Visible And Near-infrared Spectra Research Articles

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380–1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops’ health, thus minimizing crop loss and optimizing yields.

Estimation of soil organic matter by in situ Vis-NIR spectroscopy using an automatically optimized hybrid model of convolutional neural network and long short-term memory network

It is attractive nowadays to estimate soil information based on in situ Visible and Near-infrared (Vis-NIR) spectroscopy. However, there exist a lot of errors, mainly due to modeling between soil properties and spectra. A hybrid of deep learning (DL) models seems an ideal approach for the modeling, e.g., convolutional neural network (CNN) and long short-term memory (LSTM). Nevertheless, it is not easy for a user, or not efficient for an automatic technique, such as Bayesian optimization (BO), to select hundreds of parameters of a hybrid of DL models. Recently, tree Parzen Estimator (TPE) based HyperBand within BO (BOHB) has been widely used in other fields for establishing a hybrid of DL models efficiently and automatically. Further, it is not quite clear if a DL model or a hybrid DL model performs well for different sample sizes and different numbers of spectral data. This study aimed to investigate these issues by evaluating performances of two hybrids of DL models (CNN-LSTM and LSTM-CNN) established using BOHB, based on soil spectra and organic matter contents of 670 soil samples collected from a forest in Nanning, southwest China. The performances were also compared with those of separate DL models and two commonly used methods, i.e., partial least square regression (PLSR) and random forest (RF), while the effect of sample sizes on the performances was also investigated. Besides, the effect of different numbers of spectra was also investigated, and the spectra were augmented using two methods, one by stacking scans of all measurement points of a sample and one by stacking preprocessed spectra using several methods. Results showed that, given less than 600 samples, accuracies of CNN-LSTM and LSTM-CNN were not higher than those of separate DL models, PLSR and RF. However, BOHB optimization largely improved the accuracies of the two hybrid models which were close to or higher than those of separate DL models, PLSR and RF. With the increase of sample sizes, accuracies of all models used in this study gradually increased and the DL models seemed to be more promising for large sample sizes. Furthermore, augmenting spectra with preprocessed data provided much more benefits than sample sizes, modeling methods, and optimization methods. Thus, it is promising to estimate soil using in situ Vis-NIR spectra based on a hybrid of DL models optimized using BOHB, particularly for large datasets or augmented spectra.

Custom Hyperspectral Imaging System Reveals Unique Spectral Signatures of Heart, Kidney, and Liver Tissues

The rapid advancement of diagnostic and therapeutic optical techniques for oncology demands a good understanding of the optical properties of biological tissues. This study explores the capabilities of hyperspectral (HS) cameras as a non-invasive and non-contact optical imaging system to distinguish and highlight spectral differences inbiological soft tissuesof three structures (kidney, heart, and liver) for use inendoscopic interventionoropen surgery. The study presents an optical system consisting of two individual setups, the transmission setup, and the reflection setup, both incorporating anHS camerawith apolychromatic light sourcewithin the range of 380 to 1050 nm to measure tissue's light transmission (Tr) and diffuse light reflectance (Rd), respectively. The optical system was calibrated with a customizedliquid optical phantom, then 30 samples from various organs were investigated fortissue characterizationby measuring both Tr and Rd at the visible and near infrared (VIS-NIR) band. We exploited the ANOVA test, subsequently by a Tukey's test on the created three independent clusters (kidney vs. heart: group I / kidney vs. liver: group II / heart vs. liver: group III) to identify the optimum wavelength for each tissue regarding their spectroscopic optical properties in the VIS-NIR spectrum. The optimum spectral span for the determined light Tr of the three groups was 640 ∼ 680 nm, and the ideal range was 720 ∼ 760 nm for the measured light Rd for mutual group I and group II. However, the group III range was different at a range of 520 ∼ 560 nm. Therefore, the investigation provides vital information concerning theoptimum spectral scalefor the computed light Tr and Rd of the investigatedbiological tissues(kidney, liver, and heart) to be employed in diagnostic andtherapeutic medical applications.

