Mid-infrared Reflectance Spectroscopy Research Articles

Mid-infrared reflectance spectroscopy as a tool for forage feed composition prediction

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, in the Mid-infrared (MIR) region, has been evaluated for the prediction of chemical components in forage feeds using a modified Partial Least Squares Regression (PLSR) model. Regression regression models have been developed that predict the chemical composition from a MIR spectrum of a given forage feed sample. Data collection was carried out on 140 herbage samples consisting of 84 ryegrass - white clover samples and 56 herb mix samples containing different combinations of chicory, plantain, white clover and red clover. Several spectral data pre-treatments were explored, the best of which combined Standard Normal Variant scaling (SNV) with a first-order Savitzky-Golay (SG) spectral derivative and smoothing filter. Several of the resulting models illustrated high quality predictions (for hemicellulose, 156. 9 g / kg with a standard error of prediction (SEPc) 19.8 g / kg, R2 = 0.92, Relative Performance Deviation (RPD) = 3.54; for neutral detergent fibre, 382.8 g / kg with SEPc = 43.5 g / kg, R2 = 0.86, RPD = 2.60), at least on par with, or superior to, current near-infrared (NIR) methods. The SNV and SG pre-treatment almost completely reduces the contribution of strong water-based signals to the regression model, allowing the possibility of in situ prediction of forage feed composition with minimal sample preparation. ATR-FTIR spectrometers are available in a hand-held form, and the results of this research suggest that in situ forage quality analysis could be performed using MIR reflectance spectroscopy.

Dynamical anisotropic response of black phosphorus under magnetic field

Black phosphorus (BP) has emerged as a promising material candidate for next generation electronic and optoelectronic devices due to its high mobility, tunable band gap and highly anisotropic properties. In this work, polarization resolved ultrafast mid-infrared transient reflection spectroscopy measurements are performed to study the dynamical anisotropic optical properties of BP under magnetic fields up to 9 T. The relaxation dynamics of photoexcited carrier is found to be insensitive to the applied magnetic field due to the broadening of the Landau levels and large effective mass of carriers. While the anisotropic optical response of BP decreases with increasing magnetic field, its enhancement due to the excitation of hot carriers is similar to that without magnetic field. These experimental results can be well interpreted by the magneto-optical conductivity of the Landau levels of BP thin film, based on an effective k · p Hamiltonian and linear response theory. These findings suggest attractive possibilities of multi-dimensional control of anisotropic response (AR) of BP with light, electric and magnetic field, which further introduces BP to the fantastic magnetic field sensitive applications.

Could predicting fatty acid profile by mid-infrared reflectance spectroscopy be used as a method to increase the value added by milk production chains?

The aims of this work were (1) to develop prediction equations from mid-infrared spectroscopy (MIRS) to establish a detailed fatty acid (FA) composition of milk; (2) to propose a milk FA index, utilizing MIRS-developed equations, in which the precision of the FA-prediction equations is taken into account to increase the value of milk; and (3) to show application examples. A total of 651 bulk cow milk samples were collected from 245 commercial farms in northwest Italy. The results of the 651 gas chromatography analyses were used to establish (421 samples) and to validate (230 samples) the outcomes of the FA composition prediction that had been obtained by MIRS. A class-based approach, in which the obtained MIRS equations were used, was proposed to define a milk classification. The method provides a numerical index [milk FA index (MFAI)] that allows a premium price to be quantified to increase the value of a favorable FA profile of milk. Ten FA were selected to calculate MFAI, according to their relevance for human health and potential cheese sensory properties, and animal welfare and environmental sustainability were also considered. These factors were selected as dimensions of MFAI. A statistical analysis and expert judgment aggregation were performed on the selected FA by weighting the FA and normalizing the dimensions to reduce redundancy. A class approach was applied, using the precision of the MIRS equations to establish the classes. The median FA concentration of the data set was set as a reference value of class 0. The width, number, and limits of classes above and below the median were calculated using the 95% confidence level of the standard error of prediction, corrected with the bias of each FA. A progressive number and a positive or negative sign were assigned to each FA class above or below the median according to their role in the above mentioned dimensions. The sum of the numbers of each class, associated with its sign for each FA, was used to generate MFAI. The MFAI was applied to dairy farms characterized by different feeding strategies, all of which deliver milk to a commercial dairy plant. The MFAI values ranged from 0.7 to 4.2, and large variations, which depended on the cows' diet and forage quality, were observed for each feeding system. The proposed method has been found to be flexible and adaptable to several contexts on both intensive and extensive dairy farms.

