1.8), Rb (1.8), Zn (1.7), Co (1.7), Zr (1.6), Cr (1.6), Sr (1.6), Y (1.6), Nb (1.6), Ba (1.5), Mn (1.5), As (1.5), Ce (1.5); “Poor quality”, Cs (1.4), Th (1.4), P (1.4), Cu (1.4), Pb (1.3), La (1.2), Hf (1.1).Good agreement was observed between the RPD values obtained for the elements analysed in this study and those from the AR study. Despite the different elemental concentrations determined by the XRF method compared to the AR method, MIR spectroscopy was still capable of predicting elemental concentrations.

Discrimination of Radix Angelicae Sinensis According to Harvest Time and Geographical Origin with Midinfrared Diffuse Reflectance Spectroscopy and Chemometric Methods

ABSTRACT The combination of midinfrared (MIR) spectrum with chemometric methods provides an approach to study the Radix Angelicae sinensis (RAs) in relation to its geographical origin and harvest time. Random forests (RF) was applied to select the most effective wave-number variables from full MIR variables; as a result, three variables (3730 cm−1, 2310 cm−1, and 1050 cm−1) were selected for the discrimination of different RAs. The selected effective wave-number variables were put into linear discriminant analysis (LDA) and radial basis function neural networks (RBF-NNs) to establish the classification models. For the independent test set, the overall accuracy was 96.67%, 93.33%, and 96.67% for RF, LDA, and RBF-NNs models, respectively. These results show that MIR combined with chemometric methods might be a suitable way to classify RAs.

Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy

Accurate assessment of total carbon (Ct) content is important for fertility and nutrient management of soils, as well as for carbon sequestration studies. The non-destructive analysis of soils by diffuse reflectance spectroscopy (DRS) is a potential supplement or alternative to the traditional time-consuming and costly combustion method of Ct analysis, especially in spatial or temporal studies where sample numbers are large. This alternative technique has been utilized in several locations in the United States and elsewhere, but has not been tested on the unique and diverse tropical soils in Hawaii. This study investigated the feasibility of DRS for Ct prediction of Hawaiian agricultural soils by creating visible, near-infrared (VNIR) and mid-infrared (MIR) spectral libraries and developing chemometric models with partial least squares regression (PLSR) and random forests (RF) ensemble tree regression. The sample set contained 305 soils from across the five main Hawaiian Islands, representing 10 soil orders and over 100 soil series. The Ct of these samples measured by dry combustion ranged from <1% to 56%, greater than that of most previously published studies. The VNIR spectra of the soils commonly exhibited features associated with OH− and H2O, iron oxides, phyllosilicates, and organic molecules. The numerous features in the MIR spectra can be attributed to OH−, organic molecules, and a variety of silicate minerals, such as phyllosilicates and quartz. Excellent results were obtained from both PLSR models using VNIR and MIR spectra. The models had R2 values of 0.95/0.94 (VNIR/MIR), root mean squared error (RMSE) values of 2.80%/3.08%, residual prediction deviation (RPD) values of 4.25/3.91, and ratio of performance to inter-quartile distance (RPIQ) values of 3.66/3.38. The RF VNIR and MIR models were also of high quality, with R2=0.95/0.96 (VNIR/MIR), RMSE=2.82%/2.28%, RPD=4.27/5.28, and RPIQ=3.68/4.56. An evaluation of the different wavelength ranges and chemometric methods determined that for this sample set all were capable of robust predictions of Ct. These results indicate that DRS is an alternative technique for analysis of Ct in Hawaiian soils.