Universal visible emitters in nanoscale integrated photonics

Visible wavelengths of light control the quantum matter of atoms and molecules and are foundational for quantum technologies, including computers, sensors, and clocks. The development of visible integrated photonics opens the possibility for scalable circuits with complex functionalities, advancing both science and technology frontiers. We experimentally demonstrate an inverse design approach based on the superposition of guided mode sources, allowing the generation and complete control of free-space radiation directly from within a single 150 nm layer , showing low loss across visible and near-infrared spectra. We generate diverging circularly polarized beams at the challenging 461 nm wavelength that can be directly used for magneto-optical traps of strontium atoms, constituting a fundamental building block for a range of atomic-physics-based quantum technologies. Our generated topological vortex beams and the potential for spatially varying polarization emitters could open unexplored light–matter interaction pathways, enabling a broad new photonic–atomic paradigm. Our platform highlights the generalizability of nanoscale devices for visible-laser emission and will be critical for scaling quantum technologies.

In Situ VIS-NIR Spectroscopy for a Basic and Rapid Soil Investigation

Visible and near-infrared (VIS-NIR) spectroscopy is extensively used in the field of soil science to predict several soil properties, mostly in laboratory conditions. When measured in situ, contact probes are used, and, very often, time-consuming methods are applied to generate better spectra. Unfortunately, spectra obtained by these methods differ greatly from spectra remotely acquired. This study tried to address this issue by measuring reflectance spectra directly with a fibre optic or a 4° lens on bare untouched soils. C, N content and soil texture (sand, silt, and clay) prediction models were established using partial least-square (PLS) and support vector machine (SVM) regression. With spectral pre-processing, some satisfactory models were obtained, i.e., for C content (R2 = 0.57; RMSE = 0.09%) and for N content (R2 = 0.53; RMSE = 0.02%). Some models were improved when using moisture and temperature as auxiliary data for the modelling. Maps of C, N and clay content generated with laboratory and predicted values were presented. Based on this study, VIS-NIR spectra acquired with bare fibre optic and/or a 4° lens could be used to build prediction models in order to obtain basic preliminary information on soil composition at the field scale. The predicting maps seem suitable for a fast but rough field screening.

Application of portable Vis-NIR spectroscopy for rapid detection of myoglobin in frozen pork

Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (Rp) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork.

Enhancement of plasmonic photovoltaics with pyramidal nanoparticles.

Light trapping as a result of embedding plasmonic nanoparticles (NPs) into photovoltaics (PVs) has been recently used to achieve better optical performance compared to conventional PVs. This light trapping technique enhances the efficiency of PVs by confining incident light into hot-spot field regions around NPs, which have higher absorption, and thus more enhancement of the photocurrent. This research aims to study the impact of embedding metallic pyramidal-shaped NPs inside the PV's active region to enhance the efficiency of plasmonic silicon PVs. The optical properties of pyramidal-shaped NPs in visible and near-infrared spectra have been investigated. The light absorption into silicon PV is significantly enhanced by embedding periodic arrays of pyramidal NPs in the cell compared to the case of bare silicon PV. Furthermore, the effects of varying the pyramidal-shaped NP dimensions on the absorption enhancement are studied. In addition, a sensitivity analysis has been performed, which helps in identifying the allowed fabrication tolerance for each geometrical dimension. The performance of the proposed pyramidal NP is compared with other frequently used shapes, such as cylinders, cones, and hemispheres. Poisson's and Carrier's continuity equations are formulated and solved for the current density-voltage characteristics associated with embedded pyramidal NPs with different dimensions. The optimized array of pyramidal NPs provides an enhancement of 41% in the generated current density when compared to the bare silicon cell.

