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Fluorescence spectroscopy is an attractive candidate for analyzing samples of nylon. Impurities within the polymers formed during the synthesis and processing of nylons give rise to the observed fluorescence, allowing for nylons to be analyzed based on their impurities. Nylons from the same source are expected to display similar fluorescence profiles, and nylons with different fluorescence are expected to be from different sources. This paper investigates an important case where different nylons displayed similar fluorescence, preventing easy discrimination. Samples of Nylon 6 and Nylon 6/12 had visually indistinguishable excitation–emission matrices (EEM), excitation spectra, fluorescence spectra, and synchronous fluorescence spectra at larger Δλ. By collecting synchronous fluorescence spectra at smaller Δλ, additional features in the fluorescence profiles were identified that allowed for some discrimination between the two nylons. Combining the EEM and synchronous fluorescence data with chemometric algorithms provided a clearer differentiation between the two nylons. parallel factor analysis, principal component analysis, and common dimension partial least squares (ComDim-PLS) showed two distinct clusters in the data, with ComDim-PLS providing the greatest distinction between the clusters. The loadings revealed the variables of interest to the ComDim-PLS were the 400 nm and 335 nm bands for all synchronous fluorescence spectra, the 460 nm and 310 nm bands for the Δλ = 20 nm and Δλ = 30 nm synchronous fluorescence spectra, and the 440 nm band for the Δλ = 20 nm synchronous fluorescence spectra. The linear discriminant analysis performed with the PLS data yielded a classification accuracy of 95% with the EEM data and 100% with the synchronous fluorescence data, displaying the power of this technique to differentiate two different nylons with visually indistinguishable fluorescence spectra. [ABSTRACT FROM AUTHOR]

Here, Raman spectroscopy is used to develop a univariate partial least squares (PLS) calibration capable of quantifying geochemistry in synthetic and natural silicate glass samples. The calibration yields eight oxide-specific models that allow predictions of silicon dioxide (SiO2), sodium oxide (Na2O), potassium oxide (K2O), calcium oxide (CaO), titanium dioxide (TiO2), aluminum oxide (Al2O3), ferrous oxide (FeOT), and magnesium oxide (MgO) (wt%) in glasses spanning a wide range of compositions, while also providing correlation-coefficient matrices that highlight the importance of specific Raman channels in the regression of a particular oxide. The PLS suite is trained on 48 of the 69 total glasses, and tested against 21 validation samples (i.e., held out of training). Trends in root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP) model accuracy metrics are investigated to uncover the efficacy of utilizing multivariate analysis for such Raman data and are contextualized against recently produced strategies. The technique yields an average root mean of calibration (∼2.4 wt%), cross-validation (∼ 2.9 wt%), prediction (∼ 2.6 wt%), and normalized variance (∼ 28%). Raman band positional shifts are also mapped against underlying chemical variations; with major influences arising primarily as a function of overall oxidation state and silica concentration: via ferric cation (Fe3+)/ferrous cation (Fe2+) ratios and SiO2 (wt%). The algorithm is further validated preliminarily against a separate external set of 11 natural basaltic glasses to unravel the limitations of the synthetic models on natural samples, and to determine the suitability of "universal" Raman-model applications in scenarios where prior chemical contextualization of the target sample is possible. This study represents the first time Raman spectra of amorphous silicates have been paired with PLS, offering a foundation for future improvements utilizing these systems. [ABSTRACT FROM AUTHOR]

In-situ Fourier transform infrared (FT-IR) spectroscopy has been recognized as an important technology for online monitoring of chemical reactions. However, analysis of the real-time IR data for identification and quantification of uncertain reactants or intermediates is often ambiguous and difficult. Here, we propose an analysis algorithm based on reaction kinetic modeling and the chemometric method of partial least squares (PLS) to comprehensively and quantitatively study reaction processes. Concentration profiles and apparent kinetic parameters can be simultaneously calculated from the spectral data, without the demand of complicated analysis on characteristic absorbance peaks or tedious sampling efforts for multivariate modeling. Paal–Knorr reactions and glyoxylic acid synthesis reactions were selected as typical reactions to validate the algorithm. A lack of fit of the Paal–Knorr reaction spectra was less than 2.5% at various conditions, and the absolute errors between the predicted values and HPLC measurement of glyoxylic acid synthesis were less than 6% during the reaction process. Moreover, the reaction kinetic models extracted from FT-IR data were used to simulate reaction processes and optimize the conditions in order to maximize product yields, which proved that this analysis method could be used for process optimization. Graphical Abstract [ABSTRACT FROM AUTHOR]