Pyrosequencing and mid-infrared spectroscopy reveal distinct aggregate stratification of soil bacterial communities and organic matter composition

This study integrated physical, chemical, and molecular techniques to assess relationships between soil bacterial community structures and the quantity and quality of soil organic carbon (SOC) at the soil microenvironment scale (e.g., within different aggregate size-fractions). To accomplish this goal, soil samples (0–5 cm) were collected from the Texas High Plains region under a variety of dryland and irrigated cropping systems. The soil was separated into macroaggregates, microaggregates, and silt + clay fractions that were analyzed for (1) bacterial diversity via pyrosequencing of the 16s rRNA gene and (2) SOC quantity and quality using a combustion method and mid-infrared diffuse reflectance spectroscopy (mid-IR), respectively. Results from pyrosequencing showed that each soil microenvironment supported a distinct bacterial community. Similarly, mid-IR data revealed distinct spectral features indicating that these fractions were also distinguished by organic and mineral composition. Macroaggregates showed relatively high abundance of Actinobacteria (excluding order Rubrobacteriales) and α- Proteobacteria and contained the most SOC. Microaggregates showed high relative abundance of Rubrobacteriales and the least amount of SOC. Predominance within the soil microenvironment and correlations along the mid-IR spectra were different between members of the order Rubrobacteriales compared with all other members of the Actinobacteria phyla, suggesting they have different ecological niches. Mid-IR results revealed microaggregates had greater absorbance in the 1370–1450 cm −1 region for phenolic and alkyl groups (possibly recalcitrant C). Silt + clay fractions were distinguished by Gemmatimonadetes and OP10 phyla, which positively correlated with spectral absorption in the1250–1150 cm −1 range (indicating both degradable and recalcitrant C forms). In contrast to general diversity index measurements, distributions of the more rare bacterial phyla (phyla representing <6% of the identified population) were more important for differentiating between communities in soil microenvironments. To our knowledge, this is the first study to investigate soil bacterial communities among soil aggregates using pyrosequenging and to associate these communities to specific soil C chemistries as indicated by mid-IR absorbance.

Mid-infrared diffuse reflectance spectroscopy for the rapid analysis of plant root composition

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 4000 cm − 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.

Assessment of estuarine sediment and sedimentary organic matter properties by infrared reflectance spectroscopy

The goal of this work was to evaluate the capability of mid-infrared diffuse reflectance (DRIFT) spectroscopy, coupled with multivariate chemometric analysis, to rapidly provide valuable information on sediment composition and organic geochemistry. A large (150) and heterogeneous set of estuarine sediments was analyzed in their natural matrix by DRIFT. Principal component analysis (PCA) of DRIFT spectra clearly discriminated sediments from distinct deposition events, such as landslides or floods, based on their higher carbonate contents. PCA also distinguished mostly terrigenous sediments, enriched in organic matter, aromatics and SiO 2, from mostly marine sediments, enriched in clays and carbonates. Partial least-squares (PLS) regression of the DRIFT spectra was used to predict various sediment properties, without the extractions or alterations required by traditional analytical methods. The relative errors in quantifying properties, as expressed by the relative root-mean-squared error (%RMSE), were ~ 6% for total humic substance and humin content, 7.6% for %C, 11.5% for atomic N:C ratio, and 13–14% for %N, %C as humic substances and %C as humin. The study revealed the importance of using a calibration sample set that is representative of the samples to be quantified. This calibration can then be used for the future and rapid analyses of unknown samples. Geochemical processes could also be inferred from band assignments of the loadings of the PLS models. These loadings indicated that nitrogen, humin (%C as humin), and aluminosilicate contents were correlated, providing independent evidence that nitrogen-rich compounds are important bonding agents between the organic and mineral fractions of the sediments. DRIFT-PCA and DRIFT-PLS represent valuable techniques for a rapid and cost-effective screening of sediment core characteristics and for an evaluation of sediment complex composition.