Predicting key soil properties from Vis-NIR spectra by applying dual-wavelength indices transformations and stacking machine learning approaches

Soil visible and near-infrared (Vis-NIR) spectroscopy has been widely used for soil characterization, due to its high potential for assessing different soil properties. However, despite great successes achieved by applying the method, there is a potential for further improvement, especially regarding data (pre-) processing and modeling. In this study, we employ simple ratio indices (SRI) and normalized difference indices (NDI), calculated from all combinations of different wavebands of Vis-NIR spectra, for improving the prediction of key soil properties such as calcium carbonate content (CaCO3), cation exchange capacity (CEC), clay content (Clay), sand content (Sand), nitrogen content (N), organic carbon content (OC), and pH in CaCl2 (pH). For this, we use the topsoil physicochemical and multispectral reflectance data from the Land Use/Land Cover Area Frame Survey (LUCAS) dataset. Various machine learning techniques were employed for the modeling, including partial least squares regression (PLSR), ridge regression, principal component regression (PCR), and two stacked algorithms. Based on the results obtained, both the SRI and NDI transformations significantly improved the performance of all the models compared to the untransformed data. However, SRI was superior to NDI. The stacked model PCR-poly applied on the SRI data yielded the best prediction performances for all the soil properties. The model gave RMSE (R2) values of 25.71 (0.96), 6.89 (0.80), 5.41 (0.82), 1.11 (0.92), 21.34 (0.95), 0.35 (0.94) and 13.41 (0.73) for estimation of CaCO3, CEC, Clay, N, OC, pH and sand, respectively. The findings of this study showed that applying the SRI and NDI transformations on Vis-NIR data and employing stacked models for calibration, yield performances for soil properties prediction that are similar to those obtained by using deep learning based methods on the same dataset in previous works, while being computationally less demanding.

Reliable spectroscopic identification of minerals associated with serpentinization: Relevance to Mars exploration

Mars has become the preeminent target of astrobiology due to its many Earth-like features. Serpentinized environments on Mars are increasingly of astrobiological interest because they imply the presence of several of the “key elements” for life. The Mars 2020 rover carries a compelling set of spectral instruments with the intent to characterize past habitable serpentinized environments, search for potential biosignatures, and collect samples for potential return to Earth. Reliable spectroscopic identification of serpentinization minerals is, of course, a prerequisite for mission accomplishment. The current assignment of spectroscopic features is based on the databases derived from pure minerals. However, many studies have confirmed that mineral assemblage can complicate spectrum identification, often leading to misinterpretation of the data. Therefore, a rock-based library should be built, which will increase our capability to interpret the Martian spectroscopic data. As such, we performed a comprehensive mineralogical and spectroscopic survey of several rocks sampled from an ophiolite complex in Qaidam Basin, one of the largest Mars analogs on Earth, to build an ophiolite spectral database. X-ray fluorescence (XRF), visible and near-infrared (VNIR), Raman spectroscopy, and XRD were used to identify minerals in the rocks. The results show that serpentine in the rocks with talc could be misinterpreted as sepiolite only relying on the Raman vibrations, while the VNIR spectra can identify serpentine well in all rocks. In addition, the camera and Raman spectrometer on the Mars rover should work together to identify different polymorphs of serpentine, i.e., antigorite, lizardite, and chrysotile. Raman and/or VNIR spectroscopy is effective for other minerals associated with serpentinization, including brucite, dolomite, magnesite, magnetite, talc, and quartz. Our study provides a framework for detecting serpentinization minerals on Mars with spectrometers and can be used for data interpretation by the Mars 2020 mission. All the spectral data presented in the supplementary material facilitate further comparison with future in situ and orbital measurements on Mars.

Reducing Moisture Effects on Soil Organic Carbon Content Estimation in Vis-NIR Spectra With a Deep Learning Algorithm

