Near-infrared Diffuse Reflectance Spectroscopy Research Articles

Theoretical and experimental investigation of BaY2(MoO4)4:xSm3+ phosphors

In this paper, the novel BaY2(MoO4)4:xSm3+ phosphors with strong red emission and high thermal stabilization were prepared by a conventional method of high temperature solid state reaction in air atmosphere. Morphology, phase purity, element distribution, UV–Vis–NIR diffuse reflectance spectroscopy (DRS), and luminescence properties were investigated in detail. The crystal structure of the BaY2(MoO4)4 host was refined with the aid of the Rietveld refinement method by the GSAS program. The electronic band structure and phonon dispersion were performed using plane-wave density functional theory (DFT). The prepared BaY2(MoO4)4:Sm3+ phosphor exhibits excellent red emission under near-ultraviolet excitation. Strong red emission intensity and high thermal stability suggest potential application in backlighting or phosphor-covered white light-emitting diodes (pc-LEDs).

Near-Infrared Spectroscopy for Growth Estimation of Spirulina platensis Cultures

The present study proposes the use of Near-Infrared (NIR) spectroscopy as a rapid method for estimating the growth of Spirulina platensis cultures, avoiding any sample manipulation or pretreatment. NIR spectroscopy in diffuse reflectance mode was used on culture volumes as received, with Principal Component Analysis (PCA) and Partial Least Squares (PLS) linear regression, for developing the calibration model in the wavelength range of 1000–2500 nm, in order to choose the appropriate wavelength to estimate the growth of the microalga. The local reflectance maximum at 1062.6 nm, connected with reduced water absorption and scattering effects by the microalga, was identified from PCA as the positive peak in the first loading plot, correlating diffuse reflectance with dilution levels. The calibration curve of diffuse reflectance at 1062.6 nm in response to dilution presented strong linearity, supported by a coefficient of determination (R2) of 0.995. Cross-validation of NIR spectra with a S. platensis culture confirmed the method’s reliability, showing that the growth follows an exponential pattern. The study shows that diffuse reflectance NIR spectroscopy can be used for the rapid monitoring of Spirulina platensis growth.

Non-destructive measurement of the dispersion of High-density polyethylene/Polystyrene blends using near-infrared diffuse reflectance spectroscopy based on deep learning

The dispersion of polymer blends significantly influences their overall performance. However, current methods for characterizing dispersion are destructive and time-consuming. In this study, a near-infrared (NIR) regression model was first developed for the measurement of cumulative particle size distribution function (CDF) of dispersed particles in polymer blends, thus indirectly and quantitively characterizing dispersion. High-density Polyethylene/Polystyrene (HDPE/PS) with varying dispersed particle sizes were prepared by adjusting the content of PS, flow field intensity during processing, and viscosity ratio between HDPE and PS. Scanning electron microscopy (SEM) was used to provide reference particle size values for calibrating the NIR data. Deep-learning methods were used for the model building. Compared to traditional partial least-square regression, deep learning methods demonstrated superior fitting ability, The best-performing model, an MLP with three hidden layers, achieved an coefficient of determination (R2) of approximately 0.93 on the independent test set. The predicted CDFs closely matched the actual values, with no significant deviations observed. This study provides a non-destructive and time-saving new method for characterizing the dispersion of polymer blends.

Predicting Hass Avocado Maturity with NIR Spectroscopy for Non-invasive Dry Matter Estimation in Hass Avocados

Aims: To develop a rapid, non-invasive method for predicting Hass avocado maturity using near- infrared diffuse reflectance spectroscopy (NIR-DRS) combined with machine learning algorithms, and to identify the optimal NIR wavelength range for accurate dry matter content prediction. Study Design: An experimental design involving spectral data collection from Hass avocados and the development of machine learning models for dry matter prediction. Methodology: Spectral data from 200 Hass avocados were collected using near-infrared diffuse reflectance spectroscopy (900-2500 nm). To improve the quality of the spectral data and reduce noise, standard normal variate was used to correct for scattering and remove unwanted variability in the spectral data. PCA was then performed to reduce the dimension of the spectral data while retaining the most significant variance. Following preprocessing, machine learning models, including Convolutional Neural Networks (CNN), were trained to predict dry matter content, and the optimal wavelength range was determined for accurate prediction. Results: The CNN model demonstrated superior performance for dry matter prediction with R² of 0.91 in the testing set. The wavelength range of 1000-1500 nm was identified as optimal, offering accurate predictions while reducing computational complexity. This range shows potential for developing cost-effective NIR devices for real-time maturity assessment. Conclusion: NIR spectroscopy combined with machine learning offers a non-invasive, accurate method for predicting avocado dry matter, with potential applications for quality control in the avocado industry. The findings demonstrate that focusing on specific wavelength ranges can lead to more affordable and efficient NIR solutions.

