Temperature-independent near-infrared analysis of lysozyme aqueous solutions

Digital Fourier filtering is used to produce a temperature-insensitive univariate calibration model for measuring lysozyme in aqueous solutions. Absorbance spectra over the 5000-4000 cm-1 spectral range are collected for lysozyme standards maintained at 14 degrees C. These spectra are used to compute the calibration model while a set of spectra collected at temperatures ranging from 4 to 24 degrees C are used to validate the accuracy of this model. The root-mean-square error of prediction (RMSEP) is 0.279 mg/mL over a tested lysozyme concentration range of 0.036-51.6 mg/mL. The detection limit is 0.68 mg/mL. In addition, multivariate calibration models based on partial least-squares regression (PLS) are evaluated and compared to the results from the univariate model. PLS outperforms the univariate model by providing a RMSEP of 0.090 mg/mL. Analysis of variance showed that both calibration methods effectively eliminate the adverse affects created by variations in solution temperature.     

Multivariate calibration for assays in clinical chemistry using attenuated total reflection infrared spectra of human blood plasma

A spectral analysis of human blood plasma was undertaken by use of a Fourier-transform infrared spectrometer with a circular attenuated total reflection cell. The concentrations of total protein, glucose, triglycerides, total cholesterol, urea, and uric acid were measured by chemical or enzymatic methods. For these constituents the partial least-squares (PLS) algorithm was used for a multivariate calibration including the infrared fingerprint region of the plasma spectra. Best results were achieved for total protein with an average prediction error (PRESS based on cross validation) of 2.1 g/L; other PRESS results were for glucose 22 mg/dL, triglycerides 33 mg/dL, cholesterol 31 mg/dL, urea 4.4 mg/dL, and uric acid 1.6 mg/dL.     

Multivariate determination of glucose in whole blood by attenuated total reflection infrared spectroscopy

A spectral analysis of whole EDTA blood was undertaken by using attenuated total reflection and Fourier-transform infrared spectroscopy. The concentration of blood glucose was measured by an enzymatic method using glucose dehydrogenase and ranged between 40 and 290 mg/dL with an average concentration of 90.4 mg/dL. Multivariate calibration with the partial least-squares (PLS) algorithm was performed on spectral data between 1500 and 750 cm-1 showing a varying background from different unidentified interfering compounds. Cross validation was carried out for optimizing the PLS model. PRESS was 19.8 mg/dL, which was calculated on the basis of 127 standards, whereas the estimated standard deviation for the calibration fit was computed to be 11.9 mg/dL. Infrared spectroscopy can be used for monitoring glucose levels within the normal physiological range in a complex matrix like whole blood as an alternative to electrochemical sensors.     

Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment

The present study has aimed at providing new insight into short-wave near-infrared (NIR) spectroscopy of biological fluids. To do that, we analyzed NIR spectra in the 800-1,100-nm region of 100 raw milk samples. The contents of fat, proteins, and lactose were predicted by partial least-squares (PLS) regression and band assignment in that region was investigated based upon PLS loading plots and regression coefficients. For the fat prediction, the whole set of samples was divided into two groups and the fat concentration was predicted for the samples that were not included in the calibration procedures. The correlation coefficient and root-mean-square error of prediction (RM-SEP) in the better prediction run were found to be 0.996 and 0.087 wt %, respectively. Assignment of the bands due to fat was proposed based upon the regression coefficients and PLS loading weights, and the importance of a pretreatment in the prediction was discussed. Milk proteins also yielded sufficient correlation coefficients and RMSEP although the contributions of protein bands to the milk spectra were much smaller than those of the fat bands. The sizes of the calibration models for protein prediction were considered. This is the first time that good correlation coefficients and RMSEP of proteins have ever been obtained for the short-wave NIR spectra of milk. For lactose, noisy regression coefficients with limited prediction ability were obtained. Band assignment was investigated also for bands due to proteins and lactose. We propose the detailed band assignment for the short-wave NIR region useful for various biological fluids. The results presented here demonstrate that the short-wave NIR region is promising for the fast and reliable determination of major components in biological and biomedical fluids.     

