vis-NIR Reflectance Spectroscopy Research Articles

Developing a Prediction Method for Physicochemical Characteristics of Pontianak Siam Orange (Citrus suhuiensis cv. Pontianak) Based on Combined Reflectance-Fluorescence Spectroscopy and Artificial Neural Network

The slightly sweet and acidic taste offered by Pontianak Siam oranges is influenced by the total soluble solids (TSS) and acidity in the fruit, in which, measuring these attributes is commonly performed using instruments that potentially damage the fruit's structure, thus, impractical for fresh fruit products. Moreover, the process of classifying the quality of fresh oranges has been based on physical appearance, leading to subjective results. Correspondingly, the objective of the study is to develop a prediction method for the physicochemical characteristics of Pontianak Siam oranges based on VIS-NIR-Fluorescence spectroscopy and an artificial neural network (ANN) model. The method is applicable to classify oranges based on physicochemical characteristics without damaging the fruit's structure. As a result, the best model for classifying the maturity level of Pontianak Siam oranges was obtained using a dataset with all feature combined spectra, attaining a training accuracy of 0.99 and testing accuracy of 1. The best model for predicting TSS was obtained using all feature combined spectra dataset, attaining R2 training = 0.89 and R2 testing = 0.91. The best model for predicting acidity was obtained using all feature reflectance spectra datasets, attaining R2training = 0.96 and R2testing = 0.97. The best model for predicting fruit firmness was obtained using all feature reflectance spectra dataset, attaining R2training = 0.97, R2testing = 0.89. Overall, the combination of Vis-NIR reflectance and fluorescence spectroscopy have the potential to be applied for non-destructive assessment of citrus quality in terms of visual classification and maturity parameters prediction.

Prediction of soil nutrients through PLSR and SVMR models by VIs-NIR reflectance spectroscopy

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement.

Porous silicon structures passivated with 10-undecenoic acid for possible ethanol sensing

Multifunctional sensing platforms are useful for classifying and quantifying different analytes. This work presents a simple optical sensor based on porous silicon film modified using open-air pyrolysis with 10-undecenoic acid. The fabricated structure was characterized using vis-NIR reflectance spectroscopy and the functional groups were identified using Fourier transform infrared spectroscopy. With respect to the control, the proposed undecenoic acid passivated porous silicon nanostructured film has been tested as reusable ethanol sensor in water.

Improving selectivity of thin film solar absorber by cobalt oxide grafted reduced graphene oxide

Reduced graphene oxide (rGO) modified cobalt oxide (Co3O4) nanocomposite (NC) thin films were prepared on aluminum substrates through dip-coating technique for solar selective absorber. The prepared rGO-Co3O4 NC thin films were characterized by X-ray diffraction, which revealed that the prepared samples have face centered cubic structure. Scanning electron microscopy showed that morphology of prepared thin films is comprised of spherical shaped grains with rough surface. Energy-dispersive X-ray spectra exhibited absence of impurities in the thin films. Raman spectra predicted spinel structure, combined vibrations of tetrahedral site and octahedral oxygen motions in the rGO-Co3O4 NC thin films. Optical properties were investigated by UV–Vis–NIR reflectance spectroscopy. The prepared thin films with a concentration of 1.0 wt% of rGO (1%-rGO-Co3O4) disclosed a highest solar absorptance (α = 89.95) and low thermal emittance (ε = 4.6) with a highest solar selectivity (ξ) of 19.55. These values indicated that the rGO-Co3O4 NC thin film samples can be ideal candidates as solar selective absorbers for solar thermal energy gadgets.