Temperature measurement of internal components of a jet engine is a crucial control parameter to ensure its component life and efficiency. Particularly for thermal analysis of internal components of jet engines, irreversible thermochromic paints (TPs) have been developed at Rolls-Royce plc to evaluate the surface temperature of engine components where it is otherwise impossible. Thermochromic paints change color with respect to an increased temperature whereby the resulting change in the TP color corresponds to the maximum temperature experienced by the surface of engine components during testing. To improve the reliability and reproducibility of the temperature measurement by TPs, this work explored the potential use of diffuse reflection Fourier transform infrared spectroscopy (DRIFTS) combined with partial least squares regression (PLSR) analysis. The outcome of the prediction of the raw and pre-processed datasets was compared and discussed. The major contributors to the prediction models were the change in the property of the surface M–OH bonds, the structural change of the inorganic pigments and fillers, and their solid-state reaction at a higher temperature. The result showed improved reliability of the prediction model after the combined pre-process treatments with reported RMSEC of 4.5 °C and RMSECV of 13.0 °C using three latent variables. Graphical Abstract [ABSTRACT FROM AUTHOR]

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample. Graphical Abstract [ABSTRACT FROM AUTHOR]

The peroxide value of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine peroxide values using field portable and process instrumentation; those efforts presented "best-case scenarios" with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique or combination thereof is best for predicting peroxide values. Following peroxide value assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils. Graphical Abstract [ABSTRACT FROM AUTHOR]

Heroin is one of the most frequently seized drugs in Southeastern Europe. Due to the position in the Balkan route, the Republic of Serbia keeps important role in suppression of the trafficking of heroin for domestic and foreign illegal market. This research is aimed to provide a good scientific approach in the field of seized heroin analysis. Two different forms of heroin are present in the illegal market, mostly in mixtures with typical "cutting" agents: caffeine, paracetamol, and sugars. It was observed that the quantity of pure heroin in seized samples slightly increases from year to year. The aim of this study was to produce a reliable and fast procedure for classification of illicit heroin samples and determination of the concentration range of heroin in the samples. For that purpose, the attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) technique was used and combined with such chemometric methods as principal component analysis, cluster analysis, and partial least squares. Principal component analysis (PCA) as an unsupervised model was used for exploratory purposes to identify trends, similarities, and differences between samples by reducing the dimensionality of the data. The cluster classification of examined samples turned out to be extremely useful to evaluate the possibilities of the ATR FT-IR technique to classify the samples appropriately into the patterns, the constituted clusters. Additionally, partial least square was the suitable method for the purpose of determination of the heroin hydrochloride concentration range in examined samples. It is proved that the joined application of spectroscopy and chemometrics can be extremely convenient and useful for forensic and drugs control laboratories. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) affects more than 10% of the global population and is associated with significant morbidity and mortality. In most cases, this disease is developed silently, and it can progress to the end-stage renal failure. Therefore, early detection becomes critical for initiating effective interventions. Routine diagnosis of CKD requires both blood test and urinalyses in a clinical laboratory, which are time-consuming and have low sensitivity and specificity. Surface-enhanced Raman scattering (SERS) is an emerging method for rapidly assessing kidney function or injury. This study was designed to compare the differences between the SERS properties of the serum and urine for easy and simple detection of CKD. Enrolled for this study were 126 CKD patients (Stages 2–5) and 97 healthy individuals. SERS spectra of both the serum and urine samples were acquired using a Raman spectrometer (785 nm excitation). The correlation of chemical parameters of kidney function with the spectra was examined using prinicpal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squares (PLS) analysis. Here, we showed that CKD was discriminated from non-CKD controls using PCA–LDA with a sensitivity of 74.6% and a specificity of 93.8% for the serum spectra, and 78.0% and 86.0 % for the urine spectra. The integration area under the receiver operating characteristic curve was 0.937 ± 0.015 (p < 0.0001) for the serum and 0.886 ± 0.025 (p < 0.0001) for the urine. The different stages of CKD were separated with the accuracy of 78.0% and 75.4% by the serum and urine spectra, respectively. PLS prediction (R2) of the serum spectra was 0.8540 for the serum urea (p < 0.001), 0.8536 for the serum creatinine (p < 0.001), 0.7500 for the estimated glomerular filtration rate (eGFR) (p < 0.001), whereas the prediction (R2) of urine spectra was 0.7335 for the urine urea (p < 0.001), 0.7901 for the urine creatinine (p < 0.001), 0.4644 for the eGFR (p < 0.001) and 0.6579 for the urine microalbumin (p < 0.001). In conclusion, the accuracy of associations between SERS findings of the serum and urine samples with clinical conclusions of CKD diagnosis in this limited number of patients is similar, suggesting that SERS may be used as a rapid and easy-to-use method for early screening of CKD, which however needs further evaluation in a large cohort study. [ABSTRACT FROM AUTHOR]