Real-time glucose sensing by surface-enhanced Raman spectroscopy in bovine plasma facilitated by a mixed decanethiol/mercaptohexanol partition layer

A new, mixed decanethiol (DT)/mercaptohexanol (MH) partition layer with dramatically improved properties has been developed for glucose sensing by surface-enhanced Raman spectroscopy. This work represents significant progress toward our long-term goal of a minimally invasive, continuous, reusable glucose sensor. The DT/MH-functionalized surface has greater temporal stability, demonstrates rapid, reversible partitioning and departitioning, and is simpler to control compared to the tri(ethylene glycol) monolayer used previously. The data herein show that this DT/MH-functionalized surface is stable for at least 10 days in bovine plasma. Reversibility is demonstrated by exposing the sensor alternately to 0 and 100 mM aqueous glucose solutions (pH approximately 7). The difference spectra show that complete partitioning and departitioning occur. Furthermore, physiological levels of glucose in two complex media were quantified using multivariate analysis. In the first system, the sensor is exposed to a solution consisting of water with 1 mM lactate and 2.5 mM urea. The root-mean-squared error of prediction (RMSEP) is 92.17 mg/dL (5.12 mM) with 87% of the validation points falling within the A and B range of the Clarke error grid. In the second, more complex system, glucose is measured in the presence of bovine plasma. The RMSEP is 83.16 mg/dL (4.62 mM) with 85% of the validation points falling within the A and B range of the Clarke error grid. Finally, to evaluate the real-time response of the sensor, the 1/e time constant for glucose partitioning and departitioning in the bovine plasma environment was calculated. The time constant is 28 s for partitioning and 25 s for departitioning, indicating the rapid interaction between the SAM and glucose that is essential for continuous sensing.     

In vivo, transcutaneous glucose sensing using surface-enhanced spatially offset Raman spectroscopy: multiple rats, improved hypoglycemic accuracy, low incident power, and continuous monitoring for greater than 17 days

This paper presents the latest progress on quantitative, in vivo, transcutaneous glucose sensing using surface enhanced spatially offset Raman spectroscopy (SESORS). Silver film over nanosphere (AgFON) surfaces were functionalized with a mixed self-assembled monolayer (SAM) and implanted subcutaneously in Sprague-Dawley rats. The glucose concentration was monitored in the interstitial fluid of six separate rats. The results demonstrated excellent accuracy and consistency. Remarkably, the root-mean-square error of calibration (RMSEC) (3.6 mg/dL) and the root-mean-square error of prediction (RMSEP) (13.7 mg/dL) for low glucose concentration (<80 mg/dL) is lower than the current International Organization Standard (ISO/DIS 15197) requirements. Additionally, our sensor demonstrated functionality up 17 days after implantation, including 12 days under the laser safety level for human skin exposure with only one time calibration. Therefore, our SERS based sensor shows promise for the challenge of reliable continuous glucose sensing systems for optimal glycemic control.     

Blank augmentation protocol for improving the robustness of multivariate calibrations

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.     

Determination of glucose concentration from NIR spectra using independent component regression

In this paper, a new model based on independent component regression (ICR) is proposed to predict the glucose concentration from near infrared (NIR) spectra. The efficiency of the proposed model is validated using mixtures composed of glucose, urea and triacetin. The whole experiments were carried out in a non-controlled environment or sample conditions to show that the proposed model can suppress effectively most of the experimental variations. The proposed model decreases the standard error of prediction (SEP) from 35.59 mg/dL for Partial Least Square regression (PLS) and from 29.1 mg/dL for ICR to only 24.1 mg/dL.     

Rapid nondestructive on-site screening of methylamphetamine seizures by attenuated total reflection fourier transform infrared spectroscopy

The identification and quantification of illicit substances in the field is often desirable. Fourier transform infrared spectroscopy (FT-IR) has both qualitative and quantitative capabilities and field portable instruments are commercially available. Transmission infrared spectra of mixtures containing ephedrine hydrochloride, glucose, and caffeine and attenuated total reflection (ATR) infrared spectra of mixtures composed of methylamphetamine hydrochloride, glucose, and caffeine were used to develop principal component regression (PCR) calibration models. The root mean sum of errors of predictions (RMSEP) of all individual components in a mixture from a single measurement was <6% w/w, which reduced to approximately 3% w/w when triplicates were averaged. Sample mixing and grinding are essential to minimize the effect of heterogeneity, as deviations of up to 20% w/w were observed for single measurements of unground samples. Poor predictions of the components in a mixture occurred when samples were "contaminated" with substances not present in the calibration set, as would be expected. When only a single analyte (drug) was targeted, using a calibration set that contained both contaminated and uncontaminated samples, an RMSEP of approximately 4% w/w was achieved. The results demonstrate that ATR-FT-IR has the potential to quantify methylamphetamine samples, and possibly other licit or illicit substances, in at-seizure and on-site scenarios.     