Spectral analysis of Deccan intrabasaltic bole beds: Implications for the formation and alteration of phyllosilicates on Mars

To fully understand phyllosilicates on Mars, it is beneficial to study analog deposits on Earth. One attractive candidate for martian phyllosilicates is the intrabasaltic bole beds (palaeosols) from the Deccan Volcanic Province of India. Eleven samples from the upper-layer red Deccan bole beds and underlying yellow and green Deccan bole beds were analyzed by X-ray diffraction (XRD), near-infrared (1.0–2.5µm) and mid-infrared (5–15µm) reflectance spectroscopy. Analysis of the bole beds indicated that the red boles are composed of a mixture of montmorillonite and hematite, yellow boles contain vermiculite and minor montmorillonite and green boles are composed mainly of nontronite (smectite) and celadonite (mica). While the bole beds are all chemically similar to each other and to the underlying basalt from which they were weathered, they are mineralogically different. This suggests transformation from one mineral to the next without ion transfer or loss which could be indicative of a limited-water environment. In fact, celadonite can transform into smectites (such as montmorillonite), often with vermiculite as an intermediate step. This not only explains the stratigraphy and mineralogy of the Deccan bole beds but may also explain the layered phyllosilicates identified in various, global locations on Mars. The transition observed in the Deccan bole beds suggests an evolution of the alteration process from deuteric alteration to low-temperature weathering, likely due to changes in temperature, in a closed system (no significant ion transfer) as evidenced by the minerals’ similar chemistry. Thus, the Deccan bole beds are a good analog for the phyllosilicates layers on Mars and by studying the chemistry, mineralogy and spectral properties of the Deccan bole beds, we can link their formation and alteration processes to those of martian phyllosilicates. This will provide a clearer understanding of the environmental conditions on Mars at the time of the phyllosilicates’ formation and hence during Mars’ earliest history.

Estimating soil properties with mid-infrared spectroscopy

The mid-infrared technique (MIR) can be used to identify and estimate soil properties with high accuracy. The aim of this study was to evaluate the potential of mid-infrared reflectance spectroscopy (MIR) for the estimation of chemical properties of soils as well as the application of this technique in obtaining digital maps. In this study, 249 soil samples from two orders, Andisols and Oxisols, were analyzed. The results obtained in the analysis of the curves verified that the greater number of attributes was reflected in the spectral region of 400 and 850cm-1. The Andisols stood out due to the results in the calibration of the models, which were better than those of the Oxisols. The spectral responses were similar in both soils, but with different levels of reflectivity. This difference was more notable in the Andisols, where the spectral peaks were lower, a fact attributable to the compounds of the organic matter that tended to obscure the soil, absorbing infrared light. The results demonstrated that the mid-infrared reflectance spectroscopy MIR allowed for the processing of a large number of samples, where information about various parameters was obtained in a single spectrum. The organic carbon was the attribute with the best prediction. Similarly, the semivariogram models and contour maps obtained from the spectral data models showed high similarity to those obtained from the laboratory measurements for those properties, where the spectral models were representative.

Studying the Physical Protection of Soil Carbon with Quantitative Infrared Spectroscopy

