Near-infrared Reflectance Spectra Research Articles

Exploring the feasibility of near-infrared spectroscopy in analyzing and predicting the chemical and biochemical changes of chitin-amended soils

Chitin, a natural polymer, has been added to soil to enhance plant resistance to pathogens, yet its impact on soil’s chemical and biochemical properties remains unclear. Standard methods to measure these properties are often slow and labor-intensive. This study aimed to assess pH, total DNA, chitinase activity, and trpB gene DNA targeting for the identification of pathogen Streptomyces scabies in chitin-treated soil using both conventional wet chemical analysis methods and near-infrared (NIR) spectroscopy for rapid measurement. Five soil groups were treated with varying chitin levels (control, 0.2% and 2% fungus chitin, 0.2% and 2% Sigma chitin) and sampled every 9 days over 122 days. NIR reflectance spectra (900-1,685 nm) from the soil samples were acquired at each sampling time. Partial least squares regression models were developed using preprocessing methods like Kubelka-Munk, second derivative, and smoothed data. The second derivative model for 900-1,660 nm yielded high predictive accuracy with R² values of 0.915, 0.892, and 0.810 for pH, total DNA, and trpB gene DNA respectively. This study demonstrates the potential of NIR spectroscopy coupled with partial least squares algorithms as a rapid, cost-effective method for estimating soil biochemical properties.

On the Lunar Origin of Near-Earth Asteroid 2024 PT5

Abstract The near-Earth asteroid (NEA) 2024 PT5 is on an Earth-like orbit that remained in Earth's immediate vicinity for several months at the end of 2024. PT5's orbit is challenging to populate with asteroids originating from the main belt and is more commonly associated with rocket bodies mistakenly identified as natural objects or with debris ejected from impacts on the Moon. We obtained visible and near-infrared reflectance spectra of PT5 with the Lowell Discovery Telescope and NASA Infrared Telescope Facility on 2024 August 16. The combined reflectance spectrum matches lunar samples but does not match any known asteroid types—it is pyroxene-rich, while asteroids of comparable spectral redness are olivine-rich. Moreover, the amount of solar radiation pressure observed on the PT5 trajectory is orders of magnitude lower than what would be expected for an artificial object. We therefore conclude that 2024 PT5 is ejecta from an impact on the Moon, thus making PT5 the second NEA suggested to be sourced from the surface of the Moon. While one object might be an outlier, two suggest that there is an underlying population to be characterized. Long-term predictions of the position of 2024 PT5 are challenging due to the slow Earth encounters characteristic of objects in these orbits. A population of near-Earth objects that are sourced by the Moon would be important to characterize for understanding how impacts work on our nearest neighbor and for identifying the source regions of asteroids and meteorites from this understudied population of objects on very Earth-like orbits.

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using near-infrared (NIR) reflectance spectra in the 940-1660 nm range. Various preprocessing techniques, including Savitzky-Golay (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. Feature wavelengths were selected to enhance model accuracy further using the Successive Projections Algorithm (SPA) and Uninformative Variable Elimination (UVE). Experimental results indicated that the PLSR model, preprocessed with 1D + MSC, yielded the best performance, achieving a root mean square error of prediction (RMSEP) of 0.3 g/kg, a correlation coefficient (Rp) of 0.93, and a residual predictive deviation (RPD) of 3. The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.

Tortuosity transformation of near-infrared diffuse reflectance spectra for the detection of enhanced and aggregate spectral changes with reference to dietary preferences of elephants

Tortuosity transformation of near-infrared diffuse reflectance spectra for the detection of enhanced and aggregate spectral changes with reference to dietary preferences of elephants

Grain Size Effects on Visible and Near-infrared (0.35–2.5 μm) Laboratory Spectra of Rare Meteorite Classes