Prediction of material composition in lateritic soil using spectroradiometer

Laterite soil products are vital in coastal region construction. Conventional identification methods are slow and require significant labour. Remote sensing expedites large-scale identification, making it essential in the process. Soil samples were randomly collected from the study area for laboratory analysis to determine soil iron content. Additionally, spectral signatures were obtained from a spectroradiometer to assess iron content variability. The considerable variability in material composition underscores its significance as an indicator of soil characteristics. The iron content serves as a crucial indicator of soil development and can be utilized as an index for assessing material composition in soil. The iron content in soil samples is analysed using Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer, which operates within the spectral wavelength range of 350–2500 nm. The Vis-NIR reflectance spectroscopy is an innovative and efficient method for predicting soil iron content, requiring less effort compared to conventional techniques. The study uses Partial Least Squares Regression (PLSR), a statistical regression method employed to predict iron content in soil from spectral reflectance data. The application of PLSR resulted in moderate prediction accuracy, with coefficient of determination R2 value for calibration and validation are 0.74 and 0.73, respectively. The analysis revealed that the spectroradiometer in combination with PLSR can accurately predict the material composition in lateritic soil with an efficiency of 70%.

Nondestructive Vis-NIR Reflectance Spectroscopy as a Forensic Tool for Ink Discrimination: A Preliminary Study

Nondestructive Vis-NIR Reflectance Spectroscopy as a Forensic Tool for Ink Discrimination: A Preliminary Study

Enhancing selectivity of solar absorber using reduced graphene oxide modified nickel oxide nanocomposite thin films

Commercial solar selective absorbers prepared through several techniques have encountered drawbacks of necessitating complicated and expensive equipment, substantial materials requirement, and polluting environment. In this context, carbon incorporated metal oxide thin films have shown promise of low-cost, non-toxic, and efficient solar selective absorbers. In particular, association of reduced graphene oxide (rGO) and nickel oxide (NiO) could facilitate highly ascertained optical properties, environment friendliness, and low-cost. Present study focuses on preparing a novel, high performance, and low-cost rGO-NiO solar selective absorber thin films. Thin films comprised of reduced graphene oxide modified nickel oxide nanocomposites (rGO-NiO NCs) were prepared through solvothermal route and coated on aluminum (Al) substrates by dip-coating technique. The rGO-NiO NCs examined using X-ray diffractometer revealed formation of face centered cubic crystallite structure. The scanning electron microscope images disclosed spherical morphology of NiO decorated on the rGO sheets. The spectra derived from energy-dispersive X-ray spectroscopy ascertained absence of peaks corresponding to impurities. The Raman investigation exhibited high intensity ratio (0.97) of the rGO-NiO NCs that described higher disorder and binding sites. The optical property analysis of the rGO-NiO NC (0.2 wt% rGO) thin film performed in UV–vis-NIR reflectance spectroscopy showed high absorptance (α = 88.03 %) and low thermal emittance (ε = 4.5 %), which eventually delivered high solar selectivity (ξ) of 19.56. These findings suggest that the prepared 0.2 % rGO-NiO NC thin film can be utilized as an appropriate selective absorber for solar to thermal energy conversion.

Vis-NIR Reflectance Spectroscopy and PLSR to Predict PCB Content in Severely Contaminated Soils: A Perspective Approach

Soil reclamation from polychlorinated biphenyls (PCBs) requires careful analysis in terms of their concentrations and spatial distribution. Conventional laboratory analysis, even if providing the careful evaluation of PCBs, is costly and time-consuming. Therefore, rapid and cost-effective techniques to replace traditional analytical approaches are required. The utility of visible-near infrared (vis-NIR) reflectance spectroscopy in conjunction with partial least square regression (PLSR) analysis was evaluated in this study. Spectral reflectance was measured in the laboratory on 28 soil samples collected in a highly contaminated area of southern Italy and chemically analysed to determine eighteen PCB congeners, their sum (PCBs18), and extractable organic halogen content (EOX). Spectroscopic data were pre-processed prior to data analysis by combining different methods. Using PLSR analysis, significant relationships were observed between the predicted and the measured content of PCBs18, EOX, and the percentage of several isomeric classes of PCBs. Although rigorous models could not be calibrated, due to the limited number of samples, the preliminary results of this study demonstrated that vis-NIR reflectance spectroscopy, coupled with PLSR, can be considered a promising method for a rapid and cost-effective prediction of PCBs.

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods.

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction () was 0.9504 and the root mean square error of prediction () was 0.8181. For the stearic acid, and were 0.9636 and 0.2965. In the prediction of arachidic acid, and were 0.9576 and 0.0577. In the prediction of behenic acid, the and were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.

