Mid-infrared Reflectance Research Articles

Reflectance Spectroscopy of 27 Fine-particulate Mineral Samples from Far-ultraviolet through Mid-infrared (0.12–20 μm)

This paper presents far-ultraviolet through mid-infrared (0.12–20 μm) reflectance spectra of 27 fine-particulate (<10 μm) terrestrial mineral samples, providing continuous spectra that cover an unusually broad spectral range and are of unusually fine particle size relative to most existing spectral libraries. These spectra of common geologic materials are useful for future applications that study the dust on various planetary bodies. Reflectance spectra were acquired of the samples at multiple laboratories at multiple wavelengths. All of the spectra were compared to one another to observe the general, common spectral characteristics (e.g., slope, band shape, and band depth), and the best segments of the spectra representing the mineral reflectance were scaled and spliced together to form a “Frankenspectrum” for each mineral that best represents the full wavelength range of far-ultraviolet, visible, near-infrared, and middle-infrared wavelengths. These scaled and spliced Frankenspectra, as well as the entire set of individual “original” reflectance spectra from each laboratory, are available in the Planetary Data System Geosciences Node.

Laboratory reflectance spectra of enstatite and oldhamite mixtures for comparison with Earth-based reflectance spectra of asteroid 2867 Šteins and Mercury

The reflectance spectra of synthetic oldhamite (CaS), synthetic enstatite (Mg2Si2O6), and their mixtures have been studied in the spectral range from 0.3 μm to 16 μm. The spectrum of enstatite is very bright, with a steep slope in the ultraviolet (UV) and an almost neutral slope in the visible (VIS) and near-infrared (NIR). The mid-infrared (MIR) region is characterized by the Christiansen feature, Reststrahlen bands and the Transparency feature. The oldhamite spectrum shows a red slope in the UV and VIS and an absorption band at 0.41 μm. The absorption band has a relative depth of 11.4%. In the MIR, the oldhamite spectrum is much brighter than the enstatite spectrum and shows several broad absorption bands. The spectra of the mixtures show an intermediate behavior between the two endmembers. The absorption band at 0.41 μm is visible in the spectra of all mixtures, even in the spectrum of the mixture with only 1 vol% oldhamite. In the MIR, the spectra of the mixtures with ≤10 vol% oldhamite are very similar to the spectrum of pure enstatite. Changes in the spectral characteristics such as reflectance or band depths do not follow simple linear trends but show two distinct trends: One for mixtures with ≤10 vol% oldhamite and one for mixtures with ≥20 vol% oldhamite. Changes occur significantly faster in the spectra of mixtures with ≤10 vol% than in those with ≥20 vol% oldhamite.Reflectance spectra of the E[II]-type asteroid 2867 Šteins are flat and almost featureless but show an absorption band at 0.49 μm, which is attributed to oldhamite. Comparison of the laboratory spectra in the VIS and MIR with spectra of Šteins gives an upper limit for the oldhamite content on its surface of 40 %vol. Hence, Šteins probably consists of aubrite-like material with virtually FeO-free enstatite as the major constituent and an oldhamite abundance of <40 vol%. Šteins and other E[II]-type asteroids likely formed through igneous processes on a larger now destroyed body, where an immiscible CaS-melt formed within a silicate melt.The surface of Mercury is also linked to FeO-poor silicates like enstatite. Oldhamite and other sulfides are linked to the formation of hollows on Mercury. Mixtures of enstatite and oldhamite could therefore serve as suitable model analog for the surface of Mercury. Contrasting trends at ∼7–8.5 μm in the MIR reflectance spectra of oldhamite and enstatite could be used as an indicator for the presence of oldhamite in spectral data that will be collected by the MERTIS (Mercury Radiometer and Thermal Infrared Spectrometer) instrument onboard the ESA/JAXA BepiColombo mission to Mercury.

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

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

A pilot study on non-invasive in situ detection of phytochemicals and plant endogenous status using fiber optic infrared spectroscopy

Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000–900 cm−1) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.

Asymmetric Multienergy-Coupled Radiative Warming Textiles for Personal Thermal-Moisture Management.