Near infrared (NIR) and mid-infrared (mid-IR) reflectance spectroscopy are time- and cost-effective tools for characterising soil organic carbon (SOC). Here they were used for quantifying (i) carbon (C) dioxide (CO2) emission from soil samples crushed to 2 mm and 0.2 mm, at 18°C and 28°C; (ii) physical C protection, calculated as the difference between CO2 emissions from 0.2 mm and 2 mm crushed soil at a given temperature; and (iii) the temperature vulnerability of this protection, calculated as the difference between C protection at 18°C and 28°C. This was done for 97 topsoil samples from Tunisia, mostly calcareous, which were incubated for 21 days. Soil CO2 emission increased with temperature and fine crushing. However, C protection in 0.2–2 mm aggregates had little effect on the temperature vulnerability of CO2 emission, possibly due to preferential SOC protection in smaller aggregates. In general, NIR spectroscopy, and to a lesser extent mid-IR spectroscopy, yielded accurate predictions of soil CO2 emission (0.60 ≤ R2 ≤ 0.91), and acceptable predictions of C protection at the beginning of incubation (0.52 ≤ R2 ≤ 0.81) but not over the whole 21 day period ( R2 ≤ 0.59). For CO2 emission, prediction error was the same order of magnitude as, and sometimes similar to, the uncertainty of conventional determination, indicating that a noticeable proportion of the former could be attributed to the latter. The temperature vulnerability of C protection could not be modelled correctly ( R2 ≤ 0.11), due to error propagation. The prediction of SOC was better with NIR spectroscopy and that of soil inorganic C (SIC) was very accurate ( R2 ≥ 0.94), especially with mid-IR spectroscopy. Soil CO2 emission, C protection and its vulnerability were best predicted with NIR spectra, those of 0.2 mm samples especially. Mid-IR spectroscopy of 2 mm samples yielded the worst predictions in general. NIR spectroscopy prediction models suggested that CO2 emission and C protection depended (i) on aliphatic compounds (i.e. labile substrates), dominantly at 18°C; (ii) on amides or proteins (i.e. microbial biomass), markedly at 28°C; and (iii) negatively, on organohalogens and aromatic amines (i.e. pesticides). Models using mid-IR spectra showed a negative influence of carbonates on CO2 emission, suggesting they did not contribute to soil CO2 emission and might form during incubation. They also suggested that CO2 emission and C protection related to carboxylic acids, saturated aliphatic ones especially.

Quantification of SOC and Clay Content Using Visible Near-Infrared Reflectance–Mid-Infrared Reflectance Spectroscopy With Jack-Knifing Partial Least Squares Regression

A total of 125 soil samples were collected from a Danish field varying in soil texture from sandy to loamy. Visible near-infrared reflectance (Vis-NIR) and mid-infrared reflectance (MIR) spectroscopy combined with chemometric methods were used to predict soil organic carbon (SOC) and clay contents. The main objective of this study was to find the best model for predicting SOC and clay content in the sampled field using Vis-NIR, MIR, and the combination of Vis-NIR and MIR and using different model development techniques. The secondary objectives were (i) to use iterations of calculation to find the optimal number of replicates for MIR measurements based on the root mean square error of cross validation (RMSECV) and (ii) to apply partial least squares regression in combination with jack-knifing (JK) to identify the most important part of spectral variables and the best model for predicting SOC and clay content. The study showed that with repeated MIR measurements it was possible to improve RMSECV by 20%. The optimal number of repeated MIR measurements was between 3 and 4 for SOC and clay content. Comparing all the prediction results, the combination of MIR and Vis-NIR with the partial least squares regression–JK technique resulted in the lowest prediction errors (RMSECVsoc of 0.35% and RMSECVclay of 1.05%). The average uncertainties of laboratory measurements were 0.39% and 1.86% for SOC and clay contents, respectively. All models had acceptable and—to a large extent—comparable margins of error. Partial least squares regression with JK simplified and enhanced the interpretation of the developed models because of a reduction in the number of variables used in the models.

Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues

Visible near-infrared (Vis-NIR) reflection spectroscopy and mid-infrared (mid-IR) reflection spectroscopy are cost- and time-effective and environmentally friendly techniques that could be alternatives to conventional soil analysis methods. Successful determination of spectrally active soil components, including soil organic matter (SOM), depends on the selection of suitable pretreatment and multivariate calibration techniques. The objective of the present review is to critically examine the suitability of Vis-NIR (350-2500 nm) and mid-IR (4000-400 cm(-1)) spectroscopy as a tool for SOM quantity and quality determination. Particular attention is paid to different pretreatment and calibration procedures and methods, and their ability to predict SOM content from Vis-NIR and mid-IR data is discussed. We then review the most recent research using spectroscopy in different calibration scales (local, regional, or global). Finally, accuracy and robustness, as well as uncertainty in Vis-NIR and mid-IR spectroscopy, are considered. We conclude that spectroscopy, especially the mid-IR technique in association with Savitzky-Golay smoothing and derivatization and the least squares support vector machine (LS-SVM) algorithm, can be useful in determining SOM quantity and quality. Future research conducted for the standardization of protocols and soil conditions will allow more accurate and reliable results on a global and international scale.