Spectral fluorescence signatures and partial least squares regression: model to predict dissolved organic carbon in water

Spectro-fluorescence signature (SFS) of water samples contains information that may be used to quantify dissolved organic carbon (DOC) if combined with multivariate analyses. A model was built through SFS and partial least squared (PLS) regression. The SFSs of 219 samples of natural water along the Raritan River and Millstone River watersheds located in central New Jersey, and their corresponding DOC concentrations were used to build the model. Calibration, full cross-validation, and prediction performances of various models were statistically compared before optimal model selection. The final selected model, tested on the Passaic River watershed in northern New Jersey, provided a bias of 0.028 mg/l and a root mean squared error of prediction (RMSEP) of 0.35 mg/l. Linked to PLS, SFS can be a quality and cost effective method to perform on-line rapid DOC measurements.     

Glucose quantification in dried-down nanoliter samples using mid-infrared attenuated total reflection spectroscopy

The aim of this study was to determine the feasibility of minimally invasive glucose concentration measurement of a body fluid within the physiologically important range below 100 nL with a number of samples such as interstitial fluid, plasma, or whole blood using mid-infrared spectroscopy, but starting with preliminary measurements on samples of simple aqueous glucose solutions. The Fourier transform infrared spectrometer was equipped with a Golden Gate single reflection diamond attenuated total reflection (ATR) accessory and a room-temperature pyroelectric detector. As the necessary detection limits can be achieved only for dried samples within the spectrometric conditions realized by a commercial instrument, the work focused on the optimization of such ATR measurements. We achieved quantification of samples with volumes as low as 7 nL between 10 and 600 mg/dL. The standard error of prediction (SEP) for the concentration range 10-100 mg/dL is 3.2 mg/dL with full interval data between 1180 and 940 cm(-1). The performance of the prediction is given by a coefficient of variation of prediction (CV(pred) ) of 6.2%. When all samples within the whole concentration range are included, the SEP increases to 20.2 mg/dL, and hence the CV(pred) to 10.6% due to a nonlinear signal dependence on glucose concentration. A detection limit for glucose of 0.7 ng with a signal-to-noise ratio of 10 was obtained.     

Evaluation of infrared-reflection absorption spectroscopy measurement and locally weighted partial least-squares for rapid analysis of residual drug substances in cleaning processes

The usefulness of infrared-reflection absorption spectroscopy (IR-RAS) for the rapid measurement of residual drug substances without sampling was evaluated. In order to realize the highly accurate rapid measurement, locally weighted partial least-squares (LW-PLS) with a new weighting technique was developed. LW-PLS is an adaptive method that builds a calibration model on demand by using a database whenever prediction is required. By adding more weight to samples closer to a query, LW-PLS can achieve higher prediction accuracy than PLS. In this study, a new weighting technique is proposed to further improve the prediction accuracy of LW-PLS. The root-mean-square error of prediction (RMSEP) of the IR-RAS spectra analyzed by LW-PLS with the new weighting technique was compared with that analyzed by PLS and locally weighted regression (LWR). The RMSEP of LW-PLS with the proposed weighting technique was about 36% and 14% smaller than that of PLS and LWR, respectively, when ibuprofen was a residual drug substance. Similarly, LW-PLS with the weighting technique was about 39% and 24% better than PLS and LWR in RMSEP, respectively, when magnesium stearate was a residual excipient. The combination of IR-RAS and LW-PLS with the proposed weighting technique is a very useful rapid measurement technique of the residual drug substances.     

