Near-infrared Diffuse Reflectance Spectroscopy Research Articles

Room-temperature ferromagnetic Zn1−xNi x S nanoparticles

Nickel-doped zinc sulfide nanoparticles (Zn1−xNi x S) at x = 0.00, 0.02, 0.05, 0.08 and 0.10 were synthesized by solid-state reaction. The (nickel sulfide) NiS and (zinc sulfide) ZnS nanoparticles in desired ratios were taken, mixed and ground for 6 h at a speed rate of 300 rpm using a planetary ball mill. The milled nanoparticles were sintered at 600 °C for 8 h using a high-temperature vacuum furnace. The structural, optical, luminescence and magnetic properties of the Zn1−xNi x S nanoparticles were characterized by powder X-ray diffraction (XRD), UV–Vis–NIR diffuse reflectance spectroscopy, photoluminescence (PL) spectroscopy and vibrating sample magnetometer (VSM). No change in crystal structure was observed from XRD by substitution of Ni into ZnS lattice. The mean crystallite size was found to be 37 nm. The band gap of Zn1−xNi x S nanoparticles decreased from 3.57 to 3.37 eV on increasing the dopant concentration. The room-temperature photoluminescence (PL) spectra of Zn1−xNi x S nanoparticles showed two broad and intense emission peaks at 420 and 438 nm with excitation wavelength of 330 nm. The Zn1−xNi x S nanoparticles showed ferromagnetism at 100 K and at room temperature (300 K) and also the strength of magnetization increased with Ni concentration. The maximum magnetization value of 0.18 emu/g was observed for x = 0.10 at 100 K. The strength of the magnetization observed at 100 K was higher than that of magnetization observed at 300 K.

Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy.

Accurate information of soil macronutrient contents and fertilizer macronutrient contents is the precondition of precision fertilization; however, how to detect soil and fertilizer information rapidly, reliably, and inexpensively remains a great challenge. Visible and near-infrared (VIS/NIR) diffuse reflectance spectroscopy proves to be an effective tool for extensive investigation of soil and fertilizer properties. This study first collected many soil and chemical fertilizer samples and performed both spectral scanning and chemical analysis. During the correlation between the collected VIS/NIR spectra and the measured data, different spectral pretreatment, sample selection, and wavelength optimization methods were applied for improving the accuracy and robustness of the prediction models. After appropriate spectral processing and selection of representative samples, both principal component regression and genetic algorithm (GA) can adequately reduce the number of variables and pick out the characteristic variables, which not only enhanced prediction speed but also greatly improved prediction accuracy. In particular, using GA-based models, organic matter content (OMC), total N and pH value in soil and N, P, and K contents in fertilizer can all be accurately predicted.

Influence of reaction time on the structural, optical and electrical performance of copper antimony sulfide nanoparticles using solvothermal method

The less toxic and cost effective ternary Cu-Sb-S nanoparticles and thin films were synthesized and deposited using solvothermal and drop casting method. The reactions were carried out at different timings as 12–48 h, in steps of 12 h using ethylene glycol as solvent and polyvinylpyrrolidone (PVP) as surfactant. Systematic analysis revealed that due to the influence of different reaction time, significant and unique changes were occurring on the crystal structure, optical and electrical properties of the material. The synthesized nanopowders and deposited films were characterized by means of X-ray diffraction, Raman analysis, field emission scanning electron microscope with energy dispersive spectrometer, UV–Vis–NIR diffuse reflectance spectroscopy and hall measurement. XRD results showed that as the time increases crystallinity improves and phase transformation from chalcostibite to tetrahedrite occurs. The Optical performance revealed that the bandgap of nanoparticles were in the range of 1.21–1.49 eV. Hall measurements showed that the deposited Cu12Sb4S13 and CuSbS2 films exhibited p-type conductivity with carrier concentration ranging from 1016-1019 cm−3, indicating a promising p-type absorber material for photovoltaic applications.

