Reflectance Near-infrared Spectra Research Articles

Near-Infrared Absorption Coefficients in Kidney Stone Minerals and Their Relation to Crystal Structure

To quantitatively understand laser ablation of kidney stones by near-infrared (NIR) lasers, accurate measurements of physical properties related to heating and mechanical failure are needed. While light scattering varies among individual stones, absorption coefficients for the materials that form the bulk of each kidney stone are measurable when scattering is controlled. We developed methods to grow single crystals with lengths and widths >20 μm for the minerals that form kidney stones: calcium oxalate monohydrate, calcium oxalate dihydrate, calcium hydrogen phosphate dihydrate, hydroxyapatite, anhydrous uric acid, and magnesium ammonium phosphate hexahydrate. These single crystals were used with an infrared microscope to measure their NIR absorption coefficients from 1.67 to 2.5 μm. The strong correlation between single crystal NIR spectra and diffuse reflectance NIR spectra indicates little anisotropy in absorption. Most minerals absorbed more strongly at 1.94 μm than 2.12 μm, the wavelengths of thulium fiber and holmium:YAG surgical lasers, respectively. For water bearing minerals, we attribute absorption mainly to water combination modes and assign NIR absorption peaks to specific water molecules and the OH bonds thereof where possible. Reported absorption coefficients can be used to quantitatively compare both laboratory investigations of ablation and surgical experience to physical properties for the first time.

Research on coal moisture analysis model based on near infrared spectroscopy

In order to make full use of the advantages of rapid and non-destructive measurement of coal moisture by near-infrared spectroscopy, the 29 original coal samples in the experiment were artificially humidified, and the moisture content of each coal sample was controlled within a different interval of 0-40%; diffuse reflectance near-infrared spectra were collected on the coal samples. The sample was selected according to the random method, Kennard-Stone (KS) method, and Rank-KS method for the selection of calibration set and prediction set. The prediction models of coal moisture content were established by multiple linear regression (MLR) and partial least squares algorithm (PLS) combined with different spectral pretreatment methods, furthermore, water modeling based on BP neural network was established according to optimal sample classification. The results show that when Rank-KS algorithm was used to select correction set and prediction set, the prediction ability of water content prediction model can be significantly improved by using either MLR or PLS, and the root mean square error of prediction (RMSEP) can be minimized; the moisture prediction model of the BP neural network algorithm based on the full-spectrum feature information is the best, the RMSEP value is the smallest among all models, and the model has the best prediction ability.

Evaluating basic density calibrations based on NIR spectra recorded on the three wood faces and subject to different mathematical treatments

Background: Near infrared (NIR) spectroscopy has been successfully applied to estimate the chemical, physical and mechanical properties of various biological materials, including wood. This study aimed to evaluate basic density calibrations based on NIR spectra collected from three wood faces and subject to different mathematical treatments. Methods: Diffuse reflectance NIR spectra were recorded using an integrating sphere on the transverse, radial and tangential surfaces of 278 wood specimens of Eucalyptus urophylla x Eucalyptus grandis. Basic density of the wood specimens was determined in the laboratory by the immersion method and correlated with NIR spectra by Partial Least Squares regression. Different statistical treatments were then applied to the data, including Standard Normal Variate, Multiplicative Scatter Correction, First and Second Derivatives, Normalization, Autoscale and MeanCenter transformations. Results: The predictive model based on NIR spectra measured on the transverse surface performed the best (R²cv = 0.85 and RMSE = 25.5 kg/m³) while the model developed from the NIR spectra measured on the tangential surface had the poorest performance (R²cv = 0.53 and RMSE = 46.8 kg/m³). The difference in performance between models based on original (untreated) and mathematically-treated spectra was minimal. Conclusions: Multivariate models fitted to NIR spectra were found to be efficient for predicting the basic density of Eucalyptus wood, especially when based on spectra measured on the transversal surface. For this data set, models based on the original spectra and mathematically treated spectra had similar performance. The reported findings show that mathematical transformations are not always able to extract more information from the spectra in the NIR.

A novel drying-free identification method of cashmere textiles by NIR spectroscopy combined with an adaptive representation learning classification method

Rapid identification of cashmere textiles using near-infrared (NIR) spectroscopy chronically has been limited by the high spectral similarity of cashmere and wool, moisture interference, and the deficiency of existing spectral pattern recognition methods. A novel drying-free method of NIR spectroscopy combined with an adaptive representation learning-based classification algorithm and moisture-containing samples preparation was proposed to tackle the issue. The diffused reflectance NIR spectra of textile samples with different colors, different textures, and varying moisture contents were collected. The Soft Independent Modeling by Class Analogy (SIMCA), the Support Vector Machine (SVM) and the new method were compared to build the cashmere identification models of the dried samples and the moisture-containing samples, respectively. The effects of common spectral pretreating methods and moisture variation on the identification of cashmere textiles were studied in detail. The performance of the new approach is much superior to others when identifying moisture-containing samples, an accuracy for cashmere textiles, 93.33% and one for the cashmere-wool blend, 96.60% achieved, respectively. This fruit has been integrated with a portable NIR-based textile analyzer and successfully applied to identify the real-world textiles free of drying. This research is of great practical significance to the market supervision and the production of cashmere textiles.