The mineral chemistry, near-infrared, and mid-infrared reflectance spectroscopy of phengite from the Olympic Dam IOCG deposit, South Australia

Phengite is the main potassic dioctahedral mica identified at the Olympic Dam iron oxide–copper–gold (IOCG) deposit, South Australia, where its mineral chemistry is quite variable. These differences can be explained by contrasting degrees of hydrothermal alteration. In the heavily-sericitized, ore-bearing rocks, the phengites display a lower-Si content, a higher-Al content, and a lower Mg-number than the phengites from the weakly-sericitized alteration halo that surrounds the deposit. Variations are also observed in the near- and mid-infrared reflectance spectra collected from phengite-bearing rocks. In the near-infrared, high-Al phengite produces a spectral absorption feature at 2.206μm, and this feature is displaced to 2.213μm for low-Al phengite. In the mid-infrared, high-Al phengite produces a strong reflectance peak at 9.59μm, whereas this peak is observed at 9.57μm in the spectra from low-Al phengite. Additional peaks were also identified at 10.98, 12.22, and 13.33μm. These were most intense in the spectra from high-Al phengite. A drill core profile was produced using the results of the spectral analysis that shows the change in phengite mineral chemistry and phengite abundance as a function of depth. In general, near- and mid-infrared reflectance spectroscopy can be used to characterize the aluminum content of potassic dioctahedral micas like phengite, and this information can be used to infer the degree of sericitic alteration that has occurred as a result of hydrothermal fluid flow.

Comparing near and Mid-Infrared Reflectance Spectroscopy for Determining Properties of Malagasy Soils, Using Global or LOCAL Calibration

Nowadays, near infrared (NIR) and mid-infrared (mid-IR) reflectance spectroscopy are recognised useful approaches for quantifying soil properties, cost and time effectively. The aim of this work was to compare predictions of soil carbon (C) and nitrogen (N) content, C/N ratio, substrate-induced respiration (SIR) and denitrifying enzyme activity (DEA) using NIR and mid-IR spectroscopy over a diverse set of 360 Malagasy topsoils. Partial least square regression was used for fitting NIR and mid-IR spectra to conventional data through procedures of calibration either global (one prediction model for all samples) or LOCAL (one prediction model per sample). Prediction accuracy was assessed according to validation ( r2), standard error of prediction ( SEP) in proportion to the mean and ratio of standard deviation to SEP ( RPD). Using both NIR and mid-IR spectroscopy, global calibration over the whole sample set yielded predictions that were excellent for C and N ( r2 > 0.9, SEP <20%, RPD ⩾ 3), good for C/N, acceptable for SIR, but poor for DEA. LOCAL calibration improved C/N and SIR predictions with both NIR and mid-IR spectroscopy, while DEA prediction became acceptable with NIR spectroscopy only. Additional improvement was achieved when LOCAL calibration was carried out over the fine-textured sub-set, especially for SIR ( r2 > 0.9, SEP < 20%, RPD > 3). In contrast, LOCAL calibration over the coarse-textured sub-set was clearly not useful for improving prediction accuracy. NIR outperformed mid-IR spectroscopy whatever the variable, the calibration procedure and the sample set (except for SIR over the coarse-textured sub-set, where both similar), suggesting its possible superiority for tropical soils.

Prediction of soil organic and inorganic carbon contents at a national scale (France) using mid‐infrared reflectance spectroscopy (MIRS)

This work aimed to evaluate the potential of mid‐infrared reflectance spectroscopy (MIRS) to predict soil organic and inorganic carbon contents with a 2086‐sample set representative of French topsoils (0–30 cm). Ground air‐dried samples collected regularly using a 16 × 16‐km grid were analysed for total (dry combustion) and inorganic (calcimeter) carbon; organic carbon was calculated by difference. Calibrations of MIR spectra with partial least square regressions were developed with 10–80% of the set and five random selections of samples. Comparisons between samples with contrasting organic or inorganic carbon content and regression coefficients of calibration equations both showed that organic carbon was firstly associated with a wide spectral region around 2500–3500 cm−1 (which was a reflection of its complex nature), and inorganic carbon with narrow spectral bands, especially around 2520 cm−1. Optimal calibrations for both organic and inorganic carbon were achieved by using 20% of the total set: predictions were not improved much by including more of the set and were less stable, probably because of atypical samples. At the 20% rate, organic carbon predictions over the validation set (80% of the total) yielded mean R2, standard error of prediction (SEP) and RPD (ratio of standard deviation to SEP) of 0.89, 6.7 g kg−1 and 3.0, respectively; inorganic carbon predictions yielded 0.97, 2.8 g kg−1 and 5.6, respectively. This seemed appropriate for large‐scale soil inventories and mapping studies but not for accurate carbon monitoring, possibly because carbonate soils were included. More work is needed on organic carbon calibrations for large‐scale soil libraries.