Applications of Raman spectroscopy in art and archaeology

Applications of Raman spectroscopy in art and archaeology

Application of chemometric methods for the determination of fading and age determination of blue ballpoint inks

AbstractThis study presents preliminary results for applicability of micro‐Raman and VIS–NIR reflectance spectroscopy in age determination of blue ballpoints inks containing phthalocyanine and crystal violet. Based on chemometric methods, 74 different random blue pens were classified to various types of inks. Then handwritten samples stored in different light conditions were subjected to the analysis. The first set of samples was stored for 7 years at room temperature in an envelope without direct exposure to the light, whereas the second set of samples was aged by exposing them to natural light cycle for 85 days. The L, a, b values were determined on the basis of recorded Raman and VIS–NIR spectra and then used for chemometric calculation. Based on those values, partial least squares models were built, allowing to obtain correlations between ∆E and Raman and reflectance spectra. Relative standard errors of prediction RSEP(%) for obtained models were calculated in the range 1.97–5.13 and 1.20–2.27 for Raman and VIS–NIR spectra, respectively. The proposed nondestructive method allows to estimate the age of daylight exposed samples.

Minimum variance based-Bayes Combination for prediction of soil properties on Vis-NIR reflectance spectroscopy

Traditional laboratory methods for determining soil properties require a great deal of time and expense, while reflectance spectroscopy technology is a fast, inexpensive, and convenient way to predict physical and chemical soil properties. This technology in the spectral range of 400–2500 ​nm (Vis-NIR) as a suitable alternative method to get soil properties are accompanied by problems and challenges to extract considered properties. In this paper, we propose Minimum Variance based-Bayes Combination (MVBC) method to predict the soil properties. In the proposed MVBC method, we design two steps, prediction and combination for the training phase. Firstly, in the prediction step, five regression methods, i.e., partial least squares regression (PLSR), kernel Ridge regression (KRR), linear regression (LR), gradient boosting regression (GBR) and random forest (RF) method used to calculate and estimate nine soil properties, i.e., CaCo3, CEC, Clay, N, OC, PH in CaCl2, PH in H2O, Sand and Silt, separately. Secondly, in the combination step, the estimation errors of all regressions in the prediction step are determined to assign appropriate weight to each of them in the Bayesian framework based on minimum variance. These two steps are repeated until the final estimation error reaches an acceptable minimum value. Finally, these results and the trained system are used for the test phase. Experiments are reported to evaluate the effectiveness of the proposed MVBC method on real soil data, which shows the good performance of the proposed method with better results than other methods.

Simultaneous prediction of several soil properties related to engineering uses based on laboratory Vis-NIR reflectance spectroscopy

The conventional methods for measuring soil properties are time-consuming and expensive. Over the past few decades, reflectance spectroscopy has been widely applied as a rapid and cost-effective technique to estimate soil properties. However, less attention has been given to estimating soil engineering properties using soil spectral data. To evaluate the efficiency of the technique, selected physical and mechanical properties of 220 soil samples from two study areas in Ghorveh-Dehgolan and Zrêbar regions in the Kurdistan province, west of Iran, were measured by standard laboratory methods. Spectral measurements were also performed on air-dried soil sub-samples using a laboratory spectroradiometer apparatus. After recording the spectra, the different pre-processing methods were tested to improve the estimations. Partial least squares regression (PLSR) was used to establish relationships between soil engineering properties and the spectra transformed via Savitzky–Golay first derivative. Based on the results, the prediction accuracy was very good for clay activity (Ac) and clay content (modeling efficiency (EF) = 0.75 and the ratio of performance to deviation (RPD) > 2), and good for cation exchange capacity (CEC) and effective diameter size (D10) (EF > 0.68 and RPD > 1.81). Reasonable predictions were also obtained for Atterberg limits, silt and sand contents, particle density (PD), diameters corresponding to 30% and 60% finer (D30 and D60, respectively), median particle size (D50), and geometric mean diameter of soil particles (dg) and their standard deviation (δg) (EF: 0.51–0.67, RPD: 1.44–1.76); however, the total porosity (n) and bulk density (BD) of soils were poorly predicted. The reliability and good quality of the estimations was probably due to the variability in the properties of the different soils collected from the two distinct geographical regions. Overall, the results confirmed the potential efficacy of Vis-NIR spectroscopy for a rapid estimation of different important soil engineering properties.