A Rapid and Nondestructive Method for the Prediction of Ibuprofen Tablet Hardness Using NIR Diffuse Reflectance Spectroscopy

Introduction: This study aimed at developing a technology to measure the hardness of Ibuprofen (IBU) tablets and optimize the IBU formulation using Near-infrared (NIR) spectroscopy. Materials and Methods: Tablets (400 mg±5%, 10mm in diameter) consisting of IBU, microcrystalline cellulose SH-103, carboxymethyl starch sodium, magnesium stearate, silicon dioxide were formed of various hardness (2kg, 4kg, 6kg, 8kg, 10kg, 12kg). The reflectance NIR spectra of various tablets were employed to establish 9 calibrations models, which were further used to predict tablet hardness by Partial least squares (PLS) and principal component regression (PCR) analysis. Results and Conclusion: Cross-validation with independent samples shows that PLS is the optimal predictive model. Which R2=0.9832, RSECV=0.334 and RSE=0.0669. This study established a new, simple, rapid, nondestructive and reliable methodology to optimize the IBU tablet hardness.

Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

Regional calibration models for predicting loblolly pine tracheid properties using near-infrared spectroscopy

This study developed regional calibration models for the prediction of loblolly pine (Pinus taeda) tracheid properties using near-infrared (NIR) spectroscopy. A total of 1842 pith-to-bark radial strips, aged 19–31 years, were acquired from 268 trees from 109 stands across the southeastern USA. Diffuse reflectance NIR spectra were collected at 10-mm increments from the radial face of pith-to-bark strips using a FOSS 5000 scanning spectrometer. A subset of the 10-mm samples was selected based on the NIR spectra uniqueness and placed in calibration (N = 1020) and validation (N = 998) test sets. The samples were macerated and the tracheid properties measured using an optical analyzer (Techpap MorFi Fiber and Shive Analyzer). Models were created using partial least squares (PLS) regression using the calibration samples, and their performance checked using the validation set. Prediction PLS models for tracheid length were strong when checked with the validation set [R p 2 = 0.87, standard error of prediction (SEP) = 0.23 mm, ratio of performance to deviation (RPD) = 2.8]. Prediction models for tracheid width had moderate fit statistics (R2 = 0.61, SEP = 1.6 μm, RPD = 1.6), but the SEP was similar to the measurement error of the camera (± 2 μm). The NIR wavelengths of importance were largely attributed to cellulose. The 1542 nm wavelength explained 77% of the variation in tracheid length (RMSE = 0.31 mm). These results demonstrate that NIR spectroscopy models for tracheid length and width can be developed from a diverse set of samples and still maintain prediction accuracy.

Detection of urinary estrogen conjugates and creatinine using near infrared spectroscopy in Bornean orangutans (Pongo Pygmaeus).

For promoting in situ conservation, it is important to estimate the density distribution of fertile individuals, and there is a need for developing an easy monitoring method to discriminate between physiological states. To date, physiological state has generally been determined by measuring hormone concentration using radioimmunoassay or enzyme immunoassay (EIA) methods. However, these methods have rarely been applied in situ because of the requirements for a large amount of reagent, instruments, and a radioactive isotope. In addition, the proper storage of the sample (including urine and feces) on site until analysis is difficult. On the other hand, near infrared (NIR) spectroscopy requires no reagent and enables rapid measurement. In the present study, we attempted urinary NIR spectroscopy to determine the estrogen levels of orangutans in Japanese zoos and in the Danum Valley Conservation Area, Sabah, Malaysia. Reflectance NIR spectra were obtained from urine stored using a filter paper. Filter paper is easy to use to store dried urine, even in the wild. Urinary estrogen and creatinine concentrations measured by EIA were used as the reference data of partial least square (PLS) regression of urinary NIR spectra. High accuracies (R(2) > 0.68) were obtained in both estrogen and creatinine regression models. In addition, the PLS regressions in both standards showed higher accuracies (R(2) > 0.70). Therefore, the present study demonstrates that urinary NIR spectra have the potential to estimate the estrogen and creatinine concentrations.