The present study has investigated the transformation of sesame oil kept at low temperature during a definite period of time for refinement (called winterization) as an inactive drug ingredient by using two-dimensional difference spectra (2D-DS) analysis of spectra collected using a near-infrared (NIR) and mid-infrared (MIR) dual-wavelength spectrometer (NIR–MIR-DWS). The NIR and MIR spectra were measured nearly simultaneously from samples of sesame oil before and after winterization. The difference spectrum analysis of the obtained NIR–MIR data elucidated that, after the winterization process, the absorbances at peaks attributed to C=O, C=C, and OH groups decrease while the absorbances arising from the main chain (CH2) increase. The result indicated the removal of lignan and the fatty acids with relatively short main chains. Moreover, sesame oil unwinterized was cooled from room temperature to near 1 ℃ and subsequently warmed to room temperature. And the cycle was repeated two times. Real-time monitoring during the cooling and warming processes were carried out using the NIR-MIR-DWS. The prediction results obtained from partial least square calibration model for the temperature suggests that there are subtle differences in the oil composition between the first cooling process and after the warming and cooling cycle. For the more detailed analysis, the 2D-DS method is proposed. The results of the analyses using 2D-DS revealed that the starting point of the transformation is around 15 ℃. It can be estimated that sesame oil is mainly transformed by the first cooling down. Moreover, it was implied that the structure of methylene (CH2) was significantly related to the modifications in sesame oil with temperature change. A series of experimental results elucidated that the winterization of sesame oil removed its impurities and stabilized its conditions. These results are probably the first report on the effect of the winterization process on sesame oil. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is a technique capable of portable, quantitative elemental analysis; however, quantitative isotopic determination of samples in situ has not yet been demonstrated. This research demonstrates the ability of LIBS to quantitatively determine concentrations of 6Li in solid samples of lithium hydroxide monohydrate in a nominally 40 mTorr argon environment using chemometrics. Three chemometric analysis techniques (principal component regression, partial least squares regression, and neural networks analysis) are applied to spectra collected using a spectrometer with modest resolving power (λ/Δλ ≈ 27 000). This analysis suggests that bulk lithium isotopic assay can be determined using LIBS to within a 95% confidence interval in minutes to an hour for enrichment levels ranging from 3% to 85%. This has direct applications for the nuclear safeguards and geological exploration communities and others that desire a portable, stable isotope analytical technique. Additionally, isotope-specific self-absorption of atomic emission in a laser-produced plasma is observed for the first time. [ABSTRACT FROM AUTHOR]

In many industries, viscosity is an important quality parameter which significantly affects consumer satisfaction and process efficiency. In the personal care industry, this applies to products such as shampoo and shower gels whose complex structures are built up of micellar liquids. Measuring viscosity offline is well established using benchtop rheometers and viscometers. The difficulty lies in measuring this property directly in the process via on or inline technologies. Therefore, the aim of this work is to investigate whether proxy measurements using inline vibrational spectroscopy, e.g., near-infrared (NIR), mid-infrared (MIR), and Raman, can be used to predict the viscosity of micellar liquids. As optical techniques, they are nondestructive and easily implementable process analytical tools where each type of spectroscopy detects different molecular functionalities. Inline fiber optic coupled probes were employed; a transmission probe for NIR measurements, an attenuated total reflectance probe for MIR and a backscattering probe for Raman. Models were developed using forward interval partial least squares variable selection and log viscosity was used. For each technique, combinations of pre-processing techniques were trialed including detrending, Whittaker filters, standard normal variate, and multiple scatter correction. The results indicate that all three techniques could be applied individually to predict the viscosity of micellar liquids all showing comparable errors of prediction: NIR: 1.75 Pa s; MIR: 1.73 Pa s; and Raman: 1.57 Pa s. The Raman model showed the highest relative prediction deviation (RPD) value of 5.07, with the NIR and MIR models showing slightly lower values of 4.57 and 4.61, respectively. Data fusion was also explored to determine whether employing information from more than one data set improved the model quality. Trials involved weighting data sets based on their signal-to-noise ratio and weighting based on transmission curves (infrared data sets only). The signal-to-noise weighted NIR–MIR–Raman model showed the best performance compared with both combined and individual models with a root mean square error of cross-validation of 0.75 Pa s and an RPD of 10.62. This comparative study provides a good initial assessment of the three prospective process analytical technologies for the measurement of micellar liquid viscosity but also provides a good basis for general measurements of inline viscosity using commercially available process analytical technology. With these techniques typically being employed for compositional analysis, this work presents their capability in the measurement of viscosity—an important physical parameter, extending the applicability of these spectroscopic techniques. [ABSTRACT FROM AUTHOR]

