Mid-infrared Reflectance Spectroscopy Research Articles

Comparison of near and mid-infrared reflectance spectroscopy for the estimation of soil organic carbon fractions in Madagascar agricultural soils

Assessing the different pools of soil organic carbon (SOC) improves our understanding of how and at what rate the different forms of carbon (C) are being formed or lost in soils. Physical fractionation of soil organic matter (SOM) has often been used to separate and quantify SOC pools, but this approach is very tedious and can rarely be performed on large sample sets. Infrared spectroscopy has proven useful for time- and cost-effective quantification of total SOC, which prompted us to study its ability to characterise the distribution of SOC in physical fractions. This study aimed to compare the potential of near- and mid-infrared reflectance spectroscopy (NIRS and MIRS, respectively) to predict the distribution of SOC in particle-size and particle-density fractions, using spectra of unfractionated soils. A set of 134 sieved (< 2 mm) soil samples originating from seven sites in contrasting pedoclimatic regions of Madagascar was studied. For each sample, five SOM fractions were separated: the particulate organic matter fraction (POM) and the particle-size fractions >200 μm, 50–200-μm, 20–50-μm and < 20 μm. The mass (g fraction 100 g−1 soil), SOC concentration (gC kg−1 fraction) and SOC amount (gC fraction kg−1 soil) were determined for each fraction in the laboratory. The NIR and MIR spectra were acquired on finely ground (< 0.2 mm) aliquots of unfractionated soil. Then, spectra were used for the prediction of each variable (i.e., the mass, SOC concentration and amount of each fraction, and SOC content of unfractionated soil), which was achieved with locally weighted partial least squares regression (LW-PLSR) on NIRS and MIRS data separately. For both spectral ranges, the same samples were used for calibration (n = 109, selected for spectral representativeness) and validation (n = 25).Models based on NIRS and MIRS yielded excellent predictions for SOC content in unfractionated soil (R2 = 0.98 and ratio of performance to interquartile range RPIQ ≥13) and accurate predictions (R2 ≥ 0.75 and RPIQ ≥2) for the mass, SOC concentration and SOC amount in most fractions. The predictions of SOC concentrations and SOC amounts were better in the fractions <200 μm (R2 ≥ 0.85 and RPIQ >3), especially in the fraction <20 μm (R2 > 0.9 and RPIQ >4.5). In most cases, NIRS slightly outperformed MIRS. This result contradicts most previous studies performed on soils from temperate regions but confirms those performed on soils from tropical regions.Infrared spectroscopy allowed accurate prediction of SOC distribution in particle-size fractions, which paves the way to high-throughput characterization of SOM.

Prediction of Phosphorus Sorption Index and Availability by NIR and MIR in Soils in Madagascar

The development of techniques for the rapid, inexpensive, and accurate determination of the phosphorus (P) availability and sorption index (PSI) in soils is important for P management in highly weathered tropical soils. The applicability of near- and mid-infrared reflectance spectroscopy (NIR and MIR) as tools for estimating P availability and PSI was assessed over a wide range of highly weathered soils in Madagascar. The predictions were based on chemometric methods using multivariate calibration models with partial least squares (PLS) regressions, and pedotransfer functions (PTFs). Chemometric methods failed to predict available P (Presin). However, a P sorption index, determined as the P remaining in solution (Prem), was estimated with acceptable accuracy with both NIR and MIR (R2cv = 0.70 − 0.73; R2v = 0.65 − 0.77; SEP(c) = 5.5 − 4.6 mg kg−1). The PTFs showed that the PSI was well explained by iron oxide, gibbsite, and sand contents, all of these compounds being well predicted by NIR or MIR (R2v &gt; 0.70). These results indicate that NIR and MIR can be helpful for a rapid estimate of PSI of highly weathered ferralitic soils.

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679-0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.

Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks.