Near- and mid-infrared spectroscopic determination of algal composition

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.

Sample preparation and selection for qualitative and quantitative analyses of soil organic carbon with mid-infrared reflectance spectroscopy

Mid-infrared spectroscopy (MIRS) is a well-established analytical tool for qualitative and quantitative analysis of soil samples. However, effects of soil sample grinding procedures on the prediction accuracy of MIR models and on qualitative spectral information have not been well investigated and, in consequence, not standardized up to now. Further, the effects of soil sample selection on the accuracy of MIR prediction models has not been quantified yet. This study investigated these effects by using 180 well-characterized soil samples that were ground for different times (0, 2 or 4 minutes) and then used for MIR measurements. To study the impact of sample preparation, soil spectra were subjected to principal component analyses (PCA), multiple regression and partial least square (PLS) analysis. The results indicate that the prediction accuracy of MIR models for soil organic carbon (SOC) and pH and the qualitative spectral information were better overall for lightly ground (2 minutes) soil samples compared with intensively (4 minutes) or unground soil samples. Whereas the grinding procedure did not show any effect on spectra of clay minerals, spectral information for quartz and for SOC was modified. Even though it is difficult to recommend a global standardized soil sample grinding procedure for MIR measurements because of different mill types available within laboratories, we highly recommend using an internally standardized grinding procedure. Moreover, we show that neither land use nor soil sampling depth influences the prediction of the SOC content. However, sand and clay content substantially affect the score vectors used by the PLS algorithm to predict the SOC content. Thus, we recommend using soil samples similar in texture for more precise SOC calibration models for MIR spectroscopy.

Mid-Infrared Fiber-Optic Reflection Spectroscopy (FORS) Analysis of Artists' Alkyd Paints on Different Supports

Mid-infrared Fourier transform fiber-optic reflection spectroscopy (mid-IR FORS) is a noninvasive and flexible spectroscopic technique. It is ideal in the art conservation field because of its portability for on-site and in situ analysis of art objects, analyses that require delicate handling, or analyses of objects that cannot be sampled. This paper studies the applicability of mid-IR FORS for the characterization of commercial artists' alkyd paints cast on different supports. As predicted, the quality of the spectra and intensity of characteristic peaks varied according to reflectivity, roughness, and materials used in the supports. The presence of organic binder was best identified by its carbonyl peak (the most intense) and CH(2) stretching peaks; however, this was not sufficient to distinguish between oil and alkyd binders. The differentiation and identification of alkyds and oils must rely on the unique fingerprint peaks. However, in some cases, the fingerprint peaks were difficult to interpret because of strong absorptions caused by inorganic paint fillers, often present in modern paint formulations, resulting in anomalous dispersion and reststrahlen distortions.

Thermal alteration of nontronite and montmorillonite: Implications for the martian surface

To test the effects of meteorite impacts on martian phyllosilicate deposits, we heated two smectites (nontronite and montmorillonite) to temperatures ranging from 350 °C to 1150 °C for durations of 4–24 h in two different atmospheres, under air and a steady flow of CO 2. Samples were analyzed using X-ray diffraction (XRD) and near-infrared (NIR) and mid-infrared (MIR) reflectance spectroscopy. Interlayer water was lost after heating to temperatures of ∼400 °C. Between 400 °C and 700 °C, nontronite converted to an intermediary phase which conserved the XRD pattern of untreated nontronite with the exception of the 0 0 1 peak. Nanocrystalline high-temperature phases formed for both smectites at temperatures between 700 °C and 1000 °C. Finally, after being heated to temperatures above ∼1100 °C, the samples melted and recrystallized into secondary phases. Secondary high-temperature phases included sillimanite and cristobalite for both smectites plus hematite for nontronite. NIR and MIR reflectance spectra significantly evolved with increasing temperature. NIR spectra of smectites showed that 1.4 and 1.9 μm bands decrease in intensity and disappear above 700 °C. Similarly, the 2.2–2.3 μm metal–OH band showed a decrease in intensity as well as an overall shift towards lower wavelengths (for nontronite) due to the thermal resistance of the Al–OH bond compared to the Fe–OH bond. NIR spectra of montmorillonite showed a gradual increase in band depth up to temperatures between 500 °C and 600 °C, then decreased with increasing temperature. In the MIR spectra of samples heated to temperatures above ∼1100 °C, newly formed bands confirmed the secondary phases identified by XRD. Similarities between the NIR spectra of our heated samples and the phyllosilicates in some martian craters imply that these phyllosilicates were altered by the impact-generated heat and thus were not formed post-impact. In addition, NIR reflectance spectra provide a proxy for shock temperatures of smectites up to 700 °C while MIR is optimum for identification of high-temperature phases of smectites above 700 °C.