Sediment induced soil spatial variation in paddy fields of Northwest Vietnam

The aim of this study was to assess the impact of various sedimentation pathways (flooding, irrigation and runoff) on the spatial variability of soil fertility in rice paddy terraces in tropical mountainous regions of Northwest Vietnam. Topsoil samples were taken during two subsequent rice cropping seasons and analyzed using a combination of diffuse reflectance mid infrared spectroscopy and conventional lab analysis. A mixed model was used (i) to evaluate the spatial variability among and within paddy cascades before planting in function of field position to the main irrigation channel, and (ii) to assess the impact of various sediment deposition pathways on soil nutrients and textural changes. The topsoil taken before planting contained on average 1.75 ± 0.57 g 100 g − 1 soil organic carbon (SOC), 0.18 ± 0.06 g 100 g − 1 total nitrogen (TN) with silt being the dominating soil fraction (0.68 ± 0.11 g g − 1 ). Moderate sediment delivery of high quality through the irrigation system resulted in a significant enrichment in lower lying paddies following a linear trend for SOC (SOC (g 100 g − 1 ) = 1.4 + 0.02 Distance (m), R 2 = 0.31 − 0.62), total nitrogen (TN (g 100 g − 1 ) = 0.11 + 0.004 Distance (m), R 2 = 0.33 − 0.61) and a significant linear decrease in the sand fraction (sand (g g − 1) = 0.3 − 8 E − 04 Distance (m), R 2 = 0.28 − 0.48) with increasing distance from the irrigation channel along the cascade. Comparison of the samples taken before planting and after harvesting proved that the spatial variability in the topsoil was induced by sediment deposition resulting in a decrease of 0.11 g 100 g − 1 of SOC and 0.01 g 100 g − 1 of total N and an increase of 0.02 g g − 1 of the sand fraction in paddies close to the irrigation channel which received less nutrient rich sediment deposition. However, besides the effect of sediment rich irrigation water, direct sediment depositions originating from the highly eroded and unfertile uplands or deposited during flooding events (typhoons) strongly decreased soil fertility in the rice fields due to their low nutrient and high sand content. In conclusion, the alterations and maintenance of soil fertility of rice fields depended on the balance of the various sediment sources, i.e. quality and quantity, and is thus, strongly related to both upland management and extreme weather events and irrigation practices. These findings are relevant in the framework of site-specific fertilizer management by taking advantage of spatial variability in soil fertility along cascades of rice paddy terraces in tropical mountainous regions.

Regional transferability of mid-infrared diffuse reflectance spectroscopic prediction for soil chemical properties

Mid-infrared diffuse reflectance spectroscopy (MIR-DRS, 2500 to 25 000 nm) can provide rapid, cheap and relatively accurate predictions for a number of soil properties. This paper examines the ability of MIR-DRS to predict various soil chemical properties with emphasis on the elucidation of possible mechanisms of prediction. Three datasets from the states of Queensland, New South Wales, and Victoria, Australia were used in this study. MIR is able to predict fundamental soil properties related to soil matrix or solid constituents and soil solution concentrations which are in equilibrium with the solid phases. Basic soil organic and mineral constituents are well predicted: cation exchange capacity, clay content, organic and total C, and total N. Chemical properties that are related to the mineral and organic components can also be predicted, including: total K, total P, elemental Fe, Si, and Al. Soil pH appears to be related to the soil constituents from its relationship with the exchange sites. Due to the differences in the origins of the soil and in the laboratory measurement procedures, calibration functions from one region do not perform well on another one. Local calibration is preferred to avoid the measurement biases between laboratories, and the calibrated functions are more specific to the soil types in an area.

Robust Modelling of Soil Diffuse Reflectance Spectra by “Bagging-Partial Least Squares Regression”

Visible (vis), near infrared (NIR) and mid infrared (mid-IR) diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) are increasingly being used in the agricultural and environmental sciences as an efficient complement to conventional laboratory analysis. The DRS techniques are rapid, relatively cheap and more efficient for obtaining data than conventional analysis, especially when a large number of samples and analyses are required. A single spectrum may be used to predict various physical, chemical and biological soil properties. The robustness of PLSR models and their predictions may be improved by combining the implementation of PLSR with bootstrap aggregation or “bagging”. Bagging aims to reduce the variance of predictions by aggregating a number of models obtained in the course of re-sampling. The aim of this work was to test the implementation of bagging with PLSR (bagging-PLSR) using vis-NIR and mid-IR soil diffuse reflectance spectra to predict soil organic carbon (OC). Bagging-PLSR was shown to: (i) be more robust than PLSR alone, (ii) be less prone to over fitting and improve prediction accuracy and (iii) provide a measure of the uncertainty of the models and their predictions.

Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy (DRIFTS)

Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) and multivariate statistical analysis methods were used for the identification and classification of honey from different floral sources. The 82 honey samples (robinia, chestnut, citrus, polyfloral) were scanned by DRIFTS in the region 4000–600 cm −1 and also transformed in 1st and 2nd derivatives. Spectral data were analyzed by principal component analysis, general discriminant analysis and classification tree analysis. Classification accuracy near 100% was obtained by discriminant and classification tree analyses. Classification models were successfully validated with one-third leave out method and a classification of about 100% were achieved.

Effect of Water and Physical State on Near- and Mid-Infrared Partial Least Squares Calibrations for Multi-Component Carbohydrate Mixtures

The objective was to determine the effects of water and physical state on near- and mid-infrared calibrations for carbohydrate mixtures. Seventy-five mixtures of cellulose, starch, pectin, locust gum, glucose and xylitol were scanned in the near- and mid-infrared in three physical states (wet, simple mixtures of dry constituents and mixtures dried together). These final samples (mixtures dried together) were prepared by drying wet mixtures/solutions while under constant stirring to prevent separation of the insoluble polymers by settling, which occurs during normal oven drying. Final drying of the resulting solid mass was carried out in a vacuum oven at 60°C. The dried mass was then ground and scanned in the spectrometer. All dry samples were ground to a flour-like consistency using a A-10 Tekmar Analytical Mill to avoid effects due to gross differences in particle size. The wet samples were produced by adding sufficient water to result in samples of a putty like consistency. Spectra were taken on a Digilab FTS-65 Fourier transform spectrometer equipped with near- and mid-infrared benches by diffuse reflectance. Results demonstrated that the effects of water and physical state (matrix effects), seen previously with simple model systems, can be extrapolated to draw some general conclusions about real world calibrations, but that developing model systems to either predict the feasibility of developing a calibration or to determine the reason for the specific results of a calibration must be done with extreme care. For example, using simple mixtures of components to determine whether a calibration for the same components in a different physical condition (melted together, dried together etc.) might well lead to erroneous conclusions about its usefulness. Also, from the results, it would appear that factors (e.g. physical state) other than the mere presence of large amounts on water in samples can account for the decrease in calibration accuracy often seen in samples with high moisture contents as compared to their dried counterparts. Finally, the results seen using mid-infrared spectra indicate that mid-infrared diffuse reflectance spectroscopy might be feasible even with samples containing high percentages of water.

Mid-infrared diffuse reflectance spectroscopy of textiles containing finishing auxiliaries

Infrared spectroscopic methods have their place for a fast and contact-free qualitative and quantitative analysis of various samples and chemical substances such as found in many industrial processes. In this study, mid-infrared spectroscopy in combination with the diffuse reflectance (DR) technique has been used for quantification of the textile finishing auxiliaries that were applied for improving various fabric characteristics. The results from a quantitative determination of two exemplary auxiliaries applied onto cotton fabrics from different formulations are shown and limitations of the diffuse reflectance measurement technique are discussed.