When estimating soil organic carbon (SOC) using visible and near-infrared (Vis-NIR) spectra measured in situ, the interference of soil moisture content (SMC) needs to be eliminated. The existing SMC removal methods are mainly based on spectral transformation, but they change the original form of the soil spectrum. In this paper, a new deep learning-based SMC influence removal network (MIRNet) is proposed to establish the relationship between the spectra of moist soil and that of dry soil. This method constructs a spectral extraction module (SEM) with two one-dimensional (1-D) ghost modules to extract soil spectral characteristics and a context extraction module (CEM) with a two-layer dilated convolutional neural network (DiCNN) to extract the context information of the spectra. Then these extracted features are combined to reconstruct the SMC influence with a two-layer deconvolution using residual learning (Res). Finally, a new loss function that combining spectral distance and spectral shape measurement (D-S loss) is proposed. The input of MIRNet is the moist soil spectra, and the output is the dry soil spectra. Black soil collected from Harbin and yellow-brown soil collected from Nanjing are selected as the research objects. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> reaches 0.703, 0.747, 0.907, 0.892, 0.866, 0.907, and 0.926, respectively, when using spectra processed by external parameter orthogonalization (EPO), orthogonal signal correction (OSC), support vector regression (SVR), convolutional neural network (CNN), deep neural network (DNN), denoising convolutional neural network (DnCNN), and MIRNet. Therefore, the proposed MIRNet achieves competitive results compared with these state-of-the-art methods.

Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy

Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field.

Soil organic matter prediction with benchtop and implement‐mounted optical reflectance sensing approaches

AbstractProximal sensing technologies can densely quantify soil organic matter (OM) variability using visible and near infrared (VNIR) reflectance spectroscopy. However, issues with global calibrations and varying water content can reduce accuracy. Therefore, research was conducted to determine OM prediction accuracy across soil volumetric water content (VWC) with (a) a planter‐mounted optical sensor and (b) multiple combinations of reflectance bands within the VNIR spectrum. Ninety soils collected across Missouri and Illinois were used in the study. Data were collected at three VWC with the Precision Planting SmartFirmer and a benchtop spectrometer. SmartFirmer OM was derived through an internal algorithm, while spectral pre‐processing and machine learning were used for OM prediction in all approaches that used reflectance from the benchtop spectrometer. Results found that SmartFirmer OM predictions were affected by soil VWC, with predicted OM decreasing with increasing VWC. Findings from three modeling approaches using the benchtop spectrometer showed that a continuous spectrum (i.e., 400–1,500 nm) improved performance (RMSE = 5.25 g kg–1) over the discrete waveband approach (RMSE = 9.23 g kg–1). Furthermore, including the entire VNIR region (400–2,500 nm) resulted in the best predictive capability (RMSE = 1.42 g kg–1). However, because a full‐spectrum approach increases cost, using reflectance from 400 to 1,500 nm along with spectral pre‐processing and machine learning may be an acceptable method for estimating OM. These findings contribute to the improvement of commercially available proximal sensors that may be used to monitor soil carbon (C) stocks, assess soil health, or for other precision agriculture applications.

Assessing a VisNIR penetrometer system for in-situ estimation of soil organic carbon under variable soil moisture conditions

Soils are one of the main terrestrial carbon sinks and contain more carbon than the combined total in the atmosphere and terrestrial vegetation. A major challenge is quantifying how much carbon is stored in the soil and how much it has been sequestered due to changed management practices. In this study, a visible and near-infrared (VisNIR) penetrometer system was developed to measure in-situ soil VisNIR reflectance spectra to a depth of 90 cm into the soil profile. A spectral library of VisNIR soil spectra collected in air-dry and ground states were used to calibrate Cubist regression models for estimating soil organic carbon (SOC) contents. The External Parameter Orthogonalisation (EPO) transformation was applied to the in-situ spectra to remove soil moisture and other in-situ effects from the spectra. This penetrometer system was used to estimate SOC contents at three sites on three different days under variable soil moisture states. The penetrometer EPO performed better compared to the EPO constructed from moist cores. After applying EPO transformation matrix, soil wetness did not have any influence on soil SOC content estimations. The SOC contents were estimated using the in-situ VisNIR system were validated against lab-measured SOC content with mean R 2 , RMSE, and bias of 0.88, 0.32%, and 0.15%, respectively. It is concluded that the VisNIR penetrometer can potentially be used to measure SOC content rapidly and cost-effectively in-situ with a fine depth resolution. • A penetrometer system was used to acquire in-situ soil VisNIR spectra. • A spectral library from agricultural regions of NSW was used for calibration. • External Parameter Orthogonalisation (EPO) reduced confounding soil moisture effect. • External validation was performed to measure soil organic carbon contents.