Estimating Soil Salt Content Using Fractional Derivatives and Optional Spectral Indices in the Ebinur Lake Oasis, Northwestern China

Derivative spectroscopy is a powerful mathematical tool that provides more useful information of spectral data than untreated data. Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. The VIS-NIR spectrum is one of the most important data acquisition technologies for digital soil mapping, precision agriculture, and soil resource surveys. The objective of this study was to develop a soil salt content model using the fractional-order derivatives of field-measured spectral data paired with ground measurements. The results showed the following: There was a significant correlation between the single-band reflectance spectra and the soil salt content, and as the derivative order increased, reflectance values first increased and then decreased, with a peak value, R = 0.525, in the 1.2-order derivative. Strong correlations between the estimation of soil salt content and the derivative spectral data of the difference index (DI), ratio index (RI), and normalized difference index (NDI) were also produced by the 1.2-order derivative. R values for the DI, RI, and NDI were 0.818, 0.8624, and 0.8297, respectively. Selected bands with the highest correlation values from the 1.2-order derivative spectral data of the R1428, DI (R1426 / R2151), RI (R1429 − R2024), and NDI (R1526 − R2470)/(R1526 + R2470)] were used to build a model to estimate soil salt content, which showed an R2 value of 0.53 and low RMSE and SD values. The 15 samples of the data were then used to validate the single-band model, R1428, and the combination models, DI (R1426 / R2151), RI (R1429 − R2024), and NDI [(R1526 − R2470)/(R1526 + R2470)], which demonstrated that the R2 values were greater than 0.8. The RMSE and SD values were low, and the value of the RPD was greater than 1.4. This study demonstrated the potential of using fractional derivatives rather than integer derivatives for soil salt content estimation. By making full use of hyperspectral data, fractional derivatives could enrich data preprocessing methods and unearth information about spectral emissions that are lost by integer derivatives. Although this study is a straightforward application of the fractional derivative method, it provides a reference for the estimation of other parameters with hyperspectral technology.

On-site estimation of total polyphenol in fresh tea leaf using near-infrared spectroscopy

The quality of tea liquor primarily depends on the quality of inbound fresh leaves to a tea factory. We report here the development of an industrial set-up using diffuse reflectance near-infrared spectroscopy for quick and on-spot estimation of total polyphenol content, which is one of the major quality indicators of tea. In the inspection system, freshly plucked tea leaves are dried with a heating system designed in such a way that the moisture content is reduced to around 3–4%. The dried leaves are then ground and sieved through a 250 µm mesh. Samples thus produced are placed automatically before the near-infrared beam and assessment of total polyphenol is done instantaneously.

Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate

Near infrared diffuse reflectance spectroscopy (NIRDRS) has gained wide attention due to its convenience for rapid quantitative analysis of complex samples. A method for rapid analysis of triglycerides in human serum using NIRDRS with silver mirror as the substrate is developed. Due to the even and high reflectance of the silver mirror, the spectral response is enhanced and the background interference is reduced. Furthermore, both linear and nonlinear modeling strategies were investigated adopting the partial least squares (PLS) and least squares support vector regression (LS-SVR), continuous wavelet transform (CWT) was used for spectral preprocessing, and variable selection was tried using Monte Carlo uninformative variable elimination (MC-UVE), randomization test (RT) and competitive adaptive reweighted sampling (CARS) for optimization the models. The results show that the determination coefficient (R) between the predicted and reference concentration is 0.9624 and the root mean squared error of prediction (RMSEP) is 0.21. The maximum deviation of the prediction results is as low as 0.473mmol/L. The proposed method may provide an alternative method for routine analysis of serum triglycerides in clinical applications.

Synthesis, Characterization, and Optical Properties of Near-Infrared Reflecting Composite Inorganic Pigments Composed of TiO2/CuO Core–Shell Particles

Pigments with dark appearance and high solar energy reflectance are of great application value. In the present work, dark grey composite pigments with a TiO2/CuO core–shell structure were prepared through calcination of precursors obtained from the precipitation of Cu(OH)2 on TiO2 particles. The composition, structure, and optical properties of the synthesised powders were characterised by colourimetry, near-infrared diffuse reflectance spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The pigments are composed of rutile TiO2 and tenorite CuO, and the powder colour changes from white to dark grey as the Cu content increases. With a molar ratio of CuO to TiO2 of 0.6, the pigment presents optimal colour (a* = 1.66, b* = −2.19, L* = 52.37) and optical performance. The near-infrared reflectance, the solar reflectance in the near-infrared region, and the total solar reflectance reach 87.2, 70.6, and 37.2 %, respectively. Furthermore, it is demonstrated that the composite pigments exhibit a deeper colour and higher reflectivity than conventional dark-blended powders. These cool dark pigments could be applied in the coatings for buildings (roofs and walls) to improve building comfortability and reduce cooling energy consumption.