Linking near-Earth asteroids to associated meteorites can be a challenging process for many reasons, one being grain size differences. To address this issue for rarer meteorites, we studied visible and near-infrared (0.35–2.5 μm) reflectance spectra of 11 rare meteorite classes over five different grain size bins (45–90 μm, 90–150 μm, 150–300 μm, 300–500 μm, and 500–1000 μm). We analyzed the reflectance properties, diagnostic spectral band parameters (band centers and band area ratios), spectral slope, and taxonomic classification. The spectra were analyzed using principal component analysis to detect trends in principal component (PC) space and the impact on asteroid taxonomic classification in the Bus–DeMeo system. We found that the absolute reflectance (visual albedo) at 0.55 μm (photometric V band) typically decreases with increasing grain size, although there are some variations such as sharp increases for the slabs. Our EH4 and aubrite show a trend of increasing spectral slope with decreasing grain size. Our ureilite, angrite, winonaite, acapculoite, and mesosiderite show a general trend of a decrease in Band I (∼0.9 μm) depth with increasing grain size up to 500–1000 μm. Taxonomic classification of spectra of all grain sizes shows that classification tools generally struggle to differentiate grain size effects from mineralogical variations. This research demonstrates the need for a more robust taxonomic classification system that accounts for grain size and one that accurately classifies small near-Earth asteroids with regolith-free surfaces.

In Vivo Insights: Near-Infrared Photon Sampling of Reflectance Spectra from Cranial and Extracranial Sites in Healthy Individuals and Patients with Essential Tremor

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients.

Identifying the Vertical Stratification of Sediment Samples by Visible and Near-Infrared Spectroscopy

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0–50 cm (the upper layer), 50–100 cm (the middle layer), and 100–160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR.

Low temperature phase transitions in the visible and near-infrared (VNIR) reflectance spectra of (NH4)2HPO4 and (NH4)HSO4 salts

Low temperature phase transitions in the visible and near-infrared (VNIR) reflectance spectra of (NH4)2HPO4 and (NH4)HSO4 salts

Exoland Simulator, a Laboratory Device for Reflectance Spectral Analyses of Planetary Soil Analogs: Design and Simulation

In planetary science, visible (Vis) and near-infrared (NIR) reflectance spectra allow deciphering the chemical/mineralogical composition of celestial bodies’ surfaces by comparison between remotely acquired data and laboratory references. This paper presents the design of an automated test rig named Exoland Simulator equipped with two reflectance spectrometers covering the 0.38–2.2 µm range. It is designed to collect data of natural/synthetic rocks and minerals prepared in the laboratory that simulate the composition of planetary surfaces. The structure of the test rig is conceived as a Cartesian robot to automatize the acquisition. The test rig is also tested by simulating some project trajectories, and results are presented in terms of its ability to reproduce the programmed trajectories. Furthermore, preliminary spectral data are shown to demonstrate how the soil analogs’ spectra could allow an accurate remote identification of materials, enabling the creation of libraries to study the effect of multiple chemical–physical component variations on individual spectral bands. Despite the primary scope of Exoland, it can be advantageously used also for tribological purposes, to correlate the wear behavior of soils and materials with their composition by also analyzing the wear scars.

Hygroscopic Mg–Al LDHs composite microspheres for highly efficient hyperspectral camouflage in the VIS and NIR wavebands

In order to enhance the hyperspectral camouflage efficacy of stealth coatings against a natural vegetative backdrop, LiCl, known for its significant hygroscopic properties, was incorporated into green Mg–Al layered double hydroxide (Mg–Al LDHs) material. Micron-sized composite microspheres were subsequently synthesized via the spray-drying granulation technique. The structure, morphology, and chemical composition of these microspheres were thoroughly characterized by X-ray diffraction, scanning electron microscopy, laser particle size analysis, nitrogen adsorption–desorption isotherms, and Fourier-transform infrared spectroscopy. The effect of LiCl content on the moisture absorption capacity and near-infrared reflectance spectra of the microspheres was systematically evaluated. We found that incorporating an optimal amount of LiCl into the internal pores of the Mg–Al LDHs microspheres did not compromise their smooth surface morphology and uniform particulate distribution. Notably, when the LiCl content was 10%, the maximum saturation moisture uptake ratio of the coating increased to 0.75 g/g. This hygroscopicity significantly enhanced the absorption and scattering of near-infrared radiation by the coating while concurrently improving its ability to modulate the shape and reflectance of both the visible and near-infrared spectral curves. Spectral congruence between the synthetic coating and natural green foliage was quantified at 97.41%. Moreover, this performance was maintained over 10 cycles of programmed drying and re-humidification, and the coating consistently demonstrated stable hygroscopic properties and sustained over 95% spectral congruence. These optimized artificial coatings were found to effectively confuse hyperspectral classification algorithms, thus blending seamlessly into a natural foliage backdrop. This study provides a new method for regulating VIS and NIR spectral (visible–near infrared spectrum) features, which will be critical for applications in advanced hyperspectral camouflage materials.