Interest in reducing eructed CH4 is growing, but measuring CH4 emissions is expensive and difficult in large populations. In this study, we investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data to predict CH4 emission in lactating Canadian Holstein cows. A total of 181 weekly average CH4 records from 158 Canadian cows and 217 records from 44 Danish cows were used. For each milk spectra record, the corresponding weekly average CH4 emission (g/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d) were available. The weekly average CH4 emission was predicted using various artificial neural networks (ANN), partial least squares regression, and different sets of predictors. The ANN architectures consisted of 3 training algorithms, 1 to 10 neurons with hyperbolic tangent activation function in the hidden layer, and 1 neuron with linear (purine) activation function in the hidden layer. Random cross-validation was used to compared the predictor sets: MY (set 1); FY (set 2); PY (set 3); MY and FY (set 4); MY and PY (set 5); MY, FY, and PY (set 6); MIRS (set 7); and MY, FY, PY, and MIRS (set 8). All predictor sets also included age at calving and days in milk, in addition to country, season of calving, and lactation number as categorical effects. Using only MY (set 1), the predictive accuracy (r) ranged from 0.245 to 0.457 and the root mean square error (RMSE) ranged from 87.28 to 99.39 across all prediction models and validation sets. Replacing MY with FY (set 2; r = 0.288-0.491; RMSE = 85.94-98.04) improved the predictive accuracy, but using PY (set 3; r = 0.260-0.468; RMSE = 86.95-98.47) did not. Adding FY to MY (set 4; r = 0.272-0.469; RMSE = 87.21-100.76) led to a negligible improvement compared with sets 1 and 3, but it slightly decreased accuracy compared with set 2. Adding PY to MY (set 5; r = 0.250-0.451; RMSE = 87.66-100.94) did not improve prediction ability. Combining MY, FY, and PY (set 6; r = 0.252-0.455; RMSE = 87.74-101.93) yielded accuracy slightly lower than sets 2 and 3. Using only MIRS data (set 7; r = 0.586-0.717; RMSE = 69.09-96.20) resulted in superior accuracy compared with all previous sets. Finally, the combination of MIRS data with MY, FY, and PY (set 8; r = 0.590-0.727; RMSE = 68.02-87.78) yielded similar accuracy to set 7. Overall, sets including the MIRS data yielded significantly better predictions than the other sets. To assess the predictive ability in a new unseen herd, a limited block cross-validation was performed using 20 cows in the same Canadian herd, which yielded r = 0.229 and RMSE = 154.44, which were clearly much worse than the average r = 0.704 and RMSE = 70.83 when predictions were made by random cross-validation. These results warrant further investigation when more data become available to allow for a more comprehensive block cross-validation before applying the calibrated models for large-scale prediction of CH4 emissions.

Extracting carrier concentration of black c-BN single crystal by mid-infrared reflectance spectroscopy

Cubic boron nitride (c-BN) is a synthetic ultra-wide bandgap semiconductor with attractive application prospects in electronics and optoelectronics. At present, the size of c-BN single crystals obtained by hot pressing method is generally less than 1 mm extremely unfavorable to extracting its carrier concentration through Hall effect measurement. For c-BN crystals, the longitudinal optical phonon-plasma coupling (LOPC) mode which is produced by the coupling of longitudinal optical phonons and free carriers can be observed in mid-infrared reflectance spectrum. Thus, in this work, the mid-infrared reflectance spectrum of the black c-BN crystal grown by hot pressing method is measured. Based on classical dielectric function theory, the carrier concentration of that c-BN sample is finally extracted as 7.14 × 1017 cm−3. Combining electrical measurements, its mobility is also obtained as 11.91 cm2·V-1·s-1.