Content uniformity is a critical attribute for potent and low-dosage formulations of active pharmaceutical ingredient (API) that, in addition to the formulation parameters, plays pivotal role during pharmaceutical development and production. However, when API content is low, implementing a vibrational spectroscopic analytical tool to monitor the content and blend uniformity remains a challenging task. The aim of this study was to showcase the potentials of mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy for quantitative analysis of alprazolam (ALZ) in a low-content powder blends with lactose, which is used as a common diluent for tablets produced by direct compression. The offered approach might be further scaled up and exploited for potential application in the process analytical technology (PAT). Partial least square and orthogonal PLS (OPLS) methodologies were employed to build the calibration models from raw and processed spectral data (standard normal variate, first and second derivatives). The models were further compared regarding their main statistical indicators: correlation coefficients, predictivity, root mean square error of estimation (RMSEE), and root mean square error of cross-validation (RMSEEcv). All statistical models presented high regression and predictivity coefficients. The RMSEEcv for the optimal models was 1.118, 0.08, and 0.059% for MIR, NIR, and Raman spectroscopy, respectively. The scarce information content extracted from the ALZ NIR spectra and the major band overlapping with those from lactose monohydrate was the main culprit of poor accuracy in the NIR model, whereas the subsampling instrumental setup (resulting in a non-representative spectral acquisition of the sample) was regarded as a main limitation for the MIR-based calibration model. The OPLS models of the Raman spectra of the powder blends manifested favorable statistical indicators for the accuracy of the calibration model, probably due to the distinctive ALZ Raman pattern resulting in the largest number of predictive spectral points that were used for the mathematical modeling. Furthermore, the Raman scattering calibration model was optimized in narrower scanning range (1700–700 cm−1) and its prediction power was evaluated (root mean square error of prediction, RMSEP = 0.03%). Thus, the Raman spectroscopy presented the most favorable statistical indicators in this comparative study and therefore should be further considered as a PAT for the quantitative determination of ALZ in low-content powder blends. [ABSTRACT FROM AUTHOR]

Developing a rapid and stable method for analyzing the quality parameters of rice is important. Near-infrared (NIR) spectroscopy combined with chemometric techniques have been used to predict the critical contents of rice and shown its accuracy and stability. To further improve the predictive ability, we combine the derivative method of fractional order Savitzky–Golay derivation (FOSGD) with the wavelength selection method of competitive adaptive reweighted sampling (CARS). Compared with the traditional integer order Savitzky–Golay derivation (IOSGD), the FOSGD could improve the resolution ratio of the raw spectra more effectively. The wavelength selection method, CARS, could further extract the informative variables from the processed spectra. Four key contents of rice samples, including moisture, amylose, chalkiness degree, and gel consistency, were utilized to validate this method. The prediction results indicated that partial least squares (PLS) models optimized with FOSGD-CARS own higher accuracy and stability with smaller the root mean squared error of cross validations (RMSECVs) and root mean squared error of predictions (RMSEPs). The proposed method is convenient and provides a practical alternative for rice analysis. [ABSTRACT FROM AUTHOR]

A non-contact Fourier transform interferometric imaging system was used to collect hyperspectral images of the steady-state diffuse reflectance from a point source in turbid media for the spectral range of 550-850 nm. Steady-state diffuse reflectance profiles were generated from the hyperspectral images, and partial least-squares (PLS) regression was performed on the diffuse reflectance profiles to quantify absorption (μa) and reduced scattering (μ′s) properties of turbid media. The feasibility of using PLS regression to predict optical properties was examined for two different sets of spatially-resolved diffuse reflectance data. One set of data was collected from 40 turbid phantoms, while the second set was generated by convolving Monte Carlo simulations with the instrument response of the imaging system. Study results show that PLS prediction of μa and μ′s was accurate to within ±8% and ±5%, respectively, when the model was trained on turbid phantom data. Moreover, PLS prediction of optical properties was considerably faster and more efficient than direct least-squares fitting of spatially-resolved profiles. When the PLS model was trained on Monte Carlo simulated data and subsequently used to predict μa and μ′s from the diffuse reflectance of turbid phantom, the percent accuracies degraded to ±12% and ±5%, respectively. These accuracy values are applicable to homogenous, semi-infinite turbid phantoms with optical property ranges comparable to tissues.