Structure and optical properties of alkali rare-earth double phosphates of M3RE(PO4)2 (M = K, Rb; RE = Y, La, and Lu)

Designing and synthesizing phosphate nonlinear optical materials with novel structures and excellent properties remains challenging. In this work, the electronic structure and optical properties (second harmonic generation (SHG) response or birefringence) of alkali rare-earth double phosphates M3RE(PO4)2 (M = K, Rb; RE = Y, La, and Lu) are systematically investigated. It is worth mentioning that P31m-Rb3Lu(PO4)2 was successfully synthesized by a flux-method, and the UV–vis–NIR diffuse reflectance spectroscopy showed that Rb3Lu(PO4)2 exhibits a short absorption edge at 207 nm and a high transmittance of 77.9 %. Based on first-principles calculations, the results show that the birefringence of the M3RE(PO4)2 series of crystals exhibits significant differences (0.009-0.028@1064 nm). Subsequently, analysis of the electronic structure and the real-space atom cutting method reveals that rare-earth polyhedra play a major role in enhanced birefringence. Overall, this work enriches the study of nonlinear optical crystals of rare earth phosphates and provides a case for further exploration.

Radiative recombination model for BiSeI microcrystals: unveiling deep defects through photoluminescence

Abstract Pnictogen chalcohalides are semiconductors that have emerged as promising materials for energy conversion due to their exceptional optoelectronic properties. Their electronic configuration (ns2), particularly for Bi- and Sb-based compounds, can be a key factor in efficient carrier transport and defect tolerance, similarly, to Pb-perovskites. In the present study, the Bi-containing chalcohalide, bismuth selenoiodide (BiSeI) was synthesized via isothermal heat treatment of binary precursors in evacuated quartz ampoules. The synthesized BiSeI microcrystals exhibited a characteristic needle-like morphology and a near-stoichiometric composition. Both indirect and direct band gap energies of BiSeI were determined by ultraviolet–visible–near-infrared diffuse reflectance spectroscopy, with room temperature values of 1.17 eV and 1.29 eV, respectively. This study presents the first experimental investigation of the photoluminescence properties of BiSeI microcrystals resulting in a recombination model involving multiple defect states. This work provides valuable insights into the defect structure and recombination mechanisms within BiSeI, paving the way for further exploration of its potential in optoelectronic devices.

Authentication of oats and discrimination from their gluten-containing adulterants using NIR diffuse reflectance spectroscopy and multivariate analysis

Oat (Avena sativa L.) is recognized for its nutritional value and gluten-free status, however, ensuring its authenticity and purity is crucial, given the potential for contamination with gluten-containing grains. In this study, near-infrared spectroscopy coupled with chemometrics were employed to authenticate oat flour (from different forms of oat; oat groats, steel-cut and rolled oats) and distinguish it from common gluten-containing adulterants including wheat, farro, triticale, barley, rye, and ryegrass. Both unsupervised and supervised chemometric methodologies, encompassing PCA, SIMCA, and OPLS-DA, were applied. Both SIMCA and OPLS-DA models displayed 100% sensitivity, enabling reliable identification of oat flour and detection of potential adulteration with these gluten-containing grains with specificity of 97.78% and 100%, respectively. In the SIMCA model for oat groats and adulterated mixtures, samples with 1% and 2% adulteration were incorrectly classified as oat groats, yet successful discrimination from deliberately-adulterated mixtures was accomplished through the OPLS-DA model with 100% specificity. Moreover, PLS regression analysis was employed to precisely quantify the levels of adulterants in oat groats flour. The models exhibited reliable performance, as reflected in Root Mean Square Error of Calibration (RMSEC) values. Validation of these models using test samples provided further confirmation of their efficacy in detecting sample adulteration and ensuring product authenticity, all while maintaining a high sample throughput rate.