Effects of nonlinearities and uncorrelated or correlated errors in realistic simulated data on the prediction abilities of augmented classical least squares and partial least squares

Comparisons of prediction models from the new augmented classical least squares (ACLS) and partial least squares (PLS) multivariate spectral analysis methods were conducted using simulated data containing deviations from the idealized model. The simulated data were based on pure spectral components derived from real near-infrared spectra of multicomponent dilute aqueous solutions. Simulated uncorrelated concentration errors, uncorrelated and correlated spectral noise, and nonlinear spectral responses were included to evaluate the methods on situations representative of experimental data. The statistical significance of differences in prediction ability was evaluated using the Wilcoxon signed rank test. The prediction differences were found to be dependent on the type of noise added, the numbers of calibration samples, and the component being predicted. For analyses applied to simulated spectra with noise-free nonlinear response, PLS was shown to be statistically superior to ACLS for most of the cases. With added uncorrelated spectral noise, both methods performed comparably. Using 50 calibration samples with simulated correlated spectral noise, PLS showed an advantage in 3 out of 9 cases, but the advantage dropped to 1 out of 9 cases with 25 calibration samples. For cases with different noise distributions between calibration and validation, ACLS predictions were statistically better than PLS for two of the four components. Also, when experimentally derived correlated spectral error was added, ACLS gave better predictions that were statistically significant in 15 out of 24 cases simulated. On data sets with nonuniform noise, neither method was statistically better, although ACLS usually had smaller standard errors of prediction (SEPs). The varying results emphasize the need to use realistic simulations when making comparisons between various multivariate calibration methods. Even when the differences between the standard error of predictions were statistically significant, in most cases the differences in SEP were small. This study demonstrated that unlike CLS, ACLS is competitive with PLS in modeling nonlinearities in spectra without knowledge of all the component concentrations. This competitiveness is important when maintaining and transferring models for system drift, spectrometer differences, and unmodeled components, since ACLS models can be rapidly updated during prediction when used in conjunction with the prediction augmented classical least squares (PACLS) method, while PLS requires full recalibration.     

Determination of the polymorphic forms of bicifadine hydrochloride by differential scanning calorimetry-thermogravimetric analysis, X-ray powder diffraction, attenuated total reflectance-infrared spectroscopy, and attenuated total reflectance-near-infrared spectroscopy

The pharmaceutical compound bicifadine hydrochloride, which has been found to crystallize in two polymorphic forms, has been characterized by thermal analysis, X-ray powder diffraction (XRPD), infrared (IR) spectroscopy, and near-infrared (NIR) spectroscopy. A series of 22 sample mixtures of polymorph 1 and polymorph 2 were prepared and calibration models for the quantitation of these binary mixtures have been developed for each of the XRPD, attenuated total reflectance (ATR)-IR, and ATR-NIR analytical techniques. The quantitative results were obtained using a partial least squares (PLS) algorithm, which predicted the concentration of polymorph 1 from the XRPD spectra with a root mean standard error of prediction (RMSEP) of 4.4%, from the IR spectra with a RMSEP of 3.8%, and from the NIR spectra with a RMSEP of 1.4%. The studies indicate that when analyses are carried out on equivalent sets of spectra, NIR spectroscopy offers significant advantages in quantitative accuracy as a tool for the determination of polymorphs in the solid state and is also more convenient to use than both the ATR-IR and XRPD methods. Density functional theory (DFT) B3LYP calculations and IR spectral simulation have been used to determine the nature of the vibrational modes that are the most sensitive in the analysis.     

In vivo glucose measurement by surface-enhanced Raman spectroscopy

This paper presents the first in vivo application of surface-enhanced Raman scattering (SERS). SERS was used to obtain quantitative in vivo glucose measurements from an animal model. Silver film over nanosphere surfaces were functionalized with a two-component self-assembled monolayer, and subcutaneously implanted in a Sprague-Dawley rat such that the glucose concentration of the interstitial fluid could be measured by spectroscopically addressing the sensor through an optical window. The sensor had relatively low error (RMSEC = 7.46 mg/dL (0.41 mM) and RMSEP = 53.42 mg/dL (2.97 mM).     