Matching the spectral response between multiple spectrometers is a mandatory procedure when developing robust calibrations whose prediction is independent of instrument-related signal variations. A viable alternative to complex calibration transfer methods consists of matching the instrument spectral response by controlling a set of key instrumental and environmental parameters. This paper discusses the applicability of such an approach to three Fourier transform infrared (FT-IR) spectrometers used for the routine assessment of carbonaceous particulate matter concentrations in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) speciation network. The effectiveness of the proposed matching procedure is evaluated by comparing the spectral response for each individual instrument in order to characterize the extent, and nature, of the remaining inter-instrument spectral dissimilarities. Instrument-related contributions to the signal were determined to be small compared with the spectral variability induced by the filter type used for sample collection. The impact of spectral differences on prediction was addressed through the comparison of model performance derived from multiple calibration scenarios. A hybrid model yielding accurate and homogeneous prediction regardless of the instrument was proposed for organic carbon (OC) and elemental carbon (EC), two major constituents of atmospheric particulate matter. Coefficients of determination of 0.98 (OC) and 0.90 (EC) with median biases not exceeding 0.20 µg (OC) and 0.07 µg (EC) are reported. The long-term stability, assessed from weekly measurements of reference samples, shows a deviation in predicted concentrations of less than ±5% over a 2.5-year period for most of the data collected. Extending OC and EC hybrid models to the prediction of ambient samples collected during the two subsequent years provides satisfactory performance. The proposed instrument matching procedure coupled with the relative simplicity of the hybrid model is an alternative to computationally advanced calibration transfer methodologies for the characterization of carbonaceous particulate matter using multiple FT-IR instruments. [ABSTRACT FROM AUTHOR]

We have developed a piecewise local (PL) partial least squares (PLS) analysis method for total plutonium measurements by absorption spectroscopy in nitric acid-based nuclear material processing streams. Instead of using a single PLS model that covers all expected solution conditions, the method selects one of several local models based on an assessment of solution absorbance, acidity, and Pu oxidation state distribution. The local models match the global model for accuracy against the calibration set, but were observed in several instances to be more robust to variations associated with measurements in the process. The improvements are attributed to the relative parsimony of the local models. Not all of the sources of spectral variation are uniformly present at each part of the calibration range. Thus, the global model is locally overfitting and susceptible to increased variance when presented with new samples. A second set of models quantifies the relative concentrations of Pu(III), (IV), and (VI). Standards containing a mixture of these species were not at equilibrium due to a disproportionation reaction. Therefore, a separate principal component analysis is used to estimate of the concentrations of the individual oxidation states in these standards in the absence of independent confirmatory analysis. The PL analysis approach is generalizable to other systems where the analysis of chemically complicated systems can be aided by rational division of the overall range of solution conditions into simpler sub-regions. [ABSTRACT FROM AUTHOR]

One of the main challenges in the noninvasive sensing of blood glucose by near-infrared (NIR) spectroscopy is the background variations from light source drift, sweating, and temperature change at the human-machine interface. In this paper, a differential correction method based on the spectra from the floating-reference position and measuring position is proposed to eliminate these spectral variations from background interferences. Its effectiveness was validated by in vitro and in vivo experiments in which the diffuse reflectance of intralipid solutions and human skin was collected at the source distances of 0.6mm and 2mm by the custom-built system with six super-luminescent emitting diodes (SLEDs) light source. The results showed that, for the in vitro experiments of intralipid solutions, the coefficients of variations of diffuse reflectance decreased by 20.5% under all the six wavelengths after differential correction. For the in vivo experiments of oral glucose tolerance tests (OGTTs), partial least squares (PLS) regression models between glucose concentrations and the diffuse reflectance from palm skin were built, and the root mean square error of cross validation (RMSECV) decreased by 38.0% on average after the differential correction. Further, the spectra of the oral water tolerance tests (OWTTs) were collected for correlation with glucose concentration in OGTTs, and their correlation coefficients (R) decreased by 35.0% on average after the differential correction. Therefore, this differential correction method based on the spectra from the floating-reference position and measuring position can weaken the influence of background variations on the NIR spectroscopy and has promising potential in in vivo detection, especially for noninvasive blood glucose measurement. [ABSTRACT FROM AUTHOR]

