Near-infrared Reflectance Spectra Research Articles

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy.

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

Skin Detection System Using Infrared Optoelectronic Technology and Its Application in Facial Recognition

Facial recognition technology has made significant progress. However, variable lighting conditions can affect its performance. Considering the need to scan facial skin for recognition purposes, this study proposes a miniature optoelectronic acquisition system for skin in the near-infrared range. The system utilizes the C11708MA photodetector from Hamamatsu Photonics’ MS series as the probe for spectral data acquisition. Other hardware components are designed accordingly. A three-stage amplification buffer circuit is employed as the front-end acquisition and preprocessing circuit. The AD7671 chip from Analog Devices Inc. is selected as the AD converter, and the communication module utilizes the CY7C68013 chip from Cypress’ EZ-USB FX2 series. The control and transmission module employs the EP2C5T144C8N FPGA chip from ALTERA’s Cyclone II generation. In order to address the power supply requirements of the CY7C68013 USB chip (3.3 V), the FPGA core (1.2 V), and the AD7671 and front-end preprocessing circuit (5 V), AMS1117 voltage regulator chips are designed for stable 5 V–1.2 V and 5 V–3.3 V power supplies. In the experiments, wavelength calibration and spectral preprocessing are performed on the system prior to data processing. Near-infrared reflectance spectra of different skin conditions (melanoma, vitiligo) are compared with normal skin. The results demonstrate the accurate assessment capability of the designed infrared optoelectronic skin detection system. Facial skin data obtained from the system are used to generate facial images, and the recognition performance of different detection systems is compared in an algorithmic environment, thereby demonstrating the promising application prospects of the infrared optoelectronic skin detection system in the field of facial recognition.

Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands.

Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0-20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350-2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation (RPD) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction (R2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy (R2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra (R2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR (R2 and RMSE: 0.92 and 1.91 g/kg in validation set, RPD: 3.23). The optimal SI-based model was model SI3-SVR (R2 and RMSE: 0.87 and 2.40 g/kg in validation set, RPD: 2.57) and model SI-SVR (R2 and RMSE: 0.84 and 2.63 g/kg in validation set, RPD: 2.35).

Reflectance spectra of mascagnite and salammoniac minerals with varying viewing geometry

We analyse the quantitative effects of viewing geometry variations on the near-infrared reflectance spectra of mascagnite-(NH4)2SO4 and salammoniac-NH4Cl samples. Bi-directional reflectance spectra are collected in the 1–4.2 μm range considering a set of 3 incidence (i) angles (i = 0°; 30°; 60°) and 9 emergence (e) angles between −70° and 70° at room temperature and computed with respect to the normal direction. The NH4+ overtone and combinations bands located at ∼1.09, 1.32, 1.62, 2.04, 2.2 and 3.05 μm are experimentally investigated. The bidirectional reflectance spectra of these samples show significant variations with the observation geometry in terms of reflectance values, spectral slope, and absorption bands parameters. The band positions remain essentially unchanged by varying the incident and emergence angles. On the other hand, bands' area and depth show the highest variability for i ≥ 30° and e greater than ±40°(up to a factor 2.3 in relative mean variation). The area and depth parameters of these bands show a dual behaviour: (i) for the weak-medium spectral features below 2 μm the area and depth decrease as the phase angle increases. (ii) The strong spectral features above 2 μm increase their values only at phase angles above 90°, but also at low phase angles for high incidences, i ≥ 30°. This behaviour is linked both to the non-linear radiative transfer in particulate media and to the way the band depth and area are defined, relative to the local continuum. We observe important dependence (up to ∼60% relative mean variation) of band depth and area on the incidence angle, up to 60°, compared to moderate variation with emergence angles (up to ∼20% relative mean variation). Furthermore, the ∼3 μm features becomes more saturated at ±70° emergence angles. A general trend of spectral bluing with change in observation geometry is observed. The current dataset is a contribution in the framework of present and future space missions focused on understanding the nature and quantification of ammonium-bearing minerals on icy bodies. The NH4+ − bearing minerals identification could provide information on: (i) ocean/brine compositions, (ii) possible explanations of geological phenomena and (iii) implications for biological activity.