Automatic neural network hyperparameter optimization for extrapolation: Lessons learned from visible and near-infrared spectroscopy of mango fruit

Configuring a neural network’s architecture and hyperparameters often involves expert intuition and hand-tuning to extrapolate well without overfitting. This paper considers automatic methods for configuring a neural network for the domain of visible and near-infrared (Vis-NIR) spectroscopy. In particular, we study the effect of (a) validation set choice for validating configurations and (b) using ensembles. We consider several validation set choices: a random sample of 33% of non-test data (the technique used in previous work), samples from the latest year (a harvest season), and the first, middle, and latest 33% of samples sorted by time. To test these methods, we do a comprehensive study of a held-out 2018 harvest season of mango fruit given Vis-NIR spectra from 3 prior years. We find that ensembling improves the state-of-the-art model’s variance and accuracy. Furthermore, hyperparameter optimization experiments show that when ensembling is combined with using the latest 33% of samples as the validation set, a neural network configuration is found automatically that performs as well as an expertly-chosen configuration.

Determination of soil pH from Vis-NIR spectroscopy by extreme learning machine and variable selection: A case study in lime concretion black soil

Variable selection is widely accepted as an important step in the quantitative analysis of visible and near-infrared (Vis-NIR) spectroscopy, as it tends to improve the model’s robustness and predictive ability. In this study, a total of 140 lime concretion black soil samples were collected from two towns in Guoyang County, China. The Vis-NIR spectra measured in the laboratory were used to estimate soil pH by an extreme learning machine (ELM). First, the soil spectra were treated by the optimized continuous wavelet transform (CWT), and then four spectral feature selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; Monte Carlo uninformative variable elimination, MCUVE; genetic algorithm, GA) were applied with ELM in the CWT domain to determine the techniques with most predictions. For comparison, The PLS and SVM models were also developed. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) were used to evaluate the model performance. Based on the validation dataset, the performance of the ELM models was superior to that of the PLS and SVM models expect SPA and MCUVE. In the ELM models, the order of the prediction accuracy was GA-ELM (R2p = 0.86; RMSEp = 0.1484; RPD = 2.64), CARS-ELM (R2p = 0.84; RMSEp = 0.1565; RPD = 2.50), ELM (R2p = 0.84; RMSEp = 0.1572; RPD = 2.49), SPA-ELM (R2p = 0.84; RMSEp = 0.1589; RPD = 2.47) and MCUVE-ELM (R2p = 0.83; RMSEp = 0.1599; RPD = 2.45). The proposed method of CARS-ELM had a relatively strong ability for spectral variable selection while retaining excellent prediction accuracy and short computing time (0.39 s). In addition, the variables selected by the four methods (CARS, SPA, MCUVE and GA) indicated the prediction mechanism for pH in lime concretion black soil may be the relation between pH and iron oxides and organic matter. In conclusion, CARS-ELM has great potential to accurately determine the pH in lime concretion black soil using Vis-NIR spectroscopy.

Field-scale spatial correlation between soil and Vis-NIR spectra in the Cerrado biome of Central Brazil