In order to address the requirements for warmth and energy conservation in cold climates, the development of personal thermal management textiles that regulate local human thermal comfort has emerged as a promising solution in recent times. Nevertheless, existing warming textile strategies often rely on a singular energy source, exhibit inadequate air/moisture permeability, and lack adaptability to dynamic and intricate climate variations. Herein, a novel multienergy-coupled radiative warming Janus textile has been effectively designed and fabricated via screen printing and foam finishing. Taking advantage of the synergistic effects of directional water transport capability of polyester-covered cotton (with a directional water-transport index of R = 577.5%), high mid-infrared radiant reflection (at 60%), electrothermal conversion of copper coating (with a sheet resistance of 0.01 Ω sq-1), and strong solar absorption of the nanoporous structure TA@APTES@Fe(III)@CNT (TAFC) coating (at 98.5%), the Janus fabric exhibits exceptional performance in expelling out one-way sweat/moisture (R = 329.3%) and solar heating (86.9 °C)/Joule heating (226.4 °C at 3.0 V)/heat retention (2.4 °C higher than that of cotton fabric). Furthermore, the fabric is also provided with exceptional mechanical, washing, flame-retardant, and antibacterial performance. This research holds the potential to revolutionize the development and production of warming textiles by incorporating desirable sweat/moisture permeability and multienergy-coupled heating.

Colorful low-emissivity paints for space heating and cooling energy savings

Space heating and cooling consume ~13% of global energy every year. The development of advanced materials that promote energy savings in heating and cooling is gaining increasing attention. To thermally isolate the space of concern and minimize the heat exchange with the outside environment has been recognized as one effective solution. To this end, here, we develop a universal category of colorful low-emissivity paints to form bilayer coatings consisting of an infrared (IR)-reflective bottom layer and an IR-transparent top layer in colors. The colorful visual appearance ensures the aesthetical effect comparable to conventional paints. High mid-infrared reflectance (up to ~80%) is achieved, which is more than 10 times as conventional paints in the same colors, efficiently reducing both heat gain and loss from/to the outside environment. The high near-IR reflectance also benefits reducing solar heat gain in hot days. The advantageous features of these paints strike a balance between energy savings and penalties for heating and cooling throughout the year, providing a comprehensive year-round energy-saving solution adaptable to a wide variety of climatic zones. Taking a typical midrise apartment building as an example, the application of our colorful low-emissivity paints can realize positive heating, ventilation, and air conditioning energy saving, up to 27.24 MJ/m2/y (corresponding to the 7.4% saving ratio). Moreover, the versatility of the paint, along with its applicability to diverse surfaces of various shapes and materials, makes the paints extensively useful in a range of scenarios, including building envelopes, transportation, and storage.

Graphene Hybrid Metasurfaces for Mid-Infrared Molecular Sensors.

We integrated graphene with asymmetric metal metasurfaces and optimised the geometry dependent photoresponse towards optoelectronic molecular sensor devices. Through careful tuning and characterisation, combining finite-difference time-domain simulations, electron-beam lithography-based nanofabrication, and micro-Fourier transform infrared spectroscopy, we achieved precise control over the mid-infrared peak response wavelengths, transmittance, and reflectance. Our methods enabled simple, reproducible and targeted mid-infrared molecular sensing over a wide range of geometrical parameters. With ultimate minimization potential down to atomic thicknesses and a diverse range of complimentary nanomaterial combinations, we anticipate a high impact potential of these technologies for environmental monitoring, threat detection, and point of care diagnostics.

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

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

Mid-IR spectral properties of different surfaces of silicate mixtures before and after excimer laser irradiation

The behaviour of the Christiansen feature (CF) and the Reststrahlen bands (RBs) in mid-infrared (IR) reflectance spectra on various silicate mixtures as pressed pellets and powders was investigated in high-vacuum. In addition, the influence of micrometeorite bombardment simulated with an excimer laser was studied. The mixtures cover a wide range of possible hermean surface compositions and include the minerals olivine, pyroxene (enstatite and diopside), and plagioclase.For the laser experiments, silicates were pressed into pellets and examined by reflectance infrared spectroscopy to identify changes caused by micrometeorite impacts as one tracer of space weathering on airless bodies such as Mercury. For comparison, measurements were also performed on loose powders with the same compositions under the same conditions. As a result, it can be shown that the RBs of olivine are rather affected by laser irradiation although SEM investigations show the destruction mainly of plagioclase, indicating that the RBs of plagioclase are masked by the “stronger” RBs of olivine and pyroxene. Furthermore, we found that the CF in mixtures with a plagioclase content of >50% does not shift significantly towards the CF of pyroxene or olivine. On the other hand, the CF of a mixture containing 50% olivine shifts significantly to shorter wavelengths when pyroxene or plagioclase are present in the mixture. Therefore, care is required when interpreting remote sensing data using the CF alone. We also found that the CF shifts to longer wavelengths in rough (regolithic) samples.Our work demonstrates large dependencies of the CF and the RBs positions on the composition of the silicates as well as on the nature of the surface, which is important for space missions, e.g., data acquired by the MErcury Radiometer and Thermal Infrared Spectrometer (MERTIS) experiment onboard BepiColombo.

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.