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

The growth quality of Pinus massoniana (Lamb.) seedlings is closely related to the survival rate of afforestation. Moisture content detection is an important indicator in the cultivation of forest seedlings because it can directly reflect the adaptability and growth potential of the seedlings to the soil environment. To improve the accuracy of quantitative analysis of moisture content in P. massoniana seedlings using near-infrared spectroscopy, a total of 100 P. massoniana seedlings were collected, and their near-infrared diffuse reflectance spectra were measured in the range of 2500 to 800 nm (12,000 to 4000 cm−1). An integrated learning framework was introduced, and a quantitative detection model for moisture content in P. massoniana seedlings was established by combining preprocessing and feature wavelength selection methods in chemometrics. Our results showed that the information carried by the spectra after multiple scattering correction (MSC) preprocessing had a good response to the target attribute. The stacking learning model based on the full-band spectrum had a prediction coefficient of determination R2 of 0.8819, and the prediction accuracy of moisture content in P. massoniana seedlings could be significantly improved compared to the single model. After variable selection, the spectrum processed by MSC and feature selection with uninformative variable elimination (UVE) showed good prediction effects in all models. Additionally, the prediction coefficient of determination R2 of the support vector regression (SVR)—adaptive boosting (AdaBoost)—partial least squares regression (PLSR) + AdaBoost model reached 0.9430. This indicates that the quantitative analysis model of moisture content in P. massoniana seedlings established through preprocessing, feature selection, and stacking learning models can achieve high accuracy in predicting moisture content in P. massoniana seedlings. This model can provide a feasible technical reference for the precision cultivation of P. massoniana seedlings.

Visible to near-infrared reflectance and Raman spectra of evaporites from sulfate-chloride Mars analogue brines

While much attention has been given to the identification and characterization of single-phase salts on Mars, relatively little has been applied to mixed evaporative assemblages. Given the likely existence of these assemblages on Mars (e.g., basin deposits) and the lack of data on their spectral signatures, here we present an experimental study of multicomponent S and Cl-bearing Mars-relevant brines. We modeled, synthesized and evaporated brines in the laboratory under both martian and terrestrial (P, T, pCO2) environmental conditions, and characterized the resulting precipitates using Visible–Near Infrared (VNIR), Raman spectroscopy, XRD and SEM-EDS. We compared these results to mineral assemblages calculated using the FREZCHEM thermodynamic model. For mixed brines primarily containing Na+, K+, Mg2+, Ca2+, SO42− and Cl−, epsomite (MgSO4•7H2O) and bischofite (MgCl2•6H2O) overwhelm VNIR and Raman spectra, while anhydrous crystalline salts and Na- and K-sulfates are unidentified. Mg-sulfates are identifiable in the VNIR and Raman even at low or no modeled mass abundance in an evaporative assemblage and are often the only clearly identifiable salt. These results imply that regions of Mg-sulfate identification on Mars may have only minor amounts of Mg-sulfate present, and significant amounts of halides or other sulfates may be undetectable. This may be due to a combination of late-stage Mg-sulfate precipitation and non-linear spectral mixing. Raman is more sensitive than VNIR to the identification of Ca-sulfate salts in these mixed assemblages. We predict that high abundances of mixed chloride and sulfate salts species will be identified as the Curiosity Rover continues to explore the Sulfate Unit of Gale Crater, and note that the Perseverance rover offers the first opportunity to identify such mixed assemblages in Jezero Crater with this combination of techniques.

AKTeO2(CO3) (A = Li, Na): The First Carbonatotellurites Featuring a Zero-Dimensional [Te2C2O10]4– Cluster and a Wide Band Gap

The first carbonatotellurites, AKTeO2(CO3) (A = Li, Na), have been successfully synthesized by using boric acid as the mineralizer. AKTeO2(CO3) (A = Li, Na) crystallize in the monoclinic space group P21/n (no. 14), and their structures exhibit the novel zero-dimensional (0D) [Te2C2O10]4- clusters, in which two [TeO4]4- groups form a [Te2O6]4- dimer via edge-sharing, with each side of the dimer attached by a [CO3]2- group via a Te-O-C bridge. The alkali metal cations occupy the voids between the 0D clusters and maintain the charge balance. The ultraviolet-visible-near-infrared diffuse reflectance spectra show that the short absorption cut-off edges of LiKTeO2(CO3) (LKTC) and NaKTeO2(CO3) (NKTC) are 248 and 240 nm, respectively, and LKTC exhibits the largest experimental band gap (4.58 eV) among all of the tellurites containing the π-conjugated anionic groups reported. Theoretical calculations revealed that they exhibit moderate birefringences of 0.029 and 0.040@1064 nm, respectively.

Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis.

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

Compositional indication of E- and M-type asteroids by VIS-NIR reflectance spectra of meteorites

Context. E-type asteroids have been linked to aubrites, while M-type asteroids have been linked to enstatite chondrites (ECs) and iron meteorites (IMs). However, as ECs and IMs generally lack absorption characteristics, distinguishing their parent bodies by spectroscopy generally poses a challenge. Aims. We aim to develop a method to distinguish two kinds of M-type asteroids, the parent bodies of ECs and IMs, and to infer their composition. Methods. We measured the visible to near-infrared (VIS-NIR) reflectance spectra of aubrite, ECs, and IMs. Then we analyzed and compared their spectral parameters, such as the reflectance at 0.55 µm (R0.55), absorption bands, and spectral slopes. We also compared the geometric albedo and spectral slopes of a total of 13 E-type and 14 M-type asteroids. Furthermore, combining the collected radar albedo and density data of M-type asteroids, we discuss their potential composition at different depths. Results. We find that for most meteorites, with the exception of very weak absorption in an aubrite and an EH7 chondrite, ECs and IMs do not show any absorption characteristics. Aubrite shows extremely high reflectance and a negative near-infrared slope (NIRS) and ECs show relatively low reflectance and moderately positive NIRS, while IMs show relatively moderate reflectance and the steepest positive NIRS. Two diagrams plotting with R0.55 and NIRS calculated in the 1.1–1.2 µm and 1.1–1.4 µm bands were subsequently shown to perform optimally at distinguishing aubrite, ECs, and IMs. In addition, M-type asteroids have a wide range of NIRS and diverse radar albedo and densities, whereby 16 Psyche shows high NIRS, radar albedo, and density, while 21 Lutetia is dominated by low values for these parameters. Conclusions. We demonstrate that NIRS is correlated with metal content and increases with metal content. In particular, the NIRS calculated in the 1.1–1.4 µm band is a potentially useful parameter for inferring the surface metal content of E- and M-type asteroids. Based on our results, we suggest that the featureless M-type asteroids ought to be divided into two subtypes: Mm- (e.g., 16 Psyche) and Me-type (e.g., 21 Lutetia) in the aim of characterizing the sources of IMs and ECs, respectively.

Quantifying the effects of submicroscopic metallic iron on VIS–NIR spectra of lunar soils

Metallic iron (Fe0) particles with sizes ranging from a few nanometers to the submicroscopic scale and formed by space weathering are specific components of lunar soil. Previous studies have suggested that the iron significantly alters the optical properties of lunar soil. For example, nanophase metallic iron (npFe0) causes both reddening and darkening of the lunar soil spectrum, and submicroscopic metallic iron (SMFe) only causes darkening. Here, we prepared SMFe particles with an average size of ∼180 nm embedded within melt glasses through carbothermal reduction experiments to analogize agglutinated glasses in the lunar soil. We evaluated the effect of SMFe content on visible and near-infrared (VIS–NIR) reflectance spectra of these lunar soil samples simulants. The spectral data show that SMFe content plays a key role in the optical properties of samples, including the average reflectance in the VIS-NIR range (400–2150 nm), and the absorption depth at ∼2 μm. A small amount (0.05 wt%) of SMFe mainly causes significant spectral darkening, and the average reflectance is reduced by 50% when the SMFe content rises to 0.36 wt%. Both the average reflectance and the absorption depth at ∼2 μm show a negative correlation with the SMFe content. We developed a quantitative model relating the spectral characteristics and the SMFe abundance based on experimental results. Thus, the SMFe contents play a key role in altering spectral characteristics of airless bodies during remote sensing spectroscopic detection.

Effects of Temperature on Visible and Infrared Spectra of Mercury Minerals Analogues