Non-invasive characterisation of binding media on painted glass magic lantern plates using mid-infrared fibre-optic reflectance spectroscopy

Abstract Painted glass magic lantern plates from the Museo Nazionale del Cinema, Torino (Italy), were studied using mid-infrared (mid-IR) fibre-optic reflectance spectroscopy (FORS), a non-invasive technique, to test its potential for the identification of the types of binding media used in the paints. Gum, oil and resin media were identified on the plates and the amounts of these media varied from place to place; the discovery of these media correlate well with the literature on magic lantern plate preparation and painting. The spectra collected are reported uncorrected and also with the fingerprint region corrected with the Kramers–Kronig correction, which corrects distorted peaks caused by specular reflections.

Does the Spectral Format Matter in Diffuse Reflection Spectroscopy?

Near-infrared, and more recently, mid-infrared diffuse reflection spectroscopy (more commonly and erroneously called reflectance spectroscopy) have come to be extensively used to determine the composition of products ranging from forages and drugs to soils. In these methods, spectra are generally collected as reflectance or R and transformed to log (1/reflectance). However, some near-infrared researchers do not transform the data, but use the data directly as reflectance. As it is generally held that procedures such as partial least squares regression do not work well with nonlinear data and the log (1/reflectance) transformation is held to be a best effort at linearization for near-infrared diffuse reflection spectral data, the question arises as to why then does not everyone transform the data? The objective of this work was to investigate this question using near-infrared and mid-infrared spectra in various formats. Calibrations were developed using spectral data from forages in several formats: reflectance, log (1/reflectance), non-background corrected single beam spectra, interferograms, and Kubelka-Munk transformed data. Calibrations were developed using both non-pretreated spectra and using data pretreatments such as derivatives. Results showed that calibrations using partial least squares regression did not require any specific data format. Accurate calibrations were developed for fiber, digestibility, and protein measures in forages using any of the aforementioned spectral formats including non-background-corrected single beam spectra and even interferograms. While calibrations could be developed using any of the formats, results indicated that those using Kubelka-Munk and especially interferograms did not perform as well as the others, although they were still quite good. In conclusion, results using forage spectra indicated that accurate and equivalent calibrations can be developed using diffuse reflectance data, with (reflectance) or without background correction (single beam spectra), or log (1/reflectance) at least when using partial least squares regression for calibration development.

Reproducibility and Repeatability of Measures of Milk Coagulation Properties and Predictive Ability of Mid-Infrared Reflectance Spectroscopy

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a30, mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a30, respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a30. Repeatabilities of RCT and a30 measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a30. Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.

Strategies for Detecting Organic Liquids on Soils Using Mid-Infrared Reflection Spectroscopy

Stand-off monitoring for chemical spills can provide timely information for cleanup efforts, and mid-infrared reflection spectroscopy is one approach being investigated for spill detection. Using laboratory data, anomaly and target detection strategies were examined for the detection of four different low-volatility organic liquids on two different soil types. Several preprocessing and signal-weighting strategies were studied. Anomaly detection for C-H bands was good using second-derivative preprocessing and provided similar performance to that of target detection approaches such as generalized least-squares and partial least-squares, with detections at soil loads of approximately 3-6 microg/cm2 a real dosage. Good performance was also found for the detection of P=O, O-H, and C=O stretching vibrational modes, but the optimal strategy varied. The simplicity and generality of anomaly detection is attractive; however, target detection provides more capability for classification.

Non-invasive in-situ investigations versus micro-sampling: a comparative study on a Renoirs painting

In this paper, a multi-technique in-situ non-invasive approach has been followed for the study of the materials used for a painting by Pierre-Auguste Renoir, “A woman at her toilette”. The study was carried out using five portable spectroscopic techniques, namely X-ray fluorescence, mid-infrared reflectance spectroscopy, near infrared reflectance spectroscopy, and UV-Vis spectroscopy in absorption and emission. The painting was selected as a case study because it was examined in advance of the current investigation using conventional micro-sampling techniques. This provided the opportunity to evaluate potential and limitations of the non-invasive approach to the complex case of the modern painting.