DETERMINATION OF BIOLOGICAL MEASURES BY MID-INFRARED DIFFUSE REFLECTANCE SPECTROSCOPY IN SOILS WITHIN A LANDSCAPE

The objective of this work was to determine the effects of sample diversity on mid-infrared diffuse reflectance spectroscopic calibrations for biological measures in soils. A total of 136 soil samples representing a landscape within a small watershed were taken from a 20-ha field. The samples were obtained in a grid pattern from the first 10 centimeters. Soil samples were analyzed by mid-infrared diffuse reflectance spectroscopy and by conventional methods for total C and N, biomass C and N, acid and alkaline phosphatase, and arylsulfatase. Dried and ground samples, non-KBr diluted, were scanned from 4000 to 400 cm -1 (4 cm -1 resolution, 64 co-added scans) on a DigiLab FTS-60 Fourier transform spectrometer using a custom made sample transport that allowed a sample area of 50 by 2-mm to be scanned. Results using partial least squares regression showed that diffuse reflectance infrared fourier transform spectroscopy was capable of determining, to some degree, compositional parameters (total C and N) and biological activity as reflected by three enzymes. Based on calibration errors relative to the mean analyte values, the results for the measures of biological activity were as good as those found in previous studies using a less diverse (mineralogically) set of samples, although calibrations for biomass C and N were not feasible. In general, the results using mid-infrared spectra were also more accurate than those found using near-infrared spectra for the same samples.

Spectroscopic Analysis of Dried Manures. Near- versus Mid-Infrared Diffuse Reflectance Spectroscopy for the Analysis of Dried Dairy Manures

The objective was to examine and compare the ability of near- and mid-infrared diffuse reflectance spectroscopy to determine the composition of dried dairy manures. Samples ( n = 99) were dried at 60°C, ground and scanned in the near-infrared in a rotating sample cup on a Foss-NIRSystems model 6500 scanning monochromator, and in the mid-infrared on a Digilab FTS60 Fourier transform spectrometer using a custom-made sample transport device. While near-infrared calibrations for various measures of fibre composition (neutral detergent fibre etc.) were found to be possible, the results were generally less accurate than found for similar determinations in forages. Results based on mid-infrared spectra were sometimes better, and sometimes worse then near-infrared results, but again were not as accurate as found with forages. Overall, a clear advantage was not demonstrated for either region. Examination of the composition of the dried manures indicated that high ash contents (8–52% of dry matter) might be the cause for the decrease in calibration accuracy. However, a comparison of calibrations based on dry matter to those based on an ash-free basis showed no clear advantage for either means of computation. Accurate determination of minerals such as K or P was not possible using either spectral range (calibrations for K may be useful for gross segregation). In conclusion, while near- and mid-infrared calibrations appear feasible for determining the composition of dried dairy manures, better reference methods appear to be needed.

EFFECTS OF APODIZATION FUNCTION, ZERO FILLING, BACKGROUND SPECTRA, AND ABSORBANCE TRANSFORMATION ON MID-INFRARED CALIBRATIONS FOR FEED COMPOSITION

ABSTRACT Research has demonstrated that diffuse reflectance mid-infrared spectroscopy can, like near-infrared diffuse reflectance, be used to quantitatively determine the composition of ground samples of forages and soils without the need for KBr dilution. While it has been demonstrated that the accuracy of calibrations developed using mid-infrared spectra can be equal to or better than that achieved using near-infrared spectra, the influence of factors such as apodization function has not been determined. Results based on the spectra of 173 treated forage samples obtained using a DigiLab FTS-60 spectrometer have demonstrated that many parameters associated with mid-infrared spectra have little or no effect on partial least squares calibrations. Additional zero filling of spectra had little effect other than to increase the derivative gaps found to produce optimal calibrations, but calibrations developed using Kubelka-Munk transformed data, as opposed to absorbance data, were not as accurate. Choice of apodization function also had little effect, although slightly better results were found using triangular or weak Norton-Beer. Likewise, the frequency of taking a background spectrum did not seem to have any great effect on calibrations, although results were slightly better with hourly or daily acquisitions as opposed to one for each sample as is done in the near-infrared.