Mercury’s peculiar orbit around the Sun (3:2 spin–orbit resonance) and lack of atmosphere result in one the widest temperature ranges experienced at the surface of a planetary body in the solar system. Temperature variations affect the physical and, therefore, spectral properties of minerals to varying degrees; thus, it is crucial to study them in the context of the upcoming arrival of the BepiColombo spacecraft in Mercury orbit in the fall of 2025. In this work, we heated and cooled analog materials (plagioclase and volcanic glasses) at temperatures representative of the hermean surface. With our experimental setup, we could measure near-infrared (1.0–3.5 μm) and thermal infrared (2.0–14.3 μm) reflectance spectra of our analogs at various temperatures during a heating (25–400 ∘C) or cooling cycle (−125–25 ∘C), allowing us to follow the evolution of the spectral properties of minerals. We also collected reflectance spectra in the visible domain (0.47–14.3 μm) before and after heating. In the visible spectra, we identified irreversible changes in the spectral slope (reddening) and the reflectance (darkening or brightening) that are possibly associated with oxidation, whereas the temperature had reversible effects (e.g., band shifts of from ten to a hundred nanometers towards greater wavelengths) on the infrared spectral features of our samples. These reversible changes are likely caused by the crystal lattice dilatation during heating. Finally, we took advantage of the water and ice present on/in our samples to study the different components of the absorption band at 3.0 μm when varying temperatures, which may be useful as a complement to future observations of the north pole of Mercury. The wavelength ranges covered by our measurements are of interest for the SIMBIO-SYS and MERTIS instruments, which will map the mineralogy of Mercury’s surface from spring 2026, and for which we selected useful spectral parameters that are proxies of surface temperature variations.

Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict the Nutritive Value of Undried Ensiled Forage.

Prediction models of different types of forage were developed using a dataset of near-infrared reflectance spectra collected by three handheld NeoSpectra-Scanners and laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), Ash, and moisture content (MO) from a total of 555 undried ensiled corn, grass, and alfalfa samples. Data analyses and results of models developed in this study indicated that the scanning method significantly impacted the accuracy of the prediction of forage constituents, and using the NEO instrument with the sliding method improved calibration model performance (p < 0.05) for nearly all constituents. In general, poorer-performing models were more impacted by instrument-to-instrument variability. The exception, however, was moisture content (p = 0.02), where the validation set with an independent instrument resulted in an RMSEP of 2.39 compared to 1.44 where the same instruments were used for both calibration and validation. Validation model performance for NDF, IVTD, NDFD, ADL, ADF, Ash, CP, and moisture content were 4.18, 3.86, 6.14, 1.10, 2.75, 1.42, 2.71, and 1.67 for alfalfa-grass silage samples and 3.22, 2.21, 4.55, 0.38, 2.07, 0.50, 0.51, and 1.62 for corn silage, respectively. Based on the results of this study, the handheld spectrometer would be useful for predicting moisture content in undried and unground alfalfa-grass (R2 = 0.97) and corn (R2 = 0.93) forage samples.

Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN.

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.

Visible to Near-Infrared Reflectance Spectroscopy of Asteroid (16) Psyche: Implications for the Psyche Mission's Science Investigations.

The NASA Psyche mission will explore the structure, composition, and other properties of asteroid (16) Psyche to test hypotheses about its formation. Variations in radar reflectivity, density, thermal inertia, and visible to near-infrared (VNIR) reflectance spectra of Psyche suggest a highly metallic composition with mafic silicate minerals (e.g., pyroxene) heterogeneously distributed on the surface in low abundance (<10 vol.%). The Psyche spacecraft's Multispectral Imager is designed to map ≥80% of the surface at high spatial resolution (≤20m/pixel) through a panchromatic filter and provide compositional information for about ≥80% of the surface using seven narrowband filters at VNIR wavelengths (∼400-1,100nm) and at spatial scales of ≤500m/pixel. We analyzed 359 reflectance spectra from samples consistent with current uncertainties in Psyche's composition and compared them to published reflectance spectra of the asteroid using a chi-square test for goodness of fit. The best matches for Psyche include iron meteorite powder, powders from the sulfide minerals troilite and pentlandite, and powder from the CH/CBb chondrite Isheyevo. Comparison of absorption features support the interpretation that Psyche's surface is a metal-silicate mixture, although the exact abundance and chemistry of the silicate component remains poorly constrained. We convolve our spectra to the Imager's spectral throughput to demonstrate preliminary strategies for mapping the surface composition of the asteroid using filter ratios and reconstructed band parameters. Our results provide predictions of the kinds of surface compositional information that the Psyche mission could reveal on the solar system's largest M-type asteroid.