Multivariate calibration and instrument standardization for the rapid detection of diethylene glycol in glycerin by Raman spectroscopy

The transfer of a multivariate calibration model for quantitative determination of diethylene glycol (DEG) contaminant in pharmaceutical-grade glycerin between five portable Raman spectrometers was accomplished using piecewise direct standardization (PDS). The calibration set was developed using a multi-range ternary mixture design with successively reduced impurity concentration ranges. It was found that optimal selection of calibration transfer standards using the Kennard-Stone algorithm also required application of the algorithm to multiple successively reduced impurity concentration ranges. Partial least squares (PLS) calibration models were developed using the calibration set measured independently on each of the five spectrometers. The performance of the models was evaluated based on the root mean square error of prediction (RMSEP), calculated using independent validation samples. An F-test showed that no statistical differences in the variances were observed between models developed on different instruments. Direct cross-instrument prediction without standardization was performed between a single primary instrument and each of the four secondary instruments to evaluate the robustness of the primary instrument calibration model. Significant increases in the RMSEP values for the secondary instruments were observed due to instrument variability. Application of piecewise direct standardization using the optimal calibration transfer subset resulted in the lowest values of RMSEP for the secondary instruments. Using the optimal calibration transfer subset, an optimized calibration model was developed using a subset of the original calibration set, resulting in a DEG detection limit of 0.32% across all five instruments.     

Importance of prediction outlier diagnostics in determining a successful inter-vendor multivariate calibration model transfer

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.     

Nanoliter serum sample analysis by mid-infrared spectroscopy for minimally invasive blood-glucose monitoring

The aim of this study was to demonstrate that mid-infrared spectroscopy is able to quantify glucose in a serum matrix with sample volumes well below 1 muL. For this, we applied mid-infrared attenuated total reflectance (ATR) or transmission-based spectroscopic methods to glucose quantification in microsamples of dry-film sera, either undiluted or diluted 10 times in distilled water. The sample series spanned physiological glucose concentrations between 50 and 600 mg/dL and volumes of 80, 8, and 1 nL. Calibration was carried out using multivariate partial least-squares (PLS) modeling with spectral data between 1180 and 940 cm(-1). Best performance was achieved in the ATR experiments. For raw ATR spectra, the optimum standard error of prediction (SEP) of 13.3 mg/dL was obtained for the 8 nL sample series with subsequent 10-fold dilution. With respect to the coefficient of variation of the glucose assay, CV(pred), we obtained a value of 3% for the 80 nL volume samples with spectral preprocessing using matrix protein absorption bands as an internal standard, 4% for the 8 nL samples, and 6% for the 1 nL samples with raw data. Spectral standardization resulted in significant improvement, especially for the 80 nL volume sample series. By contrast, the accuracy of the glucose assay for the 1 nL sample volume series could not be improved either by internal standardization or by considering the dry film areas for normalization, which we attribute to varying topographies of the dry films.     

Spectrophotometric and chemometric studies on the simultaneous determination of two benzodiazepines in human plasma

A numerical simple, accurate and precise method based on spectrophotometric data coupled with multivariate calibration methods, PLS and MLR, combined with GA was developed for the simultaneous determination of two benzodiazepines, Clobazam and Flurazepam. A data set of absorption spectra obtained from a calibration set of mixtures containing the compounds was used to build GA-PLS and GA-MLR models. The models were tested using a dataset constructed from the compound synthetic solutions. The better model was also applied to plasma samples. The proposed method requires no preliminary separation steps and can be used for these drugs analysis in quality control laboratories.     

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path Fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

The use of multiple calibration sets in partial least squares (PLS) regression was proposed to improve the quantitative determination of NH(3) over wide concentration ranges from open-path Fourier transform infrared (OP/FT-IR) spectra. The spectra were measured near animal farms, where the path-integrated concentration of NH(3) can fluctuate from nearly zero to as high as approximately 1000 ppm-m. PLS regression with a single calibration set did not cover such a large concentration range effectively, and the quantitative accuracy was degraded due to the nonlinear relationship between concentration and absorbance for spectra measured at low resolution (1 cm(-1) and poorer.) In PLS regression with multiple calibration sets, each calibration set covers a part of the entire concentration range, which significantly decreases the serious nonlinearity problem in PLS regression occurring when only a single calibration set is used. The relative error was reduced from approximately 6% to below 2%, and the best results were obtained with four calibration sets, each covering one quarter of the entire concentration range. It was also found that it was possible to build the multiple calibration sets easily and efficiently without extra measurements.