Reflectance Near-Infrared Spectroscopic Method with a Chemometric Technique Using Partial Least Squares Multivariate Calibration for Simultaneous Determination of Chondroitin, Glucosamine, and Ascorbic Acid

A reflectance near-infrared (RNIR) spectroscopy method was developed for simultaneous determination of chondroitin (CH), glucosamine (GO), and ascorbic acid (AS) in capsule powder. A simple preparation of the sample was done by grinding, sieving, and compression of the powder sample for improving RNIR spectra. Partial least squares (PLS-1 and PLS-2) was successfully applied to quantify the three components in the studied mixture using information included in RNIR spectra in the 4240-9780 cm(-1) range. The calibration model was developed with the three drug concentrations ranging from 50 to 150% of the labeled amount. The calibration models using pure standards were evaluated by internal validation, cross-validation, and external validation using synthetic and pharmaceutical preparations. The proposed method was applied for analysis of two pharmaceutical products. Both pharmaceutical products had the same active principle and similar excipients, but with different nominal concentration values. The results of the proposed method were compared with the results of a pharmacopoeial method for the same pharmaceutical products. No significant differences between the results were found. The standard error of prediction was 0.004 for CH, 0.003 for GO, and 0.005 for AS. The correlation coefficient was 0.9998 for CH, 0.9999 for GO, and 0.9997 for AS. The highly accurate and precise RNIR method can be used for QC of pharmaceutical products.

Automatic and rapid discrimination of cotton genotypes by near infrared spectroscopy and chemometrics.

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n = 120) and leaves (n = 123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

A spectral similarity measure using Bayesian statistics

A spectral similarity measure was developed that can differentiate subtle differences between two spectra. The spectra are digitalized into a vector. The difference between the two spectra is defined by a difference vector, which is one spectrum minus the other. The spectral similarity measure is transformed into a hypothesis test of the similarities and differences between the two spectra. The scalar mean of the difference vector is used as the statistical variable for the hypothesis test. A threshold for the hypothesis that the spectra are different was proposed. The Bayesian prior odds ratio was estimated from multiple spectra of the same sample. The posterior odds ratio was used to quantity the spectral similarity measure of the two spectra. Diffuse reflectance near-infrared spectra of tobacco samples of two formulations were used to demonstrate this method. The results show that this new method can detect subtle differences between the spectra.

Comparison of near-infrared and Raman spectroscopy for the determination of the density of polyethylene pellets

In this study, we compare near-infrared (NIR) and Raman spectroscopy for the determination of the density of linear low density polyethylene (PE) (in a pellet form). As generally known, Raman spectral features are more selective than those of NIR for most chemical samples. NIR spectroscopy has been more extensively used for the quantitative analysis of polymers, but Raman spectroscopy is the better choice as long as the problem of reproducibility of Raman measurements (especially for solid samples), mostly resulting from insufficient sample representation due to probing only localized chemical information and the sensitivity of sample placement with regard to the focal plane, can be overcome. To improve sample representation and reproducibility of Raman measurements, we have employed the wide area illumination (WAI) Raman scheme, capable of illuminating a laser onto a large sample area (28.3 mm 2) for Raman spectral collection (a 6-mm laser spot with a focal length of 248 mm). Diffuse reflectance NIR spectra of PE pellets were collected using a sample moving system which allowed for the scanning of large areas. The prediction error was 0.0008 g cm −3 for Raman spectroscopy and 0.0011 g cm −3 for NIR spectroscopy. The harmonization of inherently selective Raman features and a reproducible spectral collection with correct sample representations using the WAI scheme led to an accurate determination of the density of the PE pellets.

New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.

Nondestructive Direct Determination of Heroin in Seized Illicit Street Drugs by Diffuse Reflectance near-Infrared Spectroscopy

A new method has been developed for the fast and nondestructive direct determination of heroin in seized street illicit drugs using partial least-squares regression analysis of diffuse reflectance near-infrared spectra. Data were obtained from untreated samples placed in standard glass chromatography vials. A heterogeneous population of 31 samples, previously analyzed by a reference method, was employed to build the calibration model and to have a separated validation set. Based on the use of zero-order data for a calibration set of 21 samples, after standard normal variate and quadratic linear removed baseline correction (detrending), in the wavelength range from 1111 to 1647 nm, 8 PLS factors were enough to obtain a root-mean-square error of prediction of 1.3% w/w, with a quality coefficient of 10% for the estimation of the accuracy error in the prediction of heroin concentration in unknown samples and a residual predictive deviation of 5.4.

Determination of weight percent gain in solid wood modified with in situ cured furfuryl alcohol by near-infrared reflectance spectroscopy

Near-infrared spectroscopy was used to determine the degree of treatment in Scots pine solid wood modified with in situ cured furfuryl alcohol. The treatment range was approximately 0 to 30% weight gain relative to wood dry mass. Reflectance near-infrared spectra were recorded – using a conventional, large sample cup – directly on wood surfaces without sample preparation. Partial Least Squares regressions were used to construct a model for weight percent gain; initially for one data set but later extended to an external test set not treated fully in the same way as the original set. This gave rise to an increase in the prediction error which was sought neutralised by addition of a small fraction of the new samples in the original calibration set. Four spectral ranges and 13 pre-processing methods were tested and the final model with addition of 30% of the new samples in the original calibration set resulted in a model with a Root Mean Square Error of Prediction of 1.7 ± 0.1% weight gain and a R 2 of 0.97 ± 0.01.