Temporal evaluation of soil chemical quality using VNIR and XRF spectroscopies

New soil sensing technologies, such as visible and near-infrared diffuse reflectance spectroscopy (VNIR) and X-ray fluorescence spectroscopy (XRF), emerged as alternatives to low-cost, rapid, and environmental-friendly soil analyses. Studies involving the prediction of integrated soil quality indices using individual and combined soil sensing techniques have not been conducted yet on tropical soils, especially regarding the temporal stability of models' performance. This study evaluated tropical soils to give insight into the potential of using individual and combined VNIR and XRF sensors to predict the Soil Chemical Quality Index (SCQI) and its sub-functions (Nutrient Availability, Acidity/Al Toxicity, and Nutrient Storage and Cycling). Furthermore, the temporal stability of these models' performance and a spiking strategy to mitigate performance loss throughout crop seasons were assessed. We conducted this study using 924 soil samples collected over three years (2018, 2019, and 2022) in a field managed with an integrated crop-livestock system (200 ha) in a Brazilian tropical area. We determined the SCQI and its sub-functions for each sample from 15 soil quality indicators. Soil samples were scanned with VNIR (350–2500 nm) and XRF (0–35 kV) spectrometers. VNIR spectroscopy predicted the SCQI, Nutrient Availability, and Nutrient Storage and Cycling with satisfactory accuracy (models classified as good, good, and excellent, respectively), outperforming the XRF technique and the fusion approach. The prediction performance of SCQI and its sub-functions does not show temporal stability. However, the performance loss can be mitigated using the spiking strategy, i.e., using new data from subsequent seasons to recalibrate the global model.

Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits' spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000-4000 cm-1. The hardness of passion fruit's outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Near-Infrared Spectroscopy: Assessment of Soil Organic Carbon Stock in a Colombian Oxisol

Soil organic carbon (SOC) is a property known for its influence on the physical, chemical, and biological characteristics of soils, which are essential when assessing their quality. SOC stock (SOCS) monitoring is a key task in climate change mitigation studies. However, the resources necessary to obtain the information required by these studies tend to be high. The objective of this study was to develop a model for estimating the SOCS of a Colombian oxisol using near-infrared (NIR) diffuse reflectance spectroscopy. In a sampling scheme of 70 points distributed over 248 ha, 313 soil samples were collected in five defined depth intervals of 10 cm each, from 0 to 50 cm. SOC was determined through an elemental analyzer, and bulk density (BD) by means of sampling cylinders. A NIRFlex spectrometer was used to acquire spectral signatures in the NIR range from the processed soil samples, and, together with the data measured in the laboratory, a statistical analysis was performed using partial least squares regression (PLSR) in order to calibrate the spectral models. Based on the residual prediction deviation (RPD), the root mean square error (RMSE), and the coefficient of determination (R2) of the validation groups, a highly representative model was achieved for the estimation of SOCS (R2 = 0,93; RMSE = 2,12 tC ha-1; RPD = 3,69), which was also corroborated with geostatistical interpolation surfaces and depth splines. This research showed NIR diffuse reflectance spectroscopy to be a viable technique for SOCS estimation in the study area.

Critical evaluation of analytical methodologies for the discrimination of hemp and drug-type cannabis

Classification of cannabis samples as hemp- or drug-type is an ever-increasing problem worldwide. Colorimetric tests and infrared spectroscopy were selected and critically compared among the available techniques. Cannabis samples with different THC and CBD concentration were analyzed by paper-based analytical devices (µPAD) using the 4-aminophenol colorimetric reaction, attenuated total reflectance mid infrared spectroscopy (ATR-MIR), and diffuse reflectance near infrared spectroscopy (DR-NIR). For the µPAD analysis, cannabis samples were extracted with methanol and later mixed with reagents to develop the colored complex. On the other hand, MIR and NIR spectra were obtained directly on raw samples, and partial least square discriminant analysis (PLS-DA) was used for cannabis classification. All the selected methodologies successfully discriminate between hemp-type and drug-type samples, being suitable to be used in-field. Additionally, assayed methodologies together a reference gas chromatography mass spectrometry (GC–MS) were evaluated and compared from a Green Analytical point of view using the AGREE criteria.

In-Line Detection of Bed Fluidity in Gas-Solid Fluidized Beds Using Near-Infrared Spectroscopy.

A novel approach was developed to detect bed fluidity in gas-solid fluidized beds using diffuse reflectance near-infrared (NIR) spectroscopy. Because the flow dynamics of gas and solid phases are closely associated with the fluidization state, the fluidization quality can be evaluated through hydrodynamic characterization. In this study, the baseline level of NIR spectra was used to quantify the voidage of the fluidized bed. Two indicators derived from the NIR baseline fluctuation profiles were investigated to characterize bed fluidity, named bubble proportion and skewness. To establish a robust fluidity evaluation method, the relationships between the indicators and bed fluidity were investigated under different conditions firstly, including static bed height and average particle size. Then, a generalized threshold was identified to distinguish poor and good bed fluidity, ensuring that the probability of the α- and β-errors was less than 15% regardless of material conditions. The results show that both indicators were sensitive to changes in bed fluidity under the investigated conditions. The indicator of skewness was qualified to detect bed fluidity under varied conditions with a robust threshold of 1.20. Furthermore, the developed NIR method was successfully applied to monitor bed fluidity and for early warning of defluidization in a laboratory-scale fluidized bed granulation process.