The rapid assessment of concentrations and solid phase associations of macro- and micronutrients in alkaline soils by mid-infrared diffuse reflectance spectroscopy

Chemical analysis is a crucial but often expensive and time consuming step in the characterisation of soils. Mid-infrared diffuse reflectance (MIR-DRIFT) spectroscopy coupled with partial least square (PLS) analysis was used to determine macro- and micronutrient concentrations of a range of alkaline soils from southern Australia. Solid phase associations of macro- and micronutrients were also investigated using the mineralogical information contained in the infrared spectra of soil samples. Results showed that MIR-PLS analysis is a powerful and rapid technique for the accurate prediction of more than 15 chemical properties from each soil sample spectrum. Correlation coefficients for MIR derived concentration versus laboratory determined values were greater than R2 = 0.80 for soil moisture, calcium carbonate concentration, total concentration of Mg, K, S, Fe, Al, Mn, Zn, Cu, and oxalate- extractable Al, Fe, Mn, and Si. In calcareous soils, sulfur was associated with carbonate and conversely Mg was more related to the clay concentration of soils. Micronutrients such as Fe, Zn, Mn, and Cu were positively associated with smectite/illite in the clay fraction and negatively with kaolinite. The potential use of these results in partitioning model to assess plant available micronutrients pools is discussed.

BINARY SYSTEMS OF NIFEDIPINE AND VARIOUS CYCLODEXTRINS IN THE SOLID STATE Thermal, FTIR, XRD studies

Nifedipine complexes with β-cyclodextrin (β-CD), γ-cyclodextrin (γ-CD), 2-hydroxypropyl-β-cyclodextrin (2HP-β-CD), randomly methylated-β-cyclodextrin (RM-β-CD) and heptakis(2,6-O-dimethyl)-β-cyclodextrin (DM-β-CD) have been prepared by both kneading and heating methods and their behaviour studied by differential scanning calorimetry (DSC), diffuse reflectance mid-infrared spectroscopy (FTIR) and X-ray diffractometry (XRD). DSC revealed the nifedipine melting endotherm with onset at approximately 171°C for the kneaded mixtures with β-CD, γ-CD and 2HP-β-CD, thus confirming the presence of nifedipine in the crystalline state, while some decrease in crystallinity was observed in the DM-β-CD kneaded mixture. With RM-β-CD, however, broadening and shifting of the nifedipine endotherm and reduction in its intensity suggested that the kneading could have produced an amorphous inclusion complex. These differing extents of interaction of nifedipine with the cyclodextrins were confirmed by FTIR and XRD studies.

Mid- versus near Infrared Spectroscopic Analysis of Diversely Treated Feedstuffs

The objectives of this study were to determine the advantages and disadvantages of using mid-infrared diffuse reflectance spectroscopy versus near infrared for the analysis of diversely-treated forages and by-products, and to compare the relative merits of calibration development using multi-linear regression versus partial least squares regression for such samples. Study samples (325) were randomly selected from 2808 possible sample–treatment combinations generated using 18 feedstuffs, 26 treatments (acids, bases, oxidising and reducing agents, heat etc.), and six levels of reagent for each treatment. Neutral detergent fibre and crude protein were determined on all samples and calculated on an organic dry matter basis. Neat samples were scanned (256 co-added scans) from 4000 to 400 cm−1 at 4 and 16 cm−1 resolutions on a Fourier transform infrared spectrometer equipped with a sample transport device, and in the near infrared (1100–2498 nm) using a scanning monochromator (64 co-added scans). Calibration results using 4 cm−1 resolution mid-infrared spectra were equal to those obtained using near infrared spectra. While the results for both spectral regions using partial least squares regression were an improvement over those using multi-linear regression, neither region produced calibrations equal to those obtained for less diversely treated or non-treated samples. The results obtained also showed that: (1) visual determination of likely compositional or spectral outliers is not obviously feasible using either near or mid-infrared spectra; (2) changes in spectra due to treatment can be much more easily attributed to compositional components using mid- as opposed to near infrared spectra; (3) examination of either mid-infrared PLS or PCR factors can be useful in determining compositional changes caused by treatments, while near infrared PLS and PCR factors appear to be dominated by moisture differences.