Combining Variable Selection and Multiple Linear Regression for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR Spectroscopy

The successful estimation of soil organic matter (SOM) and soil total nitrogen (TN) contents with mid-infrared (MIR) reflectance spectroscopy depends on selecting appropriate variable selection techniques and multivariate methods for regression analysis. This study aimed to explore the potential of combining a multivariate method and spectral variable selection for soil SOM and TN estimation using MIR spectroscopy. Five hundred and ten topsoil samples were collected from Quzhou County, Hebei Province, China, and their SOM and TN contents and reflectance spectra were measured using DRIFT-MIR spectroscopy (diffuse reflectance infrared Fourier transform in the mid-infrared range, MIR, wavenumber: 4000–400 cm−1; wavelength: 2500–25,000 nm). Two multivariate methods (partial least-squares regression, PLSR; multiple linear regression, MLR) combined with two variable selection techniques (stability competitive adaptive reweighted sampling, sCARS; bootstrapping soft shrinkage approach, BOSS) were used for model calibration. The MLR model combined with the sCARS method yielded the most accurate estimation result for both SOM (Rp2 = 0.72 and RPD = 1.89) and TN (Rp2 = 0.84 and RPD = 2.50). Out of the 2382 wavenumbers in a full spectrum, sCARS determined that only 31 variables were important for SOM estimation (accounting for 1.30% of all variables) and 27 variables were important for TN estimation (accounting for 1.13% of all variables). The results demonstrated that sCARS was a highly efficient approach for extracting information on wavenumbers and mitigating redundant wavenumbers. In addition, the current study indicated that MLR, which is simpler than PLSR, when combined with spectral variable selection, can achieve high-precision prediction of SOM and TN content. As such, DRIFT-MIR spectroscopy coupled with MLR and sCARS is a good alternative for estimating the SOM and TN of soils.

Mid-Infrared Reflectance Spectroscopy Based on External Cavity Quantum Cascade Lasers for Mineral Characterization.

Mineral characterization using tunable external cavity quantum cascade lasers for mid-infrared reflectance spectroscopy is presented. Reflection spectra taken on common minerals are shown along with their spectral variations. Measurements at different incident angles were also obtained to mimic in the field applications. Finally, spectra were obtained on mineral mixtures and their analysis assesses the quality and usability of the technique for mineral characterization.

Impacts of rice-husk biochar on soil microbial biomass and agronomic performances of tomato (Solanum lycopersicum L.)

Tomato is beneficial to human health because it contains valuable vitamins such as vitamins A, C and several minerals. However, to meet up with the demands of the ever increasing population, there is need to improve tomato production. This research, thus, investigated the impact of rice-husk biochar on the agronomic performances of tomato plant and microbial biomass of carbon, nitrogen and phosphorus in different tomato growth stages. The rice husk biochar pyrolyzed at 350 °C was amended with soil at four different application rates: 0, 2.5, 5.0 and 7.5 t/ha. Physicochemical property of soil was conducted using Mid Infrared Reflectance Spectroscopy method. Impact of biochar on Microbial Biomass Carbon, Microbial Biomass Nitrogen and Microbial Biomass Phosphorous was conducted using fumigation extraction method and monitored at three functional stages. Biochar application appreciably increase the soil physicochemical properties such as pH, Ca, Na, H+, S, P, B, C, Zn and cation exchangeable capacity in comparison with the control. Biochar amended soil significantly enhanced tomato height, stem girth, leaf area, flowers, fruit yields and weight. Although, B3 recorded the lowest leaf area, it possessed the highest number of fruits and fruit weight of 3 and 40%, respectively. The ratio of Microbial biomass C:N:P for biochar amended soil at 7.5 t/ha (B3) was 302.30:18.81:11.75 µg/g, compared to control, which was 242.12:18.30:11.49 µg/g. This study revealed that biochar amendments significantly (p < 0.05) increased the yields and microbial biomass of tomato plants. Conclusively, the application of rice-husk biochar (7.5 t/ha) to soil is considered as a suitable approach to improve tomato growth and yield.