Due to the complex nature of near-infrared (NIR) spectra, it is usually very difficult to provide quantitative interpretations of spectral data. As a consequence, careful building and validation of calibration models are of fundamental importance prior to development of useful applications of NIR technologies. For this reason, this work presents a statistical study about the NIR spectroscopy, analyzing the NIR behavior when the experimental conditions are changed. Near-infrared spectra were measured at different temperatures and stirring velocities for systems containing a pure solvent and a suspension of polymer powder in order to perform the error analysis. Then, mixtures of xylene and toluene were analyzed through NIR at different temperatures and stirring velocities and the obtained data were used to build calibration models with multivariate techniques. The results showed that the precision of the NIR measurements depends on the analytical conditions and that unavoidable fluctuations of spectral data (or spectral data variability) are strongly correlated, leading to full covariance matrices of spectral fluctuations, which has been surprisingly neglected during quantitative analyses. In particular, modeling of the xylene/toluene NIR data performed with different multivariate techniques revealed that the principal directions are not preserved when the real covariance matrix of measurement errors is taken into account. [ABSTRACT FROM AUTHOR]

An ultraviolet visible (UV-Vis) spectrophotometric and partial least squares (PLS) chemometric method was developed for the simultaneous determination of erythrosine B (red), Brilliant Blue, and tartrazine (yellow) dyes. A training set (n=64) was generated using a full factorial design and its accuracy was tested in a test set (n=13) using a Box-Behnken design. The test set garnered a root mean square error (RMSE) of 1.79×10-7 for blue, 4.59×10-7 for red, and 1.13×10-6 for yellow dyes. The relatively small RMSE suggests only a small difference between predicted versus measured concentrations, demonstrating the accuracy of our model. The relative error of prediction (REP) for the test set were 11.73%, 19.52%, 19.38%, for blue, red, and yellow dyes, respectively. A comparable overlay between the actual candy samples and their replicated synthetic spectra were also obtained indicating the model as a potentially accurate method for determining concentrations of dyes in food samples. [ABSTRACT FROM AUTHOR]

The study is aimed at developing a near-infrared (NIR) method for predicting solid fraction (SF) of dry granulated ribbons manufactured with formulation variability. The study investigated the impact of unmodeled chemical variability and regression approaches on method performance. The study utilized an excipient-only formulation system. Calibration compacts were created with chemical and processing variability; followed by collection of NIR spectra. Partial least squares (PLS) and spectral slope algorithms were utilized to model compact SF. Later, the models were deployed to predict SF of test ribbons and compacts containing an API at various concentrations. The risk associated with unmodeled chemical variation manifested itself through generation of new peaks and decreased baseline absorbance in the NIR spectra. The spectral slope was able to better manage this risk, as demonstrated by relatively higher robustness to the increasing load of the active pharmaceutical ingredient (API). The reduced robustness of the PLS approach was attributed to the impact of chemical variability on both spectral baseline and peak absorbance. A prediction error of approximately 5% was observed at 10% drug load using the spectral slope approach. An understanding of the risk associated with unmodeled variability will enable NIR method development as an API sparing technique for low-dose product development. [ABSTRACT FROM AUTHOR]

In order to help reduce silicosis in miners, the National Institute for Occupational Health and Safety (NIOSH) is developing field-portable methods for measuring airborne respirable crystalline silica (RCS), specifically the polymorph α-quartz, in mine dusts. In this study we demonstrate the feasibility of end-of-shift measurement of α-quartz using a direct-on-filter (DoF) method to analyze coal mine dust samples deposited onto polyvinyl chloride filters. The DoF method is potentially amenable for on-site analyses, but deviates from the current regulatory determination of RCS for coal mines by eliminating two sample preparation steps: ashing the sampling filter and redepositing the ash prior to quantification by Fourier transform infrared (FT-IR) spectrometry. In this study, the FT-IR spectra of 66 coal dust samples from active mines were used, and the RCS was quantified by using: (1) an ordinary least squares (OLS) calibration approach that utilizes standard silica material as done in the Mine Safety and Health Administration's P7 method; and (2) a partial least squares (PLS) regression approach. Both were capable of accounting for kaolinite, which can confound the IR analysis of silica. The OLS method utilized analytical standards for silica calibration and kaolin correction, resulting in a good linear correlation with P7 results and minimal bias but with the accuracy limited by the presence of kaolinite. The PLS approach also produced predictions well-correlated to the P7 method, as well as better accuracy in RCS prediction, and no bias due to variable kaolinite mass. Besides decreased sensitivity to mineral or substrate confounders, PLS has the advantage that the analyst is not required to correct for the presence of kaolinite or background interferences related to the substrate, making the method potentially viable for automated RCS prediction in the field. This study demonstrated the efficacy of FT-IR transmission spectrometry for silica determination in coal mine dusts, using both OLS and PLS analyses, when kaolinite was present. [ABSTRACT FROM AUTHOR]

