Quantitation of drug content in a low dosage formulation by transmission near infrared spectroscopy

A transmission near infrared (NIR) spectroscopic method has been developed for the nondestructive determination of drug content in tablets with less than 1% weight of active ingredient per weight of formulation (m/m) drug content. Tablets were manufactured with drug concentrations of ∼0.5%, 0.7%, and 1.0% (m/m) and ranging in drug content from 0.71 to 2.51 mg per tablet. Transmission NIR spectra were obtained for 110 tablets that constituted the training set for the calibration model developed with partial least squares regression. The reference method for the calibration model was a validated UV spectrophotometric method. Several data preprocessing methods were used to reduce the effect of scattering on the NIR spectra and base the calibration model on spectral changes related to the drug concentration changes. The final calibration model included the spectral range from 11 216 to 8662 cm⁻¹ the standard normal variate (SNV), and first derivative spectral pretreatments. This model was used to predict an independent set of 48 tablets with a root mean standard error of prediction (RMSEP) of 0.14 mg, and a bias of only −0.05 mg per tablet. The study showed that transmission NIR spectroscopy is a viable alternative for nondestructive testing of low drug content tablets, available for the analysis of large numbers of tablets during process development and as a tool to detect drug agglomeration and evaluate process improvement efforts. Published: March 24, 2006     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

Quantification of blockiness in pectins—A comparative study using vibrational spectroscopy and chemometrics

The gelling properties of pectins are related not only to the degree of esterification (DE), but also to the distribution of the ester groups. In this study, we have examined an experimentally designed series of 31 pectins originating from the same mother pectin and de-esterified using combinations of two different enzymatic mechanisms. The potential of using infrared (IR), Raman, and near infrared (NIR) spectroscopies combined with chemometrics for reliable and rapid determination of the DE and distribution patterns of methyl ester groups in a designed set of pectin powders was investigated. Quantitative calibration models using partial least squares (PLS) regression were developed and compared. The calibration models for prediction of DE obtained on extended inverse signal correction (EISC)-treated spectra of all three spectroscopic methods yielded models with cross-validated prediction errors (RMSECV) between 1.1%p and 1.6%p DE and correlation coefficients of 0.99. A calibration model predicting degree of random de-esterification (R) and block de-esterification (B) was developed for each spectroscopic method, yielding RMSECV values between 4.4 and 6.7 and correlation coefficients (r) between 0.79 and 0.92. Variable selection using interval PLS (iPLS) significantly improved the prediction of R for IR spectroscopy, yielding RMSECV of 3.5 and correlation coefficients of 0.95. All three spectroscopic methods were able to distinguish the spectral patterns of pectins with different enzyme treatments in simple classification models by principal component analysis (PCA). Extended canonical variate analysis revealed one specific signal in the Raman (1045 cm⁻¹) spectrum and one significant area (1250-1400 cm⁻¹) in the IR spectrum which are able to classify the pectin samples according to the four different enzyme treatments. In both Raman and IR spectra, the signal intensity decreased in the sequence R-B > B > B-R > R > re-methylated pectin.     

Design of Experiments-Based Monitoring of Critical Quality Attributes for the Spray-Drying Process of Insulin by NIR Spectroscopy

Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q² of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.KEY WORDS: moisture content, multivariate analysis, NIR, particle size, spray-drying     