Comparison of Near and Mid-Infrared Reflectance Spectroscopy for the Determination of Carbon Distribution Particle-Size Fractions of Soils of Madagascar

Comparison of Near and Mid-Infrared Reflectance Spectroscopy for the Determination of Carbon Distribution Particle-Size Fractions of Soils of Madagascar

Using carbonate absorbance peak to select the most suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy

Mid-Infrared reflectance spectroscopy (MIRS, 4000–400 cm−1) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents, based on prediction models when the test dataset is well represented by the calibration set, with similar SIC range and distribution and pedological context. This work addresses the case where the test dataset, here originating from France, is poorly represented by the calibration set, here originating from Tunisia, with different SIC distributions and pedological contexts. It aimed to demonstrate the usefulness of 1) classifying test samples according to SIC level based on the height of the carbonate absorbance peak at 2510 cm−1, and then 2) selecting a suitable prediction model according to SIC level. Two regression methods were tested: Linear Regression using the height of the carbonate peak at 2510 cm−1, called Peak-LR model; and Partial Least Squares Regression using the entire MIR spectrum, called Full-PLSR model. First, our results showed that Full-PLSR was 1) more accurate than Peak-LR on the Tunisian validation set (R2val = 0.99 vs. 0.86 and RMSEval = 3.0 vs. 9.7 g kg−1, respectively), but 2) less accurate than Peak-LR when applied on the French dataset (R2test = 0.70 vs. 0.91 and RMSEtest = 13.7 vs. 4.9 g kg−1, respectively). Secondly, on the French dataset, predictions on SIC-poor samples tended to be more accurate using Peak-LR, while predictions on SIC-rich samples tended to be more accurate using Full-PLSR. Thirdly, the height of the carbonate absorbance peak at 2510 cm−1 might be used to discriminate SIC-poor and SIC-rich test samples (<5 vs. > 5 g kg−1): when this height was > 0, Full-PLSR was applied; otherwise Peak-LR was applied. Coupling Peak-LR and Full-PLSR models depending on the carbonate peak yielded the best predictions on the French dataset (R2test = 0.95 and RMSEtest = 3.7 g kg−1). This study underlined the interest of using a carbonate peak to select suitable regression approach for predicting SIC content in a database with different distribution than the calibration database.

Extraction of carrier concentration and mobility of ZnO by mid-infrared reflectance spectroscopy

As a representative of the third-generation semiconductors, ZnO has great development potential in the fields of optics and electricity, so the measurement on its electrical properties is quite important. Here, we try to extract the carrier concentration and mobility of ZnO by mid-infrared reflectance spectroscopy. Considering that the longitudinal optical phonon-plasmon coupling (LOPC) mode formed by the coupling of ZnO optical phonons and free carriers depends on carrier concentration and mobility, we conduct a test on the MIR spectra (400-1600 cm−1) of ZnO crystals with different carrier concentration (1018-1015 cm−3). By fitting the strong reflection peak shape corresponding to phonon-polaritons formed by the coupling of its LOPC and infrared light through m-CDF model, the carrier concentration and mobility of these samples are obtained, which are consistent with those got from the Hall-effect measurement.

Mid-infrared reflectance spectroscopy of synthetic glass analogs for Mercury surface studies

We have synthesized and analyzed by mid-infrared reflectance spectroscopy silicate glasses that are representative for the glasses on the surface of Mercury. The glass compositions are based on high-pressure laboratory experiments and the resulting compositions of the glass phase. The spectra are of interest for investigating the surface of Mercury using the MERTIS (Mercury Radiometer and Thermal Infrared Spectrometer) instrument on board of the ESA/JAXA BepiColombo mission.Both powdered fractions and polished blocks have been analyzed. Powdered size fractions of 0–25 μm, 25-63 μm, 63-125 μm, and 125–250 μm were measured in reflectance in the thermal infrared (2–20 μm). Spectra for powdered bulk glasses (1.6 wt% - 19.0 wt% MgO) show a single, dominating Reststrahlenband (RB, 9.3–9.8 μm), a Christiansen Feature (CF; 7.6 μm - 8.1 μm), and a size dependent Transparency Feature (TF; 11.6–11.9 μm). Micro-FTIR analyses of polished blocks of glasses (3.4–26.5 wt% MgO) have characteristic bands at 7.8–8.2 μm (CF), and 9.3–9.9 μm (RB). Only few olivine crystalline features were observed.Spectral features correlate with compositional characteristics, e.g. SiO2 content or SCFM (SiO2/(SiO2 + CaO + FeO + MgO) index. The strongest correlation between band features CF and the strong RB are with Mg/Si. No simple mixture of glass spectra from this study is able to reproduce the entire ground based spectrum of the surface of Mercury. However, Mg-rich glasses reproduce identified features at 8.5 μm, 9.9 μm and 12.4 μm.