Vis-nir reflectance spectroscopy as an alternative method for rapid estimation of soil available potassium

Vis-nir reflectance spectroscopy as an alternative method for rapid estimation of soil available potassium

Investigation of soil surface organic and inorganic carbon contents in a low-intensity farming system using laboratory visible and near-infrared spectroscopy

ABSTRACT In some regions of Italy, low-intensity farming systems, together with variable climate conditions, have lowered soil organic carbon (SOC) content and soil quality attributes. This work aims to investigate on some aspects of (1) total organic carbon (TOC) prediction using Vis-NIR reflectance spectroscopy in combination with partial least squares regression (PLSR); (2) the most appropriate pre-processing techniques of Vis-NIR absorbance spectra; (3) the composition of organic carbon using variable importance of prediction (VIP). The study area was an olive grove, located at Montecorvino Rovella (Salerno, southwestern Italy), characterized by a calcaric soil (Leptic Calcisols) and (Luvic Phaeozem), with a low content of TOC (mean 2.03 g kg−1), caused by a low-intensity farming. Results of univariate PLSR analyses showed a good agreement between measured and predicted values both for TOC (R2: 0.66) and total carbonate content (R2: 0.93), when pH, electrical conductivity (EC) and absorbance spectra were used as predictors. The best results were obtained using as pre-treatments of the spectral data: 1) standard normal variate (SNV); 2) Savitzky-Golay algorithm; 3) first derivative. Variable Importance for Prediction (VIP) statistics showed to be a good tool to gain insights in TOC composition also when its content is low and influenced by carbonate.

Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy

In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt.

Biosignature detection by Mars rover equivalent instruments in samples from the CanMars Mars Sample Return Analogue Deployment

This work details the laboratory analysis of a suite of 10 samples collected from an inverted fluvial channel near Hanksville, Utah, USA as a part of the CanMars Mars Sample Return Analogue Deployment (MSRAD). The samples were acquired along the rover traverse for detailed off-site analysis to evaluate the TOC and astrobiological significance of the samples selected based on site observations, and to address one of the science goals of the CanMars mission: to evaluate the ability of different analytical techniques being employed by the Mars2020 mission to detect and characterize any present biosignatures. Analytical techniques analogous to those on the ExoMars, MSL and the MER rovers were also applied to the samples. The total organic carbon content of the samples was <0.02% for all but 4 samples, and organic biosignatures were detected in multiple samples by UV–Vis–NIR reflectance spectroscopy and Raman spectroscopy (532 nm, time-resolved, and UV), which was the most effective of the techniques. The total carbon content of the samples is < 0.3 wt% for all but one calcite rich sample, and organic C was not detectable by FTIR. Carotene and chlorophyll were detected in two samples which also contained gypsum and mineral phases of astrobiological importance for paleoenvironment/habitability and biomarker preservation (clays, gypsum, calcite). They were detected and characterized by multiple techniques, of which passive reflectance was most effective. The sample selected in the field (S2) as having the highest potential for TOC did not have the highest TOC value, however, when considering the sample mineralogy in conjunction with the detection of organic carbon, it is the most astrobiologically relevant. These results highlight the importance of applying multiple techniques for sample characterization and provide insights into their strengths and limitations.

Potential of VIS-NIR spectroscopy to characterize and discriminate topsoils of different soil types in the Triffa plain (Morocco)