Modification method to reduce the impact of blood vessel on noncontact discrimination of human blood based on “M+N” theory

Noncontact discriminating human blood is significantly crucial for import-export ports and inspection and quarantine departments. We had already demonstrated that visible diffuse reflectance spectroscopy combining PLS-DA method can successfully realize noncontact human blood discrimination. However, the circulated blood vessels may be produced with different materials. The use of various kinds of blood tubes may have a negative effect on the discrimination, based on “M+N” theory (Li et al., 2016). In this research, we explored the impact of different material of blood vessels, such as glass tube and plastic tube, on the prediction ability of the discrimination model. Furthermore, we searched for the modification method to reduce the influence from the blood tubes. Our work indicated that generalized diffuse reflectance method can greatly improve the discrimination accuracy. This research can greatly facilitate the application of noncontact discrimination method based on visible and near-infrared diffuse reflectance spectroscopy.

Experimental and Chemometric Optimization to Enhance the Performance of Near-infrared Diffuse Reflectance Spectroscopy

ABSTRACTApproaches for improving the performance of near-infrared diffuse reflectance spectroscopy (NIRDRS) have been extensively studied. The silver mirror was found to be a substrate to enhance the detection ability of this technique. For further optimization of the method, experimental and chemometric efforts were made in this study. Lysozyme samples of different concentrations were spotted on the silver mirror for measuring the spectra. Low-resolution spectra with repeated measurements were used for increasing the signal-to-noise ratio, and the chemometric methods of variable selection and nonlinear modeling were used to make the quantitative model more effective and accurate. The results show that the quality of the spectra was improved and a satisfactory quantitative model was obtained after optimization. The maximum deviation for the prediction samples is only 2.98 µg, which is much lower than the reported values in previous papers. Significant advances were obtained for NIRDRS for trace analysis.

Melanin quantification by in vitro and in vivo analysis of near-infrared fluorescence.

Objective measurements of melanin can provide important information for differentiating melanoma from benign pigmented lesions and in assessing pigmentary diseases. Herein, we evaluate near-infrared (NIR) fluorescence as a possible tool to quantify melanin. Various concentrations of in vitro Sepia melanin in tissue phantoms were measured with NIR fluorescence and diffuse reflectance spectroscopy. Similar optic measurements were conducted in vivo on 161 normal human skin sites. Diffuse reflectance spectroscopy was used to quantify the melanin content via Stamatas-Kollias algorithm. At physiologic concentrations, increasing in vitro melanin concentrations demonstrated higher fluorescence that was linearly correlated (R2 =0.99, p<.001). At higher concentrations, the fluorescence signal plateaued. A linear relationship was also observed with melanin content in human skin (R2 =0.59, p<.001). Comparing the fluorescence and reflectance signals with in vitro and in vivo samples, the estimated melanin concentration in human skin ranged between 0 and 1.25mg/ml, consistent with previous quantitative studies involving invasive methods.

Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy.

Determination of Serum Urea by Near-infrared Diffuse Reflectance Spectroscopy Using Silver Mirror for Enhancing the Detectability†

Determination of Serum Urea by Near-infrared Diffuse Reflectance Spectroscopy Using Silver Mirror for Enhancing the Detectability†

Temperature dependent solvothermal synthesis of Cu-Sb-S nanoparticles with tunable structural and optical properties

Copper based chalcogenide nanoparticles were synthesized by convenient solvothermal method using ethylene glycol as solvent and Polyvinylpyrrolidone as surfactant, followed by deposition of thin films using drop casting method. Here, the reactions were performed at different temperature from 200°C to 240°C in steps of 10°C. Systematic investigation revealed that as temperature increases significant changes were occurring on the crystal structure, optical properties and electrical performance of the material. The influence of different temperature was responsible for the formation of pure Cu3SbS4 to CuSbS2 nanoparticles. The obtained nanopowders and deposited films were characterized by X-ray diffraction, Raman analysis, high resolution transmission electron microscope, field emission scanning electron microscope with energy dispersive spectrometer, UV–Vis–NIR diffuse reflectance spectroscopy and hall measurement. Optical and electrical studies revealed that nanoparticles were in the range of 0.88–1.45eV with p-type conductivity, indicating their potential application as a p-type absorber layer in thin film solar cells.

Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Fast Qualitative and Quantitative Assessment of Nectarine Quality

Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. ‘Big Top’ and cv. ‘Magique’, were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (R 2 P of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.

Dissolution assessment of allopurinol immediate release tablets by near infrared spectroscopy

The purpose of this study was to develop a NIR spectroscopic method for assessment of drug dissolution from allopurinol immediate release tablets. Thirty three different batches of allopurinol immediate release tablets containing constant amount of the active ingredient, but varying in excipients content and physical properties were introduced in a PLS calibration model. Correlating allopurinol dissolution reference values measured by the routinely used UV/Vis method, with the data extracted from the NIR spectra, values of correlation coefficient, bias, slope, residual prediction determination and root mean square error of prediction (0.9632, 0.328%, 1.001, 3.58, 3.75%) were evaluated. The obtained values implied that the NIR diffuse reflectance spectroscopy could serve as a faster and simpler alternative to the conventional dissolution procedure, even for the tablets with a very fast dissolution rate (>85% in 15minutes). Apart from the possibility of prediction of the allopurinol dissolution rate, the other multivariate technique, PCA, provided additional data on the non-chemical characteristics of the product, which could not be obtained from the reference dissolution values. Analysis on an independent set of samples confirmed that a difference between the UV/Vis reference method and the proposed NIR method was not significant. According to the presented results, the proposed NIR method may be suitable for practical application in routine analysis and for continuously monitoring the product's chemical and physical properties responsible for expected quality.

Complete Soil Texture is Accurately Predicted by Visible Near-Infrared Spectroscopy

The particle-size curve (PSC) defines the continuous size distribution of mineral particles <2 mm. It is used for soil classification and to derive functional soil parameters such as the soil–water characteristic (SWC) curve, soil hydraulic properties, and gas transport properties. Conventional methods for measuring texture are time-consuming and most methods only provide discrete particle-size intervals. The Rosin–Rammler and Fredlund functions enable a continuous description of the size distribution of mineral particles using two and three fitting parameters, respectively. Visible near-infrared diffuse reflectance spectroscopy (vis-NIRS) is a time-saving and well-known alternative soil analysis method. In this study vis-NIRS was used to indirectly obtain PSCs by predicting the fitting parameters of the Rosin–Rammler (αR, βR) and Fredlund (αF, nF, and mF) functions. A total of 431 soil samples from 7 agricultural fields in Denmark and Greenland were analyzed for soil texture (clay: 0.028–0.426 kg kg⁻¹) and organic matter (OM) content (0.018–0.143 kg kg⁻¹). The Rosin–Rammler and Fredlund functions were fitted to the PSCs. Soil diffuse reflectance was measured from 400 to 2500 nm with a spectrometer. The important spectral regions for correlating αR, βR, αF, nF, mF, and OM to spectra were selected using forward interval partial least squares (iPLS) regression on a calibration set. The soil spectra showed high correlation to PSC function parameters and OM content for the validation set. Further, vis-NIRS cross-validation models for the fitting parameters of the Rosin–Rammler and Fredlund functions were built on all samples and used as input for the PSCs, generating RMSE values of 4.2 and 3.5%, respectively. Both PSC functions convincingly covered the PSC variation within fields, although the Fredlund function performed slightly better. From one vis-NIRS scanning the complete texture comprising the PSC and the OM content was successfully characterized.

A pressure vessel for studying gas foaming of thermosetting polymers: sorption, synthesis and processing

We herein report the design of an apparatus for studying the concurrent chemo-physical processes occurring during gas foaming of thermosetting polymers. In particular, to address the recent interest in combining the gas (physical) foaming with the classical (chemical) polyurethane foaming, a novel instrumented pressure vessel was designed for investigating: i) gas sorption under high pressure on the different reactants, kept separate; ii) synthesis under high gas pressure, upon mixing and iii) foaming upon release of the pressure. The design of the new pressure vessel relies on two key features. From the processing side, we make use of a rubber impeller to keep the two reactants separate during gas sorption and to allow for an efficient mixing at the end of the sorption stage. From the analytic side, we utilized a sapphire window beneath the sample holder to use diffuse reflectance near-infrared spectroscopy to measure both the amount of sorbed gas and the reaction kinetics under gas pressure. Preliminary results are reported for the polyol-isocyanate/CO2 system.