Geological context and significance of the clay-sulfate transition region in Mount Sharp, Gale crater, Mars: An integrated assessment based on orbiter and rover data

Abstract On Mars, phyllosilicate (“clay”) minerals are often associated with older terrains, and sulfate minerals are associated with younger terrains, and this dichotomy is taken as evidence that Mars’ surface dried up over time. Therefore, in situ investigation of the Mount Sharp strata in Gale crater, which record a shift from dominantly clay-bearing to sulfate-bearing minerals, as seen in visible–near-infrared orbital reflectance spectra, is a key science objective for the Mars Science Laboratory (MSL) Curiosity rover mission. Here, we present regional (orbiter-based) and in situ (rover-based) evidence for a low-angle erosional unconformity that separates the lacustrine and marginal lacustrine deposits of the Carolyn Shoemaker formation from the dominantly eolian deposits of the lower Mirador formation within the orbitally defined clay-sulfate transition region. The up-section record of wetter (Carolyn Shoemaker formation) to drier (lower Mirador formation) depositional conditions is accompanied by distinct changes in diagenesis. Clay minerals occur preferentially within the Carolyn Shoemaker formation and are absent within the lower members of the Mirador formation. At and above the proposed unconformity, strata are characterized by an increase in diagenetic nodules enriched in X-ray amorphous Mg-sulfate. Early clay formation in the Carolyn Shoemaker formation may have created a hydraulic barrier such that later migrating magnesium- and sulfur-rich fluids accumulated preferentially within the lower members of the Mirador formation. The proposed unconformity may have also acted as a fluid conduit to further promote Mg-sulfate nodule formation at the Carolyn Shoemaker–Mirador formation boundary. These results confirm an association of the clay-sulfate transition with the drying of depositional environments, but they also suggest that at least some orbital sulfate signatures within the region are not time-congruent with the environmental signals extracted from primary sedimentology. Our findings highlight that complex interactions among primary depositional environment, erosion, and diagenesis contribute to the transition in clay-sulfate orbital signatures observed in the stratigraphy of Mount Sharp.

Additive patterns in near-infrared diffuse reflectance spectra: Implications for product formulation and analysis

Additive patterns in near-infrared diffuse reflectance spectra: Implications for product formulation and analysis

Contribution to Characterizing the Meat Quality of Protected Designation of Origin Serrana and Preta de Montesinho Kids Using the Near-Infrared Reflectance Methodology.

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the "Serrana" and "Preta de Montesinho" breeds certified as "Cabrito Transmontano" and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the "Serrana" breed proved to be more tender than that of the "Preta de Montesinho". However, the meat from the "Preta de Montesinho" breed showed higher intramuscular fat content and was lighter than that from the "Serrana" breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard-Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R2 > 0.99.

Ba5Gd3(BO3)6F: A new alkaline earth-rare earth fluoroborate with magnetocaloric effect

Ba5Gd3(BO3)6F: A new alkaline earth-rare earth fluoroborate with magnetocaloric effect

Estimation of Cadmium Content in Lactuca sativa L. Leaves Using Visible–Near-Infrared Spectroscopy Technology

In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible–near-infrared reflectance spectra and leaf cadmium content under different concentrations of cadmium stress. A model for estimating lettuce leaf cadmium content was established. For model establishment, firstly, the original spectra were preprocessed using smoothing (Savitzky–Golay, SG), SG combined with multiplicative scatter correction (MSC), SG combined with standard normal variable transformation (SNV), SG combined with mean normalization (MN), SG combined with the first derivative (FD), SG combined with the second derivative (SD), SG combined with the baseline offset (B), and SG combined with de-trending (D). Then, the principal component analysis (PCA) was applied to perform dimensionality reduction on the data. Finally, the reduced dataset was divided into training and testing sets in a 2:1 ratio, and separate models for estimating the lettuce leaf cadmium content were built using partial least squares regression (PLSR), the backpropagation neural network (BP-NN), and support vector regression (SVR) in combination. The results showed that the accumulated cadmium content in lettuce leaves increased with an increase in the soil cadmium concentration. In the visible light range, the spectral reflectance of lettuce leaves increased with an increase in the cadmium concentration. In the near-infrared range, the spectral reflectance of the lettuce leaves under 10 mg/kg and 20 mg/kg cadmium stress was lower than that of the control group. The PLSR models established using the SG + MSC and SG + SNV preprocessing methods exhibited the strongest estimation capability for lettuce leaf cadmium content, with Rp2 and RMSEp values of 0.92 and 1.53 mg/kg, respectively, for the testing dataset. This study demonstrated that visible–near-infrared spectroscopy has great potential in monitoring cadmium contamination in lettuce.

Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

Soil organic carbon content and stock in Martinique – relations to near infrared spectra

AbstractThis data paper presents observations on soil organic carbon (SOC) gravimetric and volumetric contents (SOCg, in g kg−1, and SOCv, in g dm−3, respectively) for 98 profiles at least 30 cm deep (1 m deep for 59 of them), in rural areas of the Martinique island, in relation to soil types and land uses and management. The paper also presents particle size distribution down to 30 cm, and near infrared reflectance (NIR) spectra for the main soil types. This dataset allows evaluating the effects of land use, soil type and texture on SOC content and stock, at regional scale. It also allows inferring SOCg, SOCv and particle size distribution from NIR spectra. Such information is useful for studying and managing soils in the Martinique island and in other tropical volcanic regions.

Real-Time Monitoring of Fecal Nitrogen Excretion to the Environment Using Near-Infrared Reflectance Spectroscopy: A Preliminary Study in Rabbits

The excessive excretion of nitrogen (N) by farm animals can pose severe environmental risks. In this study, near-infrared reflectance (NIR) spectroscopy (NIRS) was used to explore the feasibility of developing a real-time in situ monitoring tool for fecal N excretion in rabbits. A total of 70 feed and 282 fecal samples from an in vivo digestibility experiment were used. Feed and fecal NIR spectra were employed to develop chemometric models using modified partial least squares (MPLS) for feed and feces, and artificial neural networks (ANN) for feces to predict dietary and fecal N content and fecal N digestibility. Very good accuracy was achieved in predicting feed N (R2val = 0.96; standard error of prediction, SEP = 0.15) and fecal N (R2val = 0.88; SEP = 0.44) content, whereas N digestibility models from wet fecal spectra had a relatively low precision (R2val = 0.70; SEP = 0.018) with MPLS methodology. In contrast, ANNs yielded more robust prediction models for fecal (R2val = 0.98; SEP = 0.25) N content and N digestibility (R2val = 0.91; SEP = 0.012) using wet feces. In conclusion, NIRS calibration with ANNs can be a suitable tool for monitoring the environmental load of N with high precision and accuracy.

Non-destructive Identification of the geographical origin of red jujube by near-infrared spectroscopy and fuzzy clustering methods

ABSTRACT The red jujube quality is closely associated with its place of origin. In order to quickly and easily identify the geographical origin of red jujube, the classification of red jujube samples’ near-infrared reflectance (NIR) spectra was performed using several fuzzy clustering methods in combination with principal component analysis (PCA) and linear discriminant analysis (LDA). Firstly, a NIR-M-R2 portable near-infrared spectrometer was used to collect four varieties of red jujube samples from four representative producing areas in four provinces: Gansu, Henan, Shanxi and Xinjiang in China. Each variety corresponded to a producing area, and it had 60 samples with a total of 240 samples. Near-infrared spectra of red jujube were acquired using a NIR-M-R2 portable near-infrared spectrometer, and the initial near-infrared spectra were preprocessed by Savitzky-Golay (SG) filtering. Secondly, PCA and LDA were used to further process the NIR data for dimension reduction and feature extraction, respectively. Finally, red jujube samples were classified by fuzzy C-means (FCM) clustering, Gustafson-Kessel (GK) clustering and possibility fuzzy C-means (PFCM) clustering. When GK served as the clustering algorithm, the clustering accuracy was the highest, as the value of 98.8%. Based on the experimental results, it was evident that the GK clustering algorithm played a significant role in identifying the place of origin of red jujube with near-infrared spectroscopy.

Spectral response to early detection of stressed oil palm seedlings using near-infrared reflectance spectra at region 900-1000 nm

Spectral response to early detection of stressed oil palm seedlings using near-infrared reflectance spectra at region 900-1000 nm