Abstract This study aims to identify the influence of soil organic matter (OM) content and calcium carbonates (CaCO3) on soil reflectance and select the optimum spectral bands for discriminating between topsoils of different soil types situated in the irrigated perimeter of the Triffa plain (Morocco) using VIS-NIR reflectance spectroscopy. Soil samples were collected from the plow layer in 26 sampling sites. The spectral measurements were conducted in the field using an ASD Fieldspec portable spectroradiometer (350–2500 nm), while the soil samples were analyzed in the laboratory. The spectral data were pre-processed to remove the noise effects and then analyzed with the CovSel (selected covariance) method, validated by linear discriminant analysis in order to select the most optimal spectral variables to discriminate between topsoils of different soil types in the plain. The results of the soils reflectance curves showed that low reflectance intensity marked the soils with high OM contents throughout the VIS spectrum. The influence of the soil OM content was very apparent in the VIS range (between 580–750 nm). Regarding the CaCO3 content, it was noted that the soil samples with a high percentage of CaCO3 increase the reflectance in all spectral domains situated between 350 and 2500 nm. The spectral bands of 1999, 686, 1280, 2340 and 1951 nm were the most optimal for the soil discrimination in the Triffa plain. This study concluded that the VIS-NIR spectroscopy demonstrates an excellent ability to characterize and discriminate between topsoils in the Triffa plain.

The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy

In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis–NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples and applied six commonly used spectral pretreatments to preprocess soil spectra, namely, Savitzky–Golay (SG) smoothing, first derivative (FD), logarithmic function log(1/R), mean centering (MC), standard normal variate (SNV), and multiplicative scatter correction (MSC). Then, the Kennard–Stone (KS) strategy was used to select calibration samples based on the pretreated spectra, and the size of the calibration set varied from 10 samples to 86 samples (80% of the total samples). These calibration sets were employed to construct partial least squares regression models (PLSR) to predict SOM, and the built models were validated by a set of 21 samples (20% of the total samples). The results showed that 64−78% of the calibration sets selected by the inclusion of pretreatment demonstrated significantly better performance of SOM estimation. The average improved residual predictive deviations (ΔRPD) were 0.06, 0.13, 0.19, and 0.13 for FD, log(1/R), MSC, and SNV, respectively. Thus, we concluded that spectral pretreatment improves the sample selection strategy, and the degree of its influence varies with the size of the calibration set and the type of pretreatment.

Development of predictive models for quality and maturation stage attributes of wine grapes using vis-nir reflectance spectroscopy

The viticulture business has an increasing demand for high quality products directly impacting on their market acceptance. Hence, the constant monitoring activities of vineyards, specially quality and maturation stage attributes, are important to assure the production of special wines, justified by their high value-added characteristic. Therefore, optical technologies appear as promising techniques for non-destructive analysis of wine grapes in view of reducing agricultural inputs and analysis duration. The main objective of the present work was to develop predictive models for quality and maturation stage attributes of wine grapes using visible/near infrared (VIS-NIR) reflectance spectroscopy. A total of 432 ‘Syrah’ and 576 ‘Cabernet Sauvignon’ berries were collected and their reflectance spectra were acquired using FieldSpec® 3 spectroradiometer for a spectral range from 450 to 1800 nm. In a posterior step, total soluble solids, total anthocyanins and yellow flavonoids were determined as the reference standards. Before elaborating mathematical models, the spectral data were submitted to several pretreatments, such as smoothing, derivate and corrections. Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) were utilized as predictive models using both the complete spectrum data (450–1800 nm) and a smaller set of spectral samples selected by the Jack-Knife method. Other predictive models were also developed utilizing Multiple Linear Regression (MLR) with spectral signatures from quality attributes. Maturation stages were discriminated using Principal Component Analysis – Linear Discriminant Analysis (PCA-LDA), Principal Component Analysis – Quadratic Discriminant Analysis (PCA-QDA), Principal Component Analysis – Linear Discriminant Analysis using Mahalanobis distance (PCA-LDA Mahalanobis), and Partial Least Squares Discriminant Analysis (PLS-DA) classification techniques. The construction of the PCR, PLSR and MLR regression models has provided robust predictions for total soluble solids and anthocyanins contents (R2 ≥ 0.90), as well as flavonoids contents with a certain degree of precision (R2 ≥ 0.70). Moreover, it was possible to differentiate distinct maturation stages of vines with 93.15% of accuracy using PLS-DA. Therefore, VIS-NIR reflectance spectroscopy is a powerful tool for non-destructive evaluation of quality and maturation stage attributes of intact grapes from ‘Syrah’ and ‘Cabernet Sauvignon’ grapes.