Influence of powder stream density on near infrared measurements upon scale-up of a simulated continuous process

Near-infrared (NIR) spectroscopy is a powerful process analytical tool for monitoring chemical constituents in continuous pharmaceutical processes. However, the density variation introduced when quantitative NIR measurements are performed on powder streams at different flow rates is a potential source of a lack of model robustness. Since different flow rates are often required to meet the production requirements (e.g., during scale-up) of a continuous process, the development of efficient strategies to characterize, understand, and mitigate the impact of powder density on NIR measurements is highly desirable. This study focused on assessing the effect of powder physical variation on NIR by enabling the in-line characterization of powder stream density in a simulated continuous system. The in-line measurements of powder stream density were facilitated through a unique analytical interface to a flowing process. Powder streams delivered at various design levels of flow rate and tube angle were monitored simultaneously by NIR diffuse reflectance spectroscopy, live imaging, and dynamic mass characterization. Statistical analysis and multivariate modeling confirmed powder density as a significant source of spectral variability due to flow rate. Besides providing broader process understanding, results elucidated potential mitigation strategies to facilitate effective continuous process scale-up while ensuring NIR model robustness against density.

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy.

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

The potential of near-infrared diffuse reflectance spectroscopy as a tool for characterizing the particle size of dispersion phase in polymer blends

In this study, we investigated the impact of the particle size of dispersion phase in polymer blends on the near-infrared (NIR) diffuse reflectance spectrum. Polypropylene/Polystyrene (PP/PS) blends with different particle sizes of dispersion phase were prepared at the weight ratio of 80/20. When compared to the spectra of the virgin PP, PS, and two-layer sheet of PP-PS, a clear baseline tilt is revealed by the spectrum of the PP/PS blends. By combining the diameter data of the particle size of the PS phase counted by SEM image, the reflectance of the spectrum decreases as the diameter of the particle size of the PS phase increases. In the Monte Carlo simulation results, the number of received scattering photons decreases with an increase in particle size and wavelength respectively, which can well explain the actual spectra variation. The volume mean diameter (Dv) predicted model was built through principal component regression, and the model showed good performance which correlation coefficient (R2) was 0.9532 and root mean square errors of calibration (RMSEC) was 0.2792 μm.

Portable LWNIR and SWNIR spectroscopy with pattern recognition technology for accurate and nondestructive detection of hidden mold infection in citrus

An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy (NIRDRS) and chemometric methods. Penetrability of NIRDRS light on the peel of Chunjian hybrid citrus was studied. The results show that NIRDRS light can penetrate the peel to a certain extent, while the penetrability of short-wave near infrared (SWNIR) was better than that of long-wave near infrared (LWNIR). The identification models of hidden mold infection were established with five pattern recognition methods combined with different wavelength bands. The results show that the identification models of LWNIR were much better than those of SWNIR. 100% identification accuracies of LWNIR were obtained with soft independent pattern classification (SIMCA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and the optimized pretreatment methods. In addition, the developed models were further validated by the external validation set collected one month later.

Structural, optical and magnetic studies of sol-gel synthesized Mg-doped pure anatase TiO2 nanoparticles for spintronic and optoelectronics applications

Here in we reported the synthesis of MgxT1−xO2 (for x = 0.00, 0.01, 0.02, 0.03, 0.04) nanoparticles via simple sol-gel technique. Mg-doped TiO2 is a hopeful material for the study of the origin of room-temperature ferromagnetism (RTFM) in dilute magnetic semiconductors (DMS) systems, but very scanty work on this material has been reported. The as-synthesized samples were subjected to X-ray diffraction (XRD) which shows that the samples aligned with tetragonal anatase phase of TiO2. UV\\Vis\\NIR diffuse reflectance spectroscopy analysis showed a slight increase in TiO2 energy band gap with Mg doping from 3.25eV to 3.33 eV as a result of blue shift in absorbance edge of the sample. The obtained recombination of photo-generated electrons and holes is excellently curbed by Mg doping which was examined by the photoluminescence study and the blue shift in the most intense Eg (3) Raman mode confirms integrated Mg in TiO2 lattice. As revealed by Field Emission Scanning Electron Microscopy (FESEM), the morphology of the samples showed irregularly distributed nanoparticles and to examine elemental composition of the samples, Energy Dispersive X-ray (EDX) were carried out which stipulates all elements like Ti and Mg are in the sample. Soft ferromagnetism at low magnetic field with hysteresis loop was noticed in all the samples, with values of coercivity in the range 206–309Oe. Maximum value of saturation magnetization was evident by the 2% doped sample. These materials can find applications in electronics, spintronics and magneto-optics devices.