The Effect of Spectral Pre-Treatments on the Partial Least Squares Modelling of Agricultural Products

Spectral pre-treatment, such as scatter correction, smoothing and derivatisation is considered, to the point of being almost folklore, an integral component to the development of near infrared (NIR) partial least squares (PLS) regression equations. This study was undertaken to examine the importance of pre-treatments. Diffuse reflectance NIR (1100–2500 nm) spectra of ground wheat and forages were separately analysed. For ground wheat, the effect of spectral pre-treatment on the PLS equations for protein content and sodium dodecyl sulphate (SDS) sedimentation volume (a protein quality index) was examined. For forages, similar examinations were performed on crude protein content and lignin content. Results indicate that while pre-treatment is indeed important, statistical significance, as determined by the F-test of correlated variances, is often not established. Protein content calibrations tend to be enhanced by scatter correction, as opposed to smoothing or derivatisation, whereas the SDS sedimentation volume and lignin content calibrations favoured these convolution functions. It is recommended that the selection of the best pre-treatment for an analyte be based on the combination of statistical testing and the modeller's judgement.

Near-Infrared Spectrometric Determination of Blood pH

Background. Reflectance near-infrared spectroscopy (600–2200 nm) can noninvasively probe deep into tissues. Blood is the predominant absorber of near-infrared light in biological tissues. We investigated the feasibility of using reflectance near-infrared spectroscopy to measure blood pH in vitro. Methods. Reflectance near-infrared spectra (600–2200 nm) were obtained with a fiberoptic probe immersed in diluted human packed red blood cells maintained at 37°C. Changes in pH (6.800–7.600) were induced by: (1) varying the partial pressure of carbon dioxide by the bubbling of mixtures of humidified carbon dioxide and nitrogen gas through the blood; and (2) adding 1 N HCl/NaOH. Humidified oxygen gas was bubbled through the blood to generate variations in oxygen saturation. After each titration of pH, the spectrum was recorded and blood was sampled for the measurement of: pH, pCO 2, and pO 2 using blood gas analysis; and hemoglobin concentration and oxygen saturation using co-oximetry. Samples from three separate pH titrations were combined (120 total samples) and analyzed using partial least-squares analysis to generate a mathematical model relating spectral changes to pH (calibration set). This model was then used to predict the pH of a set of 36 pH titrations (prediction set). Results. Quantitative and qualitiative analyses of the spectra in the calibration set found that spectral changes in the wavelength range, 650–1050 nm, were directly related to changes in pH. First-derivative-treated spectra from the calibration set, analyzed using partial least-squares analysis, generated a mathematical model with a cross-validated r 2 of 0.939 and a standard error of calibration of 0.046 pH unit. When this model was applied to the prediction set, with an offset correction, the r 2 was 0.936 with a standard error of prediction of 0.050 pH unit. Conclusion. Blood pH can be predicted in vitro with clinical significance using reflectance near-infrared spectroscopy (650–1050 nm) within a standard error of 0.050 pH unit.

Near-Infrared Spectroscopic Measurement of Myoglobin Oxygen Saturation in the Presence of Hemoglobin Using Partial Least-Squares Analysis

Myoglobin is an important intracellular protein found in cardiac and skeletal muscle. It is involved in the intracellular transport of oxygen from the cell membrane to the mitochondria where oxidative phosphorylation takes place. The optical absorbance characteristics of myoglobin are similar to those of hemoglobin in the near-infrared spectral region. Distinguishing spectral information of myoglobin from hemoglobin should allow for determination of intracellular oxygen availability in muscle. Partial least-squares analysis is used in this report to determine the oxygen saturation of myoglobin, in the presence of hemoglobin, in vitro. Studies were performed with the use of both transmission and reflectance spectroscopic techniques. Transmission spectra of myoglobin solutions were determined with varying degrees of oxygen saturation achieved by deoxygenating the solution using E. coli. Calibration spectral data sets were developed with the use of varying concentrations of hemoglobin interference, and with varying degrees of myoglobin oxygen saturation. Reflectance spectra were obtained from myoglobin and hemoglobin solutions containing a scattering agent to mimic muscle tissue conditions. Predicted myoglobin saturation values were within 2% of the known saturation values from the use of this analysis. Partial least-squares analysis allows for accurate prediction of myoglobin oxygen saturation in the presence of hemoglobin from either transmission of reflectance near-infrared spectra.