The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods

The development of a rapid, accurate and cost-effective method for the prediction of constituents related to soil nitrogen (N) supply is considered important. The potential of using visible (Vis) and near infrared reflectance (NIR) spectroscopy (400–2500 nm) as such a method was investigated. Vis–NIR calibrations were performed for organic carbon (C_org) and total N (N_tot) content and their potential mineralisation using 80 grassland soil samples of rather heterogeneous origin. Prediction accuracy was tested using a 'take-out-four' validation strategy (48 samples). Within investigated variables a ratio of standard deviation of reference data to standard error of bias corrected prediction (RPD) within 1.7 (r²=0.65) and 2.7 (r²=0.87) were achieved. Apparent differences in Vis–NIR prediction accuracy among the variables were partly due to errors in the reference values. Thawed moist samples tend to be more accurately predicted than dried samples, and no benefit was derived from the grinding of sieved (4 mm) and dried samples. Prediction accuracy did not differ using two different systems for sample presentation to the Vis–NIR analyses. Comparative predictions of C_org and N_tot and their potential mineralisations were performed using the take-out-four validation strategy and simple linear regression to loss on ignition (LOI) values and hot KCl extracted NH₄-N (N_hotKCl) values as predictors. Likewise, the reference values of C_org and N_tot were also used as predictors for each other and for the potential C and N mineralisation constituents. Accuracy obtained for the Vis–NIR predictions of investigated constituents was in general equal or better than prediction accuracy obtained by these comparative methods. The Vis–NIR method provided promising predictions of variables important for the soil N supply.     

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km² and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg⁻¹; R² (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg⁻¹; R² = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R² and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy.     

Screening soy hydrolysates for the production of a recombinant therapeutic protein in commercial cell line by combined approach of near-infrared spectroscopy and chemometrics

Soy hydrolysates are widely used as the major nutrient sources for cell culture processes for industrial manufacturing of therapeutic recombinant proteins. The primary goal of this study was to develop a spectroscopy based chemometric method, a partial least squares (PLS), to screen soy hydrolysates for better yield of protein production (titers) in cell culture medium. Harvest titer values of 29 soy hydrolysate lots with production yield between 490 and 1,350 mg/L were obtained from shake flask models or from manufacture engineering runs. The soy hydrolysate samples were measured by near-infrared (NIR) in reflectance mode using an infrared fiber optic probe. The fiber optic probe could easily enable in situ measurement of the soy hydrolysates for convenient raw material screening. The best PLS calibration has a determination coefficient of R ²?=?0.887 utilizing no spectral preprocessing, the two spectral ranges of 10,000–5,376 cm^?1 and 4,980–4,484 cm^?1, and a rank of 6 factors. The cross-validation of the model resulted in a determination coefficient of R ²?=?0.741 between the predicted and actual titer values with an average standard deviation of 72 mg/L. Compared with the resource demanding shake flask model, the combination of NIR and chemometric modeling provides a convenient method for soy hydrolysate screening with the advantage of fast speed, low cost and non-destructive.     

Quantitative determination of cellulose dissolved in 1-ethyl-3-methylimidazolium acetate using partial least squares regression on FTIR spectra

Rapid and quantitative measurements of cellulose concentrations in ionic liquids (ILs) are difficult. In this study, FTIR operated in attenuated total reflectance (ATR) mode was investigated as a tool to measure cellulose concentration in 1-ethyl-3-methylimidazolium acetate ([emim][OAc]) and the spectra were subjected to partial least squares (PLS) regression for the quantitative determination of cellulose content. Additionally, the spectra were subjected to 7 data preprocessing methods to reduce physical effects in the spectra. Peak normalization was found to be the technique that most improved the prediction of dissolved cellulose in [emim][OAc]. When peak normalization was used for data preprocessing, a model for the quantitative estimation of cellulose content between 0 wt.% and 4 wt.% with an error of 0.53 wt.% was generated. The methods described here provide the basis for a rapid and facile technique for the determination of dissolved cellulose content in [emim][OAc].     

Feasibility study on the potential of visible and near infrared reflectance spectroscopy to measure alpaca fibre characteristics