Detection of the Adulteration of Dendrobium Huoshanense with Dendrobium Henanense by UV-Vis-Shortwave Near-Infrared Diffuse Reflectance Spectroscopy Combined with Chemometrics.

Dendrobium huoshanense (DHS) is a classic traditional Chinese medicine (TCM) with distinctive medicinal benefits and great economic worth; nevertheless, because of similar tastes and looks, it is simple to adulterate with less expensive substitutes (such as Dendrobium henanense [DHN]). This work aimed to develop a reliable tool to detect and quantify the adulteration of DHS with DHN by using UV-Vis-shortwave near-infrared diffuse reflectance spectroscopy (UV-Vis-SWNIR DRS) combined with chemometrics. Adulterated samples prepared in varying concentrations (0-100%, w/w) were analyzed with UV-Vis-SWNIR DRS methods. Partial least-square-discriminant analysis (PLS-DA) and partial least-squares (PLS) regression techniques were used for the differentiation of adulterated DHN from pure DHS and the prediction of adulteration levels. The PLS-DA classification models successfully differentiated adulterated and nonadulterated DHS with an over 100% correct classification rate. UV-Vis-SWNIR DRS data were also successfully used to predict adulteration levels with a high coefficient of determination for calibration (0.9924) and prediction (0.9906) models and low error values for calibration (3.863%) and prediction (5.067%). UV-Vis-SWNIR DRS, as a fast and environmentally friendly tool, has great potential for both the identification and quantification of adulteration practices involving herbal medicines and foods. UV-Vis-SWNIR DRS combined with chemometrics can be applied to identify and quantify the adulteration of herbal medicines and foods.

Spectral analysis of multiple scattering factors of turbid media for glucose measurement using near-infrared spectroscopy.

Near-infrared (NIR) diffuse reflectance spectroscopy has been widely used for non-invasive glucose measurement in humans, as glucose can induce a significant and detectable optical signal change in tissue. However, the scattering-dominated glucose spectrum in the range of 1000 to 1700nm is easily confused with many other scattering factors, such as particle density, particle size, and tissue refractive index. Our aim is to identify the subtle distinctions between glucose and these factors through theoretical analysis and experimental verification, in order to employ suitable methods for eliminating these interferences, thus increasing the accuracy of non-invasive glucose measurement. We present a theoretical analysis of the spectra of 1000 to 1700nm for glucose and some scattering factors, which is then verified by an experiment on a 3% Intralipid solution. We found that both the theoretical and experimental results show that the effective attenuation coefficient of glucose has distinct spectral characteristics, which are distinct from the spectra caused by particle density and refractive index, particularly in the range of 1400 to 1700nm. Our findings can offer a theoretical foundation for eliminating these interferences in non-invasive glucose measurement, aiding mathematical methods to model appropriately and enhance the accuracy of glucose prediction.

Authentication and detection of common adulterants in clove buds (Syzygium aromaticum L.) powders and oils using near IR combined to multivariate analysis

This study investigates the use of NIR diffuse reflectance spectroscopy with multivariate analysis for quality control and authentication of clove buds powders as well as their oils. Unsupervised and supervised chemometric analysis techniques, including principal component analysis (PCA), data driven soft independent modeling of class analogy (DD-SIMCA) were implemented to authenticate clove and its oil and distinguish them from their adulterants. The SIMCA model showed 100% sensitivity and specificity values in detecting adulterants in clove powder samples and clove oil samples. This showcases the accuracy and reliability of the DD-SIMCA approach in accurately distinguishing between different classes.The models were validated using test samples, and the absence of noise modeling was confirmed through permutation. PLS regression analysis was utilized to measure levels of adulterants in clove powder and clove oil samples. The models produced good results, with RMSEC values ranging from 0.68 to 1.5% for clove oil and from 0.96 to 1.27% for clove powder. External validation was performed and the limits of quantitation ranged from 1.8% to 4.9% for clove powder and clove oil. The models can detect sample adulteration and ensure authenticity. The results showed that the suggested method and models can be used to detect sample adulteration, ensure authenticity, and have high sample throughput. The method had several advantages, including simplicity, speed of analysis, with no sample preparation needed.