Visible and near-infrared (Vis-NIR) spectroscopy is a tool to determine soil spatial variability and has been used to map soils and their properties. Considering that physical, chemical, mineralogical, and morphological soil properties can affect the intensity and the depth of the spectral reflectance band in the Vis-NIR region, the objectives of this work were to: (i) evaluate the potential of the reflectance inflection difference (RID) to discriminate soils; and (ii) verify potential spatial correlations of the RID with soil properties, compared with the full spectra, in order to build thematic maps at a field scale. In a farm of 375 ha, 78 soil samples from the 0.87–0.92-m depth were collected in a regular grid of 200 m, with a focus on the soil diagnostic horizon (Bw horizon). The sampled soils were a Latossolo Vermelho-Amarelo ácrico (Haplic Ferralsol) and a Latossolo Vermelho distrófico (Rhodic Ferralsol). Twenty-two physical, chemical, mineralogical, and morphological soil properties were determined, and the Vis-NIR spectra between 400 and 2500 nm were measured. Considering the presence of an inflection band and its relationship with soil properties, the spectral bands used to calculate the RID were between (base 1/base 2): 400–510, 730–930, 1290–1450, 1800–1950, 2000–2218, and 2218–2290 nm. The RID failed to map the spatial variability of soil properties, with a Kappa index of 39%; therefore, it is not a good parameter for building thematic maps of soil parameters. In addition, the complete spectrum (mainly in 400–510, 730–930, 1290–1450, 1800–1950, 2000–2218, and 2218–2290 nm) was better spatially correlated with soil properties than the decomposition of the spectrum by the RID. Soil classification and level of discretization as affected by spectral variability were also discussed here. Three soil groups were discriminated mainly by the Ki and Kr indexes and clay content. Moreover, the variability of the spectra was conditioned by the spatial variability of the mentioned variables. The clay content for soils with a discrepant particle size (group 1 compared with groups 2 and 3) and the Ki and Kr indexes for soils with a homogeneous particle size (between groups 2 and 3), associated with the full Vis-NIR spectral analysis, allowed building thematic maps with a good precision, without the need of mathematical models; this was possible by the modification of the reflectance intensity and the size of the concavity of the spectral band.

Evidence for hydrated minerals in the VNIR spectra of G-class asteroids: A first look

We present the initial results of our visible and near-infrared (VNIR) investigation into the spectral properties of the Tholen G-class asteroids to determine: 1) which spectral properties are common among the G-class, and 2) which surface minerals may be related to the spectral features present. Our study utilized both previously published and newly obtained spectra of five G-class asteroids. Our results indicate that four of these asteroids: (13) Egeria, (19) Fortuna, (84) Klio, and (130) Elektra, exhibit spectral features consistent with hydrated minerals in the VNIR (0.4–2.5-μm). The most notable absorption feature was located at 0.7-μm, which is related to the Fe2+ ➔ Fe3+ charge transfer transition in oxidized Fe-rich phyllosilicates. In addition, several of the asteroids also exhibit subtle absorption features located near 0.95-, 1.4-, 1.9-, and 2.3-μm, which may also be related to hydrated minerals. Only (1) Ceres' spectrum lacked features related to hydrated minerals in the VNIR. This may indicate that either (1) Ceres contains lower concentrations of hydrated minerals at its surface, or that (1) Ceres has an opaque surface component, which may obscure weak hydrated VNIR features.

Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

Prediction of soil hydraulic properties using VIS-NIR spectral data in semi- arid region of Northern Karnataka Plateau

Field capacity (FC) and permanent wilting point (PWP) are the important soil hydraulic properties which determine the availability of water for plant growth. Conventional estimation of soil hydraulic properties is tedious and expensive which are normally derived from pedo-transfer functions. Visible and near-infrared (Vis-NIR) spectroscopy is a low-cost, non-destructive alternative method for the rapid estimation of soil properties. The present study was carried out to predict soil hydraulic properties using Vis-NIR spectral data in the semi-arid region of the Northern Karnataka plateau. Spectral data of 558 soil samples were acquired using Field Spec ASD Spectroradiometer and three models (support vector machine, random forest and partial least square regression) were evaluated for prediction of FC and PWP. The models were calibrated using 2/3rd of total observations and validated using 1/3rd of observations. The validation results showed that RF and SVM provided slightly higher performance compared to PLSR model. Permanent wilting point was predicted well (R2 = 0.70–0.74, RMSE = 5.44–5.74%) compared to field capacity (R2 = 0.66–0.69 and RMSE = 7.25–7.51%). Among the soil orders, FC and PWP of Vertisols were poorly predicted by Vis-NIR spectra (R2 = 0.34&0.42) and moderately predicted for Alfisols (0.44&0.52) and Inceptisols (0.55&0.65) while excellent prediction was recorded for Entisols (0.83&0.76). The present results showed that Vis-NIR spectroscopy is helpful for fast estimation of hydraulic properties in semi-arid regions of the country with high accuracy.

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration.

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination () and the root mean square error () of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the and were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.