Visible (Vis) and near infrared (NIR) reflectance spectroscopy is a rapid and non-destructive technique that has found many applications in assessing the quality of agricultural commodities, including wool. In this study, Vis and NIR spectroscopy combined with multivariate data analysis was investigated regarding its feasibility in predicting a range of fibre characteristics in raw alpaca wool samples. Mid-side samples (n = 149) were taken from alpacas from a range of colours and ages at shearing time over 4 years (2000 to 2004) and subsequently analysed for fibre characteristics such as mean fibre diameter (MFD) and standard deviation (and coefficient of variation), spin fineness, curvature degree (and standard deviation), comfort factor, medullation percentage (by weight and number in white samples only) using traditional reference laboratory testing methods. Samples were scanned in a large cuvette using a FOSS NIRSystems 6500 monochromator instrument in reflectance mode in the Vis and NIR regions (400 to 2500 nm). Partial least squares (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Mathematical pre-treatment of the spectra (second derivative) as well as various combinations of wavelength range were used in model development. The best calibration model was found when using the NIR region (1100 to 2500 nm) for the prediction of MFD, which had a coefficient of determination in cross-validation (R2) of 0.88 with a root mean square standard error of cross validation (RMSECV) of 2.62 μm. The results show the NIR technique to have promise as a semi-quantitative method for screening purposes. The lack of grease in alpaca wool samples suggests that the technique might find ready application as a rapid measurement technique for preliminary classing of shorn fleeces or, if used directly on the animal, the technology might offer an objective tool to assist in the selection of animals in breeding programmes or shows.     

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R²CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R²CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R²CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R² of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.     

Application of vibrational spectroscopy in the quality assessment of Buchu oil obtained from two commercially important Agathosma species (Rutaceae)

Quality assessment of natural raw materials and derived consumer products is often done using conventional analytical techniques such as liquid and gas chromatography which are expensive and time consuming. This paper reports on the use of vibrational spectroscopy techniques as possible alternatives for the rapid and inexpensive assessment of the quality of ‘buchu oil’ obtained from two South African species; Agathosma betulina and Agathosma crenulata belonging to the Rutaceae family. Samples of A. betulina (55) and A. crenulata (16) were collected from different natural localities and cultivation sites in South Africa. The essential oil was obtained by hydrodistillation and scanned on Near infrared (NIR), mid infrared (MIR) and Raman spectrometers. The spectral data obtained was processed using chemometric techniques and orthogonal partial least squares discriminant analysis (OPLS-DA) was used to clearly differentiate between A. betulina and A. crenulata. The OPLS-DA technique also proved to be a useful tool to identify wave regions that contain biomarkers (peaks) that contributed to the separation of the two species. The three spectroscopy techniques were also evaluated for their ability to accurately predict the percentage composition of seven major compounds that occur in A. betulina ‘buchu’ oil. Using GC–MS reference data, calibration models were developed for the MIR, NIR and Raman spectral data to predict/profile the major compounds in ‘buchu oil’. A comparison of the three spectroscopy techniques showed that MIR together with PLS algorithms produced the best model (R²X = 0.96; R²Y = 0.88 and Q²Ycum = 0.85) for the quantification of six of the seven major oil constituents. The MIR model showed high predictive power for pseudo-diosphenol (R² = 0.97), isomenthone (R² = 0.97), menthone (R² = 0.90), limonene (R² = 0.91), pulegone (R² = 0.96) and diosphenol (R² = 0.85). These results illustrate the potential of MIR spectroscopy as a rapid and inexpensive alternative to predict the major compounds in buchu oil.     

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets

Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm^?1, 7515.24 to 7108.33 cm^?1, and 5257.00 to 5098.87 cm^?1 were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm^?1was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process.     

Multivariate Analysis of Phenol in Freeze-Dried and Spray-Dried Insulin Formulations by NIR and FTIR

Dehydration is a commonly used method to stabilise protein formulations. Upon dehydration, there is a significant risk the composition of the formulation will change especially if the protein formulation contains volatile compounds. Phenol is often used as excipient in insulin formulations, stabilising the insulin hexamer by changing the secondary structure. We have previously shown that it is possible to maintain this structural change after drying. The aim of this study was to evaluate the residual phenol content in spray-dried and freeze-dried insulin formulations by Fourier transform infrared (FTIR) spectroscopy and near infrared (NIR) spectroscopy using multivariate data analysis. A principal component analysis (PCA) and partial least squares (PLS) projections were used to analyse spectral data. After drying, there was a difference between the two drying methods in the phenol/insulin ratio and the water content of the dried samples. The spray-dried samples contained more water and less phenol compared with the freeze-dried samples. For the FTIR spectra, the best model used one PLS component to describe the phenol/insulin ratio in the powders, and was based on the second derivative pre-treated spectra in the 850–650 cm⁻¹ region. The best PLS model based on the NIR spectra utilised three PLS components to describe the phenol/insulin ratio and was based on the standard normal variate transformed spectra in the 6,200–5,800 cm⁻¹ region. The root mean square error of cross validation was 0.69% and 0.60% (w/w) for the models based on the FTIR and NIR spectra, respectively. In general, both methods were suitable for phenol quantification in dried phenol/insulin samples.     