Visible and near-infrared reflectance spectra of igneous rocks and their powders

Most solid planetary bodies in the solar system are covered by a layer of fine particles and the topic of light scattering by small particles has been thoroughly studied in the past decades. In contrast, light reflection from intact rocks has received much less attention, though the spectral features of fresh rocks are more diagnostic than that of highly space-weathered regolith grains. As high spatial-resolution spectral images obtained by modern space-borne and in-situ sensors have become available, it is important to understand the spectral feature links between rocks and powders made by crushing the rocks. In this work, we selected 13 terrestrial igneous rocks with a 1 μm absorption feature and measured the visible and near-infrared reflectance spectra of their slabs and powders in three size fractions, 0-45 μm, 90-125 μm, and 450-900 μm. We have found that the spectral characteristics of these samples can be divided into two groups. For slabs with reflectance lower than 0.1 at 0.5 μm, they have less pronounced 1 μm absorption feature. For slabs with reflectance higher than 0.1, they have pronounced 1 μm feature, consistent with that of their powdered counterparts. By using the equivalent-slab and the Hapke model, we obtained the optical constants and single scattering albedo values of the samples. The dependence of single scattering albedo on effective absorption thickness indicates that the differences between the spectral characteristics of rock slabs and powdered samples are likely controlled by the degree of weak surface scattering contributions. We reconstructed the spectrum of a powdered lunar meteorite which best matches the Chang'E-4 rock and found that the reconstructed rock spectra are very close to the rock spectrum observed in-situ by Chang'E-4.

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants.

Simple SummaryMethane is a greenhouse gas and its emissions contribute to global warming. Domestic farmed ruminants are one of the major contributors to anthropogenic methane emissions. Feed consumed by ruminants produces methane when fermented by the rumen microbiota. Thus, feed chemical composition could influence the amount of methane produced per unit of feed eaten (i.e., methane yield). Near-infrared reflectance spectroscopy (NIRS) is commonly used to estimate feed chemical composition by correlating dietary constituents against features of the near-infrared reflectance (NIR) spectrum of the feed. Thus, NIRS might be able to predict methane yield. Feed samples collected from sheep and cattle experiments in which methane was measured were scanned for NIR spectra. These spectra and methane data were used to establish prediction models. The modeling results suggested that 53% of the variation in methane yield can be predicted using NIRS. The accuracy of the prediction is modest, but it could be still useful for screening low methane feeds. To increase the accuracy of the prediction, we recommend that more data from animal experiments with measurements of methane emissions are included in the databases for NIRS calibrations and alternative algorithm methods and combination of other techniques to NIRS should be explored.Feed chemical composition is associated with methane (CH4) formation in the rumen, and thus CH4 yields (Ym; CH4 emitted from per unit of dry matter intake) could be predicted using near-infrared reflectance spectroscopy (NIRS) of feeds fed to ruminants. Two databases of NIRS data were compiled from feeds used in experiments in which CH4 yields had been quantified in respiration chambers. Each record in the databases represented a batch of feed offered to a group of experimental animals and the mean CH4 yield for the group. A near-infrared reflectance spectrum was obtained from each feed, and these spectra were used to generate a predictive equation for Ym. The predictive model generated from brassica crops and pasture fed at a similar feeding level (n = 40 records) explained 53% of the variation in Ym and had a reasonably good agreement (concordance correlation coefficient of 0.77). The predictive ability of the NIRS calibration could be useful for screening purposes, particularly for predicting the potential Ym of multiple feeds or feed samples, rather than measuring Ym in animal experiments at high expenses. It is recommended that the databases for NIRS calibrations are expanded by collecting feed information from future experiments in which methane emissions are measured, using alternative algorithms and combining other techniques, such as terahertz time-domain spectroscopy.

Proximal Soil Sensing of Low Salinity in Southern Xinjiang, China

Measuring the soil salinity using visible and near-infrared (vis–NIR) reflectance spectra is considered a fast and cost-effective method. For monitoring purposes, estimating soils with low salinity measured as electrical conductivity (EC) using vis–NIR spectra is still understudied. In this research, 399 legacy soil samples from six regions of Southern Xinjiang, China with low EC values were used. Reflectance spectra were measured in the laboratory on dried and ground soil samples using a portable vis–NIR spectrometer. By using 10-fold cross-validation, three algorithms–partial least-squares regression (PLSR), random forest (RF), and Cubist–were employed to develop statistical models of EC. The model performance evaluation was obtained by the relative importance of variants. In terms of accuracy assessment of soil EC prediction, the results demonstrated that the Cubist model performed better (R2 = 0.67, RMSE = 0.16 mS/cm, RPIQ = 2.28) than both PLSR and RF. Despite similar variants for modelling, the RF model performed somewhat better than that of the PLSR. Additionally, the 610 nm and 790 nm wavelengths only demonstrated significant promise for predicting low soil EC values when used in the Cubist mode. The current research recommends the use of Cubist to estimate the low soil salinity using the vis–NIR reflectance spectra.