A wearable textile with superb thermal functionalities and durability towards personal thermal management

As the enhanced frequency and intensity of extreme cold weather events, it is of substantial importance to ameliorate the personal thermal comfort for human health. However, developing effective technology with advanced thermal functionalities and durability to achieve the efficient personal thermal management (PTM) is still a challenge. Herein, we demonstrate a novel textile with asymmetric structure that not only possesses high-level solar and radiative heating but also presents excellent durability and wearability. The textile is realized via in-situ growing polypyrrole on cotton fibers for outer solar harvest, followed by depositing silver nanowires on one side for inner mid-infrared reflection, and consequent coating polydimethylsiloxane for durability. The designed textile ensures the maximum solar absorptivity of visible region (98.6 %) and mid-infrared reflectivity (76.3 %), effectively promoting the solar heating and restraining the radiation heat dissipation of human body. Contrast with pristine cotton fabric, wearing the textile can increase the temperature of human body by 2.0℃ without energy consumption. Moreover, the excellent Joule heating (∼137.0℃ at 3 V), electromagnetic interference shielding (67.0 dB), self-cleaning and antibacterial properties, breathability, and mechanical strength are also possessed by the textile. Impressively, after the damage of three external forces (abrasion, bending, and laundering), outstanding solar absorptivity, mid-infrared reflectivity, conductivity and hydrophobicity are reserved for the textile, indicating the excellent durability. With its superb passive heating functionalities and durability, the novel textile helps to achieve the ideal PTM and mitigate climate change.

A Framework for High-Resolution Mapping of Soil Organic Matter (SOM) by the Integration of Fourier Mid-Infrared Attenuation Total Reflectance Spectroscopy (FTIR-ATR), Sentinel-2 Images, and DEM Derivatives

Soil organic matter (SOM), as the greatest carbon storage in the terrestrial environment, is inextricably related to the global carbon cycle and global climate change. Accurate estimation and mapping of SOM content are crucial for guiding agricultural output and management, as well as controlling the climate issue. Traditional chemical analysis is unable to satisfy the dynamic estimation of SOM due to its low timeliness. Remote and proximal sensing have significant advantages in terms of ease of use, estimation accuracy, and geographical resolution. In this study, we developed a framework based on machine learning to estimate SOM with high accuracy and resolution using Fourier mid-infrared attenuation total reflectance spectroscopy (FTIR-ATR), Sentinel-2 images, and DEM derivatives. This framework’s performance was evaluated on a regional scale using 245 soil samples from northeast China. Results indicated that the calibration size could be shrunk to 50% while achieving a fair prediction performance for SOM content. The Lasso, partial least squares (PLS), support vector regression (SVR), and convolutional neural networks (CNN) performed well in predicting SOM from FTIR-ATR spectra, and the performance was enhanced further by using Sentinel-2 images and DEM derivates. The PLS, SVR, and CNN models created SOM maps with higher spatial resolution and variation than the Kriging approach. The PLS and SVR models provided enough variety and were more realistic in the local SOM map, making them usable at the field scale, and the suggested framework took a fresh look at high-resolution SOM mapping.

Excitonic quenching of the oxygen-chain phonon in the photoinduced metal-to-insulator transition of photoexcited Sr0.95NbO3.37 studied by ultraviolet-resonance Raman scattering

The quasi-one-dimensional metal ${\mathrm{Sr}}_{0.95}{\mathrm{NbO}}_{3.37}$ shows a metal-to-insulator transition upon laser-induced pumping of the d-d exciton at 1.1 eV. The oxygen-chain-related phonon modes are highly susceptible to this metal-to-insulator transition due to their sensitivity to structural and electronic changes. We employ angle-dependent UV-resonance Raman spectroscopy to probe the nonequilibrium oxygen-chain-related phonon dynamics and the concomitant changes in the angle-dependent midinfrared reflectance. The latter shows the expected reduction of charge carrier density upon pumping, whereas the central chain mode at 680 $\mathrm{cm}{}^{\ensuremath{-}1}$ can be selectively quenched upon pumping the d-d excitons. We find that the underlying quenching mechanism for this phonon is electronically driven and related to the photo-induced bleaching of the charge-transfer transition. First-principles calculations show the connection between phonon dispersion and electronic band structure leading to the selective quenching of the oxygen-chain mode. Our results contribute to a deeper understanding of the interplay between lattice and charge degrees of freedom for exciton-based optoelectronics.