Temperature dependent mid-infrared (5–25 μm) reflectance spectroscopy of carbonaceous meteorites and minerals: Implication for remote sensing in Solar System exploration

Interpretation of spectroscopic data from remote sensing strongly depends on the spectroscopic properties, particle size and temperature of materials present on the observed surface. Spectral indices of silicates, carbonates, sulfates, oxides and chemicals available on public database are commonly obtained at room temperature and pressure. Hitherto, few studies were performed by analyzing the effects of space environment such as low pressure and temperature on spectroscopic features of minerals, mostly focused on near infrared spectral region. In particular, whether temperature can affect spectral properties of minerals such as the peak emissivity position, band area and shape, was advanced decades ago, but a systematic laboratory study on such effects is still missing. This is especially lacking in the mid-infrared region, where laboratory data are almost completely absent. Thus, it is pivotal to acquire spectra in vacuum both at various temperatures and with variable particle sizes, for better simulating space environmental conditions.Our experimental apparatus at INAF-Astrophysical Observatory of Arcetri allows reflectance measurements in an extended spectral range from VIS to far IR and at temperatures ranging from 64 K to 500 K. We present here a detailed analysis on temperature-dependent variation on mineral and carbonaceous chondrite samples in the spectral range 1500–400 cm−1 (6.6–25 μm in wavelength). Mineral phases and meteorites analyzed are: pyroxene, olivine, serpentine, Tagish Lake (CI2-ungruped), Aguas Zarcas (CM2) and Orgueil (CI1). Samples are prepared with particle sizes <20 μm, <200 μm, and 200–500 μm. Our results show that temperature induces spectral features modifications such as peak position shifts, band area and peak intensity changes. Such modifications are reversible with temperature and the trend of variation is related to the sample composition and hydration level. Moreover, magnitude of temperature-dependent spectroscopic changes is strongly linked with grain size and composition, hence making this type of analysis pivotal for a correct interpretation of data collected by space telescopes and orbital spacecrafts.

Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library

Mid-infrared reflectance spectroscopy (MIRS, 4000–400 cm−1) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This approach has mainly been demonstrated by using datasets originating from the same area A, with similar geopedological conditions, to build, validate and test prediction models. The objective of this study was to analyse how MIRS performs when applied to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 topsoil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS topsoil dataset including 96 soil samples to test them. Our results showed that when using the French MIRS soil database, i) the SOC and SIC of French validation samples were successfully predicted using global models (R2val = 0.88 and 0.98, respectively), ii) the SIC of Tunisian samples was also predicted successfully both using a global model and using a selection of spectral neighbours from the French calibration database (R2test of 0.96 for both), iii) the SOC of Tunisian samples was predicted moderately well by global model (R2test of 0.64) and a transformation by natural logarithm of the calibration SOC values significantly improved the SOC prediction of Tunisian samples (R2test of 0.97), and iv) a transformation by natural logarithm of SOC values provided more benefit than a selection of spectral neighbours from the French calibration database for predicting Tunisian SOC values. Therefore, in the future, MIRS might replace conventional physico-chemical analysis techniques, or at least be considered as an alternative technique, especially when optimally exhaustive calibration databases will become available.

Mid-Infrared Reflectance Spectroscopy of Oil Sands Minerals Based on Tunable Quantum Cascade Lasers.