Design and test of portable comprehensive quality non-destructive detector for grape bunches based on spectrum

Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in growing period. The detector includes spectrum acquisition probe, spectrometer, lithium battery, halogen lamp light source, ARM board and peripheral circuit. Based on MFC development tool, the real-time analysis and processing software of the detector is written by C++ language. The optimal partial least squares regression (PLSR) detection model of multi-quality parameters was implanted into the hardware device. This paper selects the red globe grape bunches in the growth period as the research samples, collects the visible / near-infrared diffuse reflectance spectrum information, and then uses the established PLSR model to detect the Soluble solid content(SSC), Total acid (TA) and pH of the samples to generate comprehensive quality parameters. So as to realize the nondestructive testing of multiple quality and comprehensive quality parameter of red globe grape bunches in the growth period. In conclusion, the detector can realize real-time and non-destructive detecting of red globe grape bunches in growth period aiming at the multiple quality and comprehensive quality. Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth period. The detector included spectrum acquisition probe, spectrometer, lithium battery, halogen lamp light source, advanced RISC machines (ARM) board and peripheral circuit. Based on microsoft foundation classes (MFC) development tool, the real-time analysis and processing software of the detector was written by C++ language. The optimal partial least squares regression (PLSR) detection model of multi-quality parameters was implanted into the hardware device. This paper selected the red globe grapes bunches in the growth period as the research samples, collected the visible/near infrared diffuse reflectance spectrum information, and then used the established PLSR model to detect the soluble solid content (SSC), total acid (TA) and pH of the samples to generate comprehensive quality parameter. So as to realize the nondestructive detecting of comprehensive quality of red globe grapes bunches in the growth period. In conclusion, the detector could realize real-time and non-destructive detecting of red globe grapes bunches in growth period aiming at the comprehensive quality.

PCViT: A Pre-Convolutional ViT Coal Gangue Identification Method

For the study of coal and gangue identification using near-infrared reflection spectroscopy, samples of anthracite coal and gangue with similar appearances were collected, and different dust concentrations (200 ug/m3, 500 ug/m3 and 800 ug/m3), detection distances (1.2 m, 1.5 m and 1.8 m) and mixing gangue rates (one-third coal, two-thirds coal, full coal) were collected in the laboratory by the reflection spectroscopy acquisition device and the gangue reflection spectral data. The spectral data were pre-processed using three methods, first-order differentiation, second-order differentiation and standard normal variable transformation, in order to enhance the absorption characteristics of the reflectance spectra and to eliminate the effects of changes in the experimental environment. The PCViT gangue identification model is established, and the disadvantages of the violent patch embedding of the ViT model are improved by using the stepwise convolution operation to extract features. Then, the interdependence of the features of the hyperspectral data is modeled by the self-attention module, and the learned features are optimized adaptively. The results of gangue recognition under nine working conditions show that the proposed recognition model can significantly improve the recognition accuracy, and this study can provide a reference value for gangue recognition using the near-infrared reflection spectra of gangue.

Monitoring of Nitrogen Transport Data in Pear Leaves Based on Infrared Spectroscopy

In order to better monitor the data of nitrogen transport in pear leaves, a method based on infrared spectroscopy was proposed. The near-infrared reflection spectrum imaging technology is used to collect the leaf scale spectral image of the target crop. Computer image analysis software is used to process the spectral digital image and extract the spectral data. After statistical analysis, the data are selected as variables. Combined with the chemical analysis test results, the crop nutrition detection model is established, and the conclusion is drawn. The experimental results show that the band gray data involved in the model are scaled and reorganized according to the coefficient proportion by using ENVI through the band calculation command. The final gray image, the original image, and the gray image in the process default to the three-channel analog image of the band (the wavelengths of the bands are 1446, 1373, and 1304 nm, respectively); 944 nm gray image; 1043 nm gray scale image; 1662 nm gray image; (0.102R944 +0.103R1 043 +0.206R1662)/(0.102 + 0.103 + 0.206) grayscale image with signal scaling according to the scale of model coefficient. It is proved that infrared spectroscopy can effectively monitor the data of nitrogen transport in pear leaves.