In this work an attenuated total reflection Fourier transform infrared (FT-IR) absorption based method is used to measure the solubility of two matrix-assisted laser desorption ionization (MALDI) matrices in a few pure solvents and mixtures of acetonitrile and water using low microliter amounts of solution. Results from a method that averages the values obtained from multiple calibration curves created by manual peak picking are compared to those predicted using a partial least squares (PLS) chemometrics approach. The PLS method provided solubility values that were in good agreement with the manual method with significantly greater ease of analysis. As a test, the solubility of adipic acid in acetone was measured using the two methods of analysis, and the values are in good agreement with solubility values reported in literature. The solubilities of the MALDI matrices a cyano 4 hydroxy cinnamic acid (CHCA) and sinapinic acid (SA) were measured in a series of mixtures made from acetonitrile (ACN) and water; surprisingly, the results show a highly nonlinear trend. While both CHCA and SA show solubility values of less than 10 mg/mL in the pure solvents, the solubility value for SA increases to 56.3 mg/mL in a 75:25 v/v ACN:water mixture. This can have a significant effect on the matrix to analyte ratios in the MALDI experiment when sample protocols call for preparation of a saturated solution of the matrix in the chosen solvent system. [ABSTRACT FROM AUTHOR]

A nondestructive and faster methodology to quantify mechanical properties of polypropylene (PP) pellets, obtained from an industrial plant, was developed with Raman spectroscopy. Raman spectra data were obtained from several types of samples such as homopolymer PP, random ethylene-propylene copolymer, and impact ethylene-propylene copolymer. Multivariate calibration models were developed by relating the changes in the Raman spectra to mechanical properties determined by ASTM tests (Young's traction modulus, tensile strength at yield, elongation at yield on traction, and flexural modulus at 1% secant). Several strategies were evaluated to build robust models including the use of preprocessing methods (baseline correction, vector normalization, de-trending, and standard normal variate), selecting the best subset of wavelengths to model property response and discarding irrelevant variables by applying genetic algorithm (GA). Linear multivariable models were investigated such as partial least square regression (PLS) and PLS with genetic algorithm (GA-PLS) while nonlinear models were implemented with artificial neural network (ANN) preceded by GA (GA-ANN). The best multivariate calibration models were obtained when a combination of genetic algorithms and artificial neural network were used on Raman spectral data with relative standard errors (%RSE) from 0.17 to 0.41 for training and 0.42 to 0.88% validation data sets. [ABSTRACT FROM AUTHOR]

Higher-order gap derivatives are sometimes avoided as a preprocessing method for multivariate calibration despite their numerous advantages. One reason that they are avoided is the difficulty in interpreting the complex processed spectra and the regression vectors that arise from common calibration procedures like principal components regression or partial least squares regression. In this report we offer a method of calculating gap derivatives of any order with the aim of retrieving zero-order spectral information via numerical integration. This method is also extended to the integration of the accompanying regression vectors to aid in the interpretation of multivariate calibration models. [ABSTRACT FROM AUTHOR]