Sensor data fusion for soil health assessment

Assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health data. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region of Missouri, USA, visible and near-infrared (VNIR) diffuse reflectance spectroscopy has successfully been used to estimate biological components of soil health as well as Soil Management Assessment Framework (SMAF) scores. In contrast, estimation models for important chemical and physical aspects of soil health have been less successful with VNIR spectroscopy. The primary objective of this study was to apply a sensor fusion approach to estimate soil health indicators and SMAF scores using VNIR spectroscopy in conjunction with soil apparent electrical conductivity (ECa), and penetration resistance measured by cone penetrometer (i.e., cone index, CI). Soil samples were collected from two depths (0–5 and 5–15cm) at 108 locations within a 10-ha research site encompassing different cropping systems and landscape positions. Soil health measurements and VNIR spectral data were obtained in the laboratory, while CI and ECa data were obtained in situ. Calibration models were developed with partial least squares (PLS) regression and model performance was evaluated using coefficient of determination (R2) and root mean square error (RMSE). Models integrating ECa and CI with VNIR reflectance data improved estimates of the overall SMAF score (R2=0.78, RMSE=7.21%) relative to VNIR alone (R2=0.69, RMSE=8.41%), reducing RMSE by 14%. Improved models were also achieved for estimates of the individual chemical, biological, and physical soil health scores, demonstrating reductions in RMSE of 2.8, 5.4, and 10.0%, respectively. The results of this study illustrate the potential for rapid quantification of soil health by fusing VNIR sensor data with auxiliary data obtained from complementary sensors.

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy.

Moisture content (MC) is one of the most important quality parameters of green coffee beans. Therefore, its fast and reliable measurement is necessary. This study evaluated the feasibility of near infrared (NIR) spectroscopy and chemometrics for rapid and non-destructive prediction of MC in intact green coffee beans of both Coffea arabica (Arabica) and Coffea canephora (Robusta) species. Diffuse reflectance (log 1/R) spectra of intact beans were acquired using a bench top Fourier transform NIR instrument. MC was determined gravimetrically according to The International Organization for Standardization (ISO) 6673. Samples were split into subsets for calibration (n = 64) and independent validation (n = 44). A three-component partial least squares regression (PLSR) model using raw NIR spectra yielded a root mean square error of prediction (RMSEP) of 0.80% MC; a four component PLSR model using scatter corrected spectra yielded a RMSEP of 0.57% MC. A simplified PLS model using seven selected wavelengths (1155, 1212, 1340, 1409, 1724, 1908, and 2249 nm) yielded a similar accuracy (RMSEP: 0.77% MC) which opens the possibility of creating cheaper NIR instruments. In conclusion, NIR diffuse reflectance spectroscopy appears to be suitable for rapid and reliable MC prediction in intact green coffee; no separate model for Arabica and Robusta species is needed.

Combining reflectance spectroscopy and the digital elevation model for soil oxidizable carbon estimation

As soil oxidizable carbon (Cox) has several different absorptions in the visible– and near infrared (vis–NIR) region due to its complex composition, multivariate calibration techniques such as multiple linear regression (MLR), partial least squares regression (PLSR), support vector machines (SVM) or random forest (RF) can be advantageously used to obtain a good prediction. Besides that, the content of Cox is often affected by the character of the terrain, mainly due to prevailing water regime with associated transport and sedimentation processes. Therefore, the question arises; if predictive models calibrated by combining vis–NIR diffuse reflectance spectroscopy (vis–NIR DRS, 350–2500nm) and the digital elevation model (DEM) derivatives will provide a more accurate estimate of Cox. Focused on a sloping arable land (100ha) affected by distinct water erosion, we tested for this purpose two conceptually different predictive approaches that differ in the nature of the spectroscopic predictor variables. In Approach A that relied on absorption feature (AF) parameters, the inclusion of DEM derivatives resulted in improved Cox prediction using all the tested calibration techniques, i.e. MLR, RF, PLSR and SVM. The MLR prediction that was the most accurate among all others improved from R2cv=0.81 (vis–NIR DRS dataset) or 0.50 (DEM derivatives dataset) to 0.84 (combination of both). For that prediction especially AF centered at 500, 700, 900, 1800, 1900, 2200 and 2400nm, as well as elevation, LS factor and plan curvature were important. In contrast, in the Approach B that relied on reflectance (RF and PLSR) or normalized reflectance (SVM) values at each wavelength, no positive effect of inclusion of DEM derivatives was observed.