Minerals play an important role in the oil sands extraction efficiency. It is thus important to assess the major mineral abundance in oil sands ores. This paper presents the application of tunable quantum cascade lasers for mid-infrared reflectance spectroscopy on oil sands minerals. The investigations and results show a new tool to determine oil sands mineral type and to determine potentially quartz and clay contents.

Hyperspectral Estimation of Soil Organic Matter Content using Different Spectral Preprocessing Techniques and PLSR Method

Soil organic matter (SOM) is the main source of soil nutrients, which are essential for the growth and development of agricultural crops. Hyperspectral remote sensing is one of the most efficient ways of estimating the SOM content. Visible, near infrared, and mid-infrared reflectance spectroscopy, combined with the partial least squares regression (PLSR) method is considered to be an effective way of determining soil properties. In this study, we used 54 different spectral pretreatments to preprocess soil spectral data. These spectral pretreatments were composed of three denoising methods, six data transformations, and three dimensionality reduction methods. The three denoising methods included no denoising (ND), Savitzky–Golay denoising (SGD), and wavelet packet denoising (WPD). The six data transformations included original spectral data, R; reciprocal, 1/R; logarithmic, log(R); reciprocal logarithmic, log(1/R); first derivative, R’; and first derivative of reciprocal, (1/R)’. The three dimensionality reduction methods included no dimensionality reduction (NDR), sensitive waveband dimensionality reduction (SWDR), and principal component analysis (PCA) dimensionality reduction (PCADR). The processed spectra were then employed to construct PLSR models for predicting the SOM content. The main results were as follows—(1) the wavelet packet denoising (WPD)-R’ and WPD-(1/R)’ data showed stronger correlations with the SOM content. Furthermore, these methods could effectively limit the correlation between the adjacent bands and, thus, prevent “overfitting”. (2) Of the 54 pretreatments investigated, WPD-(1/R)’-PCADR yielded the model with the highest accuracy and stability. (3) For the same denoising method and spectral transformation data, the accuracy of the SOM content estimation model based on SWDR was higher than that of the model based on NDR. Furthermore, the accuracy in the case of PCADR was higher than that for SWDR. (4) Dimensionality reduction was effective in preventing data overfitting. (5) The quality of the spectral data could be improved and the accuracy of the SOM content estimation model could be enhanced effectively, by using some appropriate preprocessing methods (one combining WPD and PCADR in this study).

Improvement in spectral library-based quantification of soil properties using representative spiking and local calibration – The case of soil inorganic carbon prediction by mid-infrared spectroscopy

Mid-infrared reflectance spectroscopy (MIRS) is time- and cost-effective. It was used for quantifying soil inorganic carbon (SIC) concentration in France based on a national library, and performances were evaluated on an independent regional set. Our objective was to improve the accuracy of MIRS predictions based on common multivariate regression, through spiking (enrichment of the national library with some representative target samples) with possible extra-weighting (replication of spiking samples) and local calibration (only using calibration samples that are spectral neighbours of each target samples), which have not been fully explored yet, in combination especially.Global (i.e. common) calibration yielded accurate prediction (standard error of prediction, SEP, was ≈5 g kg−1), which could be improved when the library was completed with spiking samples (optimally 10samples extra-weighted 40times; SEP = 3.3 g kg−1). Using spiking samples only (without the library) yielded slightly less accurate results (SEP = 3.6 g kg−1). Prediction was more accurate using local calibration without spiking, but on a validation set that was reduced because some validation samples lacked calibration neighbours (SEP = 2.5–2.7 g kg−1). Local calibration with spiking (optimally 10samples without extra-weight) yielded somewhat less accurate prediction but for the full validation set when few calibration neighbours were required (SEP = 2.7 g kg−1), or higher accuracy on the reduced validation set when many neighbours were required (SEP = 2.3 g kg−1).These accurate predictions demonstrated the usefulness of representative spiking and local calibration for rendering large soil spectral libraries fully operational, while extra-weighting had no additional benefit. Along with more exhaustive spectral libraries, this paves the way for extensive use of MIRS for SIC determination.

Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis

The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate [coefficient of determination (R2) = 0.60; root mean squared error (RMSE) = 1.68 kg] than MIRS alone (R2 = 0.30; RMSE = 2.23 kg) or NIRS alone (R2 = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R2 = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R2 = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R2 = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R2 = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows.

Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa

Assessment and monitoring of soil resources is needed for ensuring environmental sustainability of rice production in sub-Saharan Africa (SSA). Diffuse reflectance spectroscopy has the potential to offer means to evaluate and monitor soils at large scale in an efficient, affordable and timely manner. However, its accuracy for predicting chemical and physical properties of soils under rice cultivation is little known. The objectives of this study were to i) determine physical and chemical soil fertility properties which can be accurately analyzed using near infrared (NIR) (700–2500 nm), mid-infrared (MIR) (2500–16,670 nm), or a combination of both (NIR-MIR) (700–16,670 nm) spectral libraries, ii) compare their prediction potential for identifying the most suitable spectral range for simultaneous analysis of soil fertility properties, and iii) assess degrees of variation of 29 soil fertility properties for 2845 soil samples collected from 42 study sites in 20 SSA countries. Diffuse reflectance spectroscopy data were obtained using a Fourier-transform infrared spectrometer (FT-IR) Tensor 27 with a high-throughput screening (HTS-XT) extension unit. About 10% of the whole sample set was chosen for conventional wet chemistry soil analysis, and was used for developing best fitted models with partial least squares regression (PLSR) that can estimate soil fertility properties using infrared (IR) spectra. The prediction performances were evaluated by R2, RMSE and RPIQ. Good prediction models (0.75 < R2 ≤ 0.86 and 1.36 ≤ RPIQ ≤ 3.78) were obtained for 13 soil properties consisting of pH H2O 1:2, exchangeable Ca, exchangeable Mg, sum of exchangeable cations, CEC, ECEC, base saturation percentage, total N, total organic C, clay content, silt content, phosphorus sorption index (PSI) and extractable Al. Satisfactory predictions (0.53 ≤ R2 ≤ 0.75 and 0.59 ≤ RPIQ ≤ 2.56) were obtained for pH H2O 1:2.5, exchangeable K, extractable Mn, Cu, and B, and sand content. NIR, MIR, and combined NIR-MIR diffuse reflectance spectroscopy demonstrate the best prediction potential for 14, 21, and 76% of the studied soil fertility properties, respectively. Except soil pH, most of soil properties are moderate to highly variable (CV = 21–131%). On average, soils in rice fields are characterized by moderate acidity, and low total N, available P and B. In conclusion, the combined NIR-MIR diffuse reflectance spectroscopy can offer an alternative to conventional wet chemistry methods for assessing soil fertility properties in rice fields in SSA, except for those having poor prediction potentials.

Prediction of soil carbon levels in calcareous soils of Iran by mid-infrared reflectance spectroscopy

ABSTRACTThe objective of this study was to assess the predictive performance of midDRIFTS-PLSR models in quantifying total carbon (TC), total organic carbon (TOC), total inorganic carbon (TIC), total nitrogen (TN), hot water extractable carbon and nitrogen (CHWE, NHWE), pH, and the clay, silt, and sand content of soils. A total of 68 soil samples were taken across an agroecological region in southwest Iran, and analyzed in the laboratory using mid-DRIFTS-PLSR. midDRIFTS-PLSR calibration models were developed, and external validation was performed for each of the soil properties via an independent algorithm. The calibration and validation models allowed for a sufficient prediction of TC, TIC, and TOC with residual prediction deviations ≥3 and R2 values >0.9. The precise prediction ofcarbon fractions, such as TC, TIC, and TOC, in a rapid and inexpensive manner confirmed that midDRIFTS analysis was a rapid-throughput and cost-effective technique for monitoring soil carbon at the regional scale.