Near infrared spectroscopy and aquaphotomics: Novel approach for rapid in vivo diagnosis of virus infected soybean

Near infrared spectroscopy with aquaphotomics as a novel approach was assessed for the diagnosis of soybean plants (Glycine max) infected with soybean mosaic virus (SMV) at latent symptomless stage of the disease. Near infrared (NIR) leaf spectra (in the range of 730-1025 nm) acquired from soybean plants with and without the inoculation of SMV were used. Leaf samples from all plants were assayed with enzyme-linked immunosorbent assay (ELISA) to confirm the infection. Previously reported NIR band for water at 970 nm and two new bands at 910 nm and 936 nm in the water specific region of NIR were found to be markedly sensitive to the SMV infection 2 weeks prior to the appearance of visual symptoms on infected leaves. The spectral calibration model soft independent modeling of class analogy (SIMCA), predicted the disease with 91.6% sensitivity and 95.8% specificity when the second order derivative of the individual plant averaged spectra were used. The study shows the potential of NIR spectroscopy with its novel approach to elucidate latent biochemical and biophysical information of an infection as it allowed successful discrimination of SMV infected plant from healthy at the early symptomless stage of the disease.     

Non-destructive determination of cellulose functional groups and molecular weight in pulp hand sheets and historic papers by NIR-PLS-R

A fast and non-destructive method to evaluate the condition of pulp and paper was developed. Partial least square regression (PLS-R) models based on near infrared (NIR) spectra and reference values for molecular weight, carbonyl group content and carboxyl group content were calculated for pulp hand sheets and rag papers.In this study, 110 pulp hand sheets were used and gave satisfactory models with high correlation coefficients (up to 0.97) during validation; whereas the test set validation (external validation) results were always better than those of cross-validation.Modeling of 267 historic rag paper samples was more demanding due to inherent variability of the material. Nevertheless, PLS-R models for the carbonyl group content, carboxyl group content and molecular weight with good correlation coefficients (up to 0.93) and low errors for cross-validation using average spectra of different paper samples were obtained. For carbonyl group content models with good correlation coefficient was also obtained without previous averaging. Joint models using both pulp hand sheets and rag papers were calculated for carboxyl and carbonyl group contents resulting in lower correlation coefficients then the single models.     

Optimization of resolution and sensitivity of 4D NOESY using Multi-dimensional Decomposition

Highly resolved multi-dimensional NOE data are essential for rapid and accurate determination of spatial protein structures such as in structural genomics projects. Four-dimensional spectra contain almost no spectral overlap inherently present in lower dimensionality spectra and are highly amenable to application of automated routines for spectral resonance location and assignment. However, a high resolution 4D data set using conventional uniform sampling usually requires unacceptably long measurement time. Recently we have reported that the use of non-uniform sampling and multi-dimensional decomposition (MDD) can remedy this problem. Here we validate accuracy and robustness of the method, and demonstrate its usefulness for fully protonated protein samples. The method was applied to 11 kDa protein PA1123 from structural genomics pipeline. A systematic evaluation of spectral reconstructions obtained using 15–100% subsets of the complete reference 4D ¹H–¹³C–¹³C–¹H NOESY spectrum has been performed. With the experimental time saving of up to six times, the resolution and the sensitivity per unit time is shown to be similar to that of the fully recorded spectrum. For the 30% data subset we demonstrate that the intensities in the reconstructed and reference 4D spectra correspond with a correlation coefficient of 0.997 in the full range of spectral amplitudes. Intensities of the strong, middle and weak cross-peaks correlate with coefficients 0.9997, 0.9965, and 0.83. The method does not produce false peaks. 2% of weak peaks lost in the 30% reconstruction is in line with theoretically expected noise increase for the shorter measurement time. Together with good accuracy in the relative line-widths these translate to reliable distance constrains derived from sparsely sampled, high resolution 4D NOESY data.     