Visible and near-infrared (Vis-NIR, 350–2500 nm) diffuse reflection spectroscopy (DRS) models built from "as-collected" samples of solid cattle manure accurately predict concentrations of moisture and crude ash. Because different organic molecules emit different spectral signatures, variations in livestock diet composition may affect the predictive accuracy of these models. This study investigates how differences in livestock diet composition affect Vis-NIR DRS prediction of moisture and crude ash. Spectral signatures of solid manure samples (n = 216) from eighteen groups of cattle on six different diets were used to calibrate and validate partial least squares (PLS) regression models. Seven groups of PLS models were created and validated. In the first group, two-thirds of all samples were randomly selected as the calibration set and the remaining one-third were used for the validation set. In the remaining six groups, samples were grouped by livestock diet (ration). Each ration in turn was held out of calibrations and then used as a validation set. When predicting crude ash, the fully random calibration model produced a root mean square deviation (RMSD) of 2.5% on a dry basis (db), ratio of standard error of prediction to the root mean squared deviation (RPD) of 3.1, bias of 0.14% (db), and correlation coefficient r 2 of 0.90., When predicting moisture, an RMSD of 1.5% on a wet basis (wb), RPD of 4.3, bias of −0.09% (wb), and r 2 of 0.95 was achieved. Model accuracy and precision were not impaired by exclusion of any single ration from model calibration. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy has been used for noninvasive measurements of solid and liquid samples, through highly scattering media such as colloids, food, and tissue. It has seen many applications in agriculture, medicine, and petroleum industries, mainly due to the minimal sample preparation that is required. This minimal sample preparation does come at a cost to the analyst, since the high signal-to-noise ratio of a typical NIR instrument can be riddled with effects stemming from heterogeneity and the scattering of light. This work proposes a novel preprocessing method, the path length distribution correction (PDC) method, to correct spectral nonlinearities in samples of highly scattering media. These nonlinearities stem from the distribution of path lengths of the incident light, which are a result of the scattering of light in the sample. Recent developments in time-of-flight (TOF) spectroscopy have allowed for the acquisition of the distribution of times that photons travel within a sample simultaneous with the collection of the NIR spectrum. The TOF distribution is used to estimate a path length distribution within a sample, which is then used to fix the measurement spectra, giving each spectrum an apparent path length of unity. The PDC-corrected spectra can then be used with traditional multivariate calibration methods such as principal component regression (PCR) and partial least squares (PLS). Another discussion looks at the viability of using a lognormal distribution as a simple approximation of the TOF distribution. This would be very useful in circumstances in which experimental TOF distributions are not collected. PDC is shown to significantly improve prediction errors in experimental data sets, while diagnostic plots indicate that the corrected spectra do appear to have a path length of unity, thus alleviating effects of the distribution of path lengths. [ABSTRACT FROM AUTHOR]

The peroxide value (PV) of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine PVs using field portable and process instrumentation; those efforts presented âbest-caseâ scenarios with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique, or combination thereof, is best for predicting PVs. Following PV assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression (PLSR) calibration models to predict the PV of unknown oil samples. A global PV model based on near-infrared (8 mm optical path length â OPL) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm OPL near infrared (5.1), Raman (6.9) and 50 μm OPL mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global PV models based on low-level fusion of the NIR (8 and 24 mm OPL) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes.

One of the main challenges in the noninvasive sensing of blood glucose by near-infrared (NIR) spectroscopy is the background variations from light source drift, sweating, and temperature change at the human-machine interface. In this paper, a differential correction method based on the spectra from the floating-reference position and measuring position is proposed to eliminate these spectral variations from background interferences. Its effectiveness was validated by in vitro and in vivo experiments in which the diffuse reflectance of intralipid solutions and human skin was collected at the source distances of 0.6 mm and 2 mm by the custom-built system with six super-luminescent emitting diodes (SLEDs) light source. The results showed that, for the in vitro experiments of intralipid solutions, the coefficients of variations of diffuse reflectance decreased by 20.5% under all the six wavelengths after differential correction. For the in vivo experiments of oral glucose tolerance tests (OGTTs), partial least squares (PLS) regression models between glucose concentrations and the diffuse reflectance from palm skin were built, and the root mean square error of cross validation (RMSECV) decreased by 38.0% on average after the differential correction. Further, the spectra of the oral water tolerance tests (OWTTs) were collected for correlation with glucose concentration in OGTTs, and their correlation coefficients (R) decreased by 35.0% on average after the differential correction. Therefore, this differential correction method based on the spectra from the floating-reference position and measuring position can weaken the influence of background variations on the NIR spectroscopy and has promising potential in in vivo detection, especially for noninvasive blood glucose measurement.

Due to the complex nature of near-infrared (NIR) spectra, it is usually very difficult to provide quantitative interpretations of spectral data. As a consequence, careful building and validation of calibration models are of fundamental importance prior to development of useful applications of NIR technologies. For this reason, this work presents a statistical study about the NIR spectroscopy, analyzing the NIR behavior when the experimental conditions are changed. Near-infrared spectra were measured at different temperatures and stirring velocities for systems containing a pure solvent and a suspension of polymer powder in order to perform the error analysis. Then, mixtures of xylene and toluene were analyzed through NIR at different temperatures and stirring velocities and the obtained data were used to build calibration models with multivariate techniques. The results showed that the precision of the NIR measurements depends on the analytical conditions and that unavoidable fluctuations of spectral data (or spectral data variability) are strongly correlated, leading to full covariance matrices of spectral fluctuations, which has been surprisingly neglected during quantitative analyses. In particular, modeling of the xylene/toluene NIR data performed with different multivariate techniques revealed that the principal directions are not preserved when the real covariance matrix of measurement errors is taken into account.