Digital morphometrics and genesis of soils with buried horizons and lithological discontinuities in southeastern Brazil

Soil morphometrics aims to measure attributes observed in soil profiles. The use of digital technologies in the morphological study of soil profiles has yet to be further explored by the scientific community. Particularly, soils with buried horizons, which are common in lowlands and floodplains throughout the world, have never been examined using digital morphometrics. This study aimed to examine the morphology and genesis of three soil profiles with buried horizons in Brazil using digital soil morphometrics combined with wet-chemistry and mineralogical analyses (including X-ray diffraction – XRD). Digital analyses used three proximal sensors – portable X-ray fluorescence spectrometry (pXRF), visible near-infrared (Vis-NIR) and mid-infrared (MIR) diffuse reflectance spectrometry. The soils had buried horizons and were classified as Typic Udifolists (TU), Typic Dystrustepts (TD) and Typic Endoaquents (TE). Each soil profile was sampled in a 15 × 15 cm grid, with 40 samples per profile. All sensors identified the buried soil horizons. Vis-NIR and MIR identified the presence of several key minerals and variations in soil organic matter (SOM) that were not observed in the whole soil pits. PXRF captured discontinuities in the parent material, not only between deposited and buried horizons, but also within horizons. The deposited material above TU and TE was derived from neighboring Fe-rich Oxisols and Ultisols, as shown by high Fe content and absorption features of Fe oxides. Sensor analyses were quick and environmentally friendly. Proximal sensor data enhanced morphological assessment of soil profiles, and provided insights otherwise very difficult or even impossible with only traditional and visual methods.

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.

Effects of silicon dioxide as the polar dielectric on the infrared absorption spectrum of the metal-insulator-metal metasurface

Metal-insulator-metal metasurfaces have been widely used as high-performance absorbers in almost all electromagnetic spectral ranges. Their absorption spectra can be engineered by making variations in the geometry of the unit cells and/or by embedding materials with specific optical constants. Including a polar dielectric in their structure is another approach for manipulating their absorption spectra. In this research, we have numerically and experimentally investigated the effect of using silicon dioxide (SiO 2) as a polar dielectric on the absorption spectrum of a metal-insulator-metal metasurface composed of a tri-layer of Ni-SiO 2 -Ni. Our results have shown the presence of absorption peaks in the mid-infrared which are attributed to the excitation of the optical phonons in the SiO 2 spacer layer. Particularly, the excitation of the Berreman mode in the SiO 2 spacer layer was observed and its effect on the total absorption spectrum is studied. The parametric effects of the top patterned Ni layer, the incident angle, and the polarization are also investigated. This study can provide engineering capabilities for the mid-infrared absorbers and reflection filters.

Quantitative Determination of Polyphenols and Flavonoids in Cistus × incanus on the Basis of IR, NIR and Raman Spectra.

Cistus is a plant that has been used in natural medicine for hundreds of years; it works primarily as an antioxidant and cleansing agent. Cistus × incanus leaves or herb can be an attractive source of polyphenols and flavonoids. The official protocols of active compound analysis relies on the extraction of compounds of interest from plant matter, which makes their determination long and costly. An analysis of plant material in its native state can be performed using vibrational spectroscopy. This paper presents a comparison of Raman spectroscopy, attenuated total reflection in mid-infrared and diffuse reflectance technique in the near-infrared region for the simultaneous quantification of total polyphenols (TPC) and flavonoids (TF) content, as well as the determination of FRAP antioxidant activity of C. incanus material. Utilizing vibrational spectra and using partial least squares algorithm, TPC and TF were quantified with the RSEPVAL errors in the 2.7-5.4% range, while FRAP antioxidant activity for validation sets was determined with relative errors ranged from 5.2 to 9.3%. For the analyzed parameters, the lowest errors of predictions were computed for models constructed using Raman data. The developed models allow for fast and precise quantification of the studied active compounds in C. incanus material without any chemical sample treatment.

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project.

The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.

A numerical approach to evaluate the personal radiative thermal management of textiles part one: Mid-infrared transmittance, reflection and absorption

The infrared radiative heat from the human body accounts for more than 60% of dry heat loss. Textiles with mid-infrared (MIR) transparency can help achieve good radiative personal cooling and potentially save energy on building refrigeration. The interaction between MIR light and textiles is complex, involving fibre materials and yarn structure parameters and a precise computation of the optical characteristics of the textile is required. This paper presents a modelling methodology to predict the MIR transfer behaviour from human skin through a textile by simulating the radiative properties of textiles on the micro-scale. The model was validated qualitatively and quantitatively for transmittance, reflection, and absorption using FTIR data and parametric studies were carried out by varying the yarn diameter and filling factor. The results reveal that: (1) Increasing the yarn diameter results in decreased total transmittance and reflection and increased absorption for both PE and PET fabrics; (2) Increasing the yarn filling factor results in decreased total transmittance and reflection and increased absorption for both PE and PET fabrics; (3) The effect of changing the structural parameters was more dominant for commercial PET fabrics than for radiative cooling PE fabrics.

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.