Assessment of embryonic growth in chicken eggs by means of visible transmission spectroscopy

During this work, it was investigated whether spectral measurements can be used to monitor embryonic growth. An experiment was conducted in which both the transmission spectra and embryonic weight were determined on 240 eggs (Cobb, 37 weeks) between Day 5 and Day 10 of incubation. The spectral data were linked to embryonic weight by means of a partial least squares analysis. Different preprocessing procedures were compared during this work, that is, smoothing, multiplicative scatter correction (MSC), and first‐ and second‐order derivative. Compared to the remainder of the preprocessing procedures, MSC leads to a considerable improvement of the prediction capability of the embryonic weight. The ratio of performance to deviation obtained for the MSC spectra equaled 4.5 indicating that a very accurate prediction of embryonic weight is feasible based on the VIS/NIR transmission measurements. Important regions for the prediction are situated around 685–740 nm. It is suggested that the spectral changes in these spectral regions result from the displacement of carotenoids from the yolk into the blood circuitry. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Quantitation of hepcidin in human urine by liquid chromatography-mass spectrometry

Hepcidin is a peptide hormone that functions as a key regulator of mammalian iron metabolism. Serum and urine levels are increased in inflammation and suppressed in hemochromatosis, and they may have diagnostic importance. This study describes the development and validation of an analytical method for the quantitative determination of the concentration of hepcidin in clinical samples. A stable, isotopically labeled internal standard, [¹⁵N,¹³C₂]Gly12,20-hepcidin, was synthesized and a standard quantity was added to urine samples. Extraction was performed using weak cation exchange magnetic nanoparticles. An ion trap mass spectrometer was used to quantify hepcidin in the samples. The hepcidin assay was validated, and good recovery of hepcidin was obtained. The assay is accurate and precise. Urinary hepcidin levels of 3 to 9 nmol/mmol creatinine⁻¹ were found in healthy controls, with reduced levels in hemochromatosis (P < 0.00006) and elevated levels in inflammation (P < 0.00035). In sickle cell disease, a wide range was found, with the mean value not differing significantly from controls (P < 0.26). In summary, a validated method has been developed for the quantitation of hepcidin using a stable, isotopically labeled internal standard and applied to determine the concentrations of hepcidin in the low nanomolar range in urine samples from patients and controls.     

Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study

In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEP_cv) and maximal coefficient of determination (R²_cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R²_cv) = 0.89, RMSEP_cv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R²_cv) ˂ 0.81, RMSEP_cv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R²_cv) = 0.44, RMSEP_cv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.     

Automated immunoassay system for AFP-L3% using on-chip electrokinetic reaction and separation by affinity electrophoresis

Implementation of the on-chip immunoassay for α-fetoprotein (AFP)-L3% was achieved using a fully automated microfluidic instrument platform that will prepare the chip and run the assay with a total assay time of less than 10 min. Reagent/sample mixing, concentration, and reaction in microfluidic channels occur by the electrokinetic analyte transport assay (EATA) technique, enabling the integration of all assay steps on-chip. The determination of AFP-L3%, a biomarker for hepatocellular carcinoma, was achieved by the presence of Lens culinaris agglutinin in the separation channel, causing separation of the fucosylated isoform, AFP-L3, from the nonfucosylated AFP-L1 by lectin affinity electrophoresis. Laser-induced-fluorescence (LIF) detection was used to quantitate the labeled immunocomplexes. The limit of detection (LOD) was 0.1 ng/ml AFP, and assay precision of less than 2% coefficient of variation (CV) was obtained for quantitation from 24 to 922 ng/ml total AFP in spiked serum samples. Assay precision of less than 3% CV was obtained for AFP-L3% measurements from 8.5 to 81%. Furthermore, good correlation of test results for 68 patient serum samples with a commercially available reference method (LiBASys assay for AFP-L3%) was obtained, with r² = 0.981 and slope = 1.03.