Selectivity for glucose,glucose-6-phosphate,and pyruvate in ternary mixtures from the multivariate analysis of near-infrared spectra

Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000–5,000 cm⁻¹) for a set of 60 standard solutions maintained at 37 °C. These standard solutions are composed of randomized concentrations (0.5–30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose. Figure Combination near-infrared spectroscopy allows selective analytical measurements for glucose, glucose-6-phosphate, and pyruvate in ternary mixtures owing to the uniqueness of the individual absorption spectra for each solute     

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L⁻¹ for Penicillin V and 0.32 g L⁻¹ for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L⁻¹ for Penicillin V and 0.15 g L⁻¹ for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.     

Evaluation of selectivity and robustness of near-infrared glucose measurements based on short-scan Fourier transform infrared interferograms

The selectivity and robustness of near-infrared (near-IR) calibration models based on short-scan Fourier transform (FT) infrared interferogram data are explored. The calibration methodology used in this work employs bandpass digital filters to reduce the frequency content of the interferogram data, followed by the use of partial least-squares (PLS) regression to build calibration models with the filtered interferogram signals. Combination region near-IR interferogram data are employed corresponding to physiological levels of glucose in an aqueous matrix containing variable levels of alanine, sodium ascorbate, sodium lactate, urea, and triacetin. A randomized design procedure is used to minimize correlations between the component concentrations and between the concentration of glucose and water. Because of the severe spectral overlap of the components, this sample matrix provides an excellent test of the ability of the calibration methodology to extract the glucose signature from the interferogram data. The robustness of the analysis is also studied by applying the calibration models to data collected outside of the time span of the data used to compute the models. A calibration model based on 52 samples collected over 4 days and employing two digital filters produces a standard error of calibration (SEC) of 0.36 mM glucose. The corresponding standard errors of prediction (SEP) for data collected on the 5th (18 samples) and 7th (10 samples) day are 0.42 and 0.48 mM, respectively. The interferogram segment used for the analysis contained only 155 points. These results are compatible with those obtained in a conventional analysis of absorbance spectra and serve to validate the viability of the interferogram-based calibration.     

Measurement of lactate in whole human blood with near-infrared transmission spectroscopy

We have evaluated the potential of near-infrared spectroscopy (NIRS) as a technique for rapid analysis of lactate in whole blood. To test the NIRS technique, a comparison was made with a standard clinical method using whole blood samples taken from five exercising human subjects at three different stage of exercise. To expand lactate concentration within the physiological range, standard additions method was used to generate 45 unique data points. Spectra were collected over the 2050-2400 nm spectral range with a 1 mm optical path length quartz cell. Reference lactate concentrations in the samples were determined by enzymatic measurements. Estimates and calibration of the lactate concentration with NIRS was made using partial least squares (PLS) regression analysis and leave-N-out cross validation on second derivative spectra. Separate calibrations were determined from each of the subject samples and cumulative PRESS was used to determine the number of PLS factors in the final model. The results from the PLS model presented are generated from the five individual calibration coefficient vectors and provided a correlation coefficient of 0.978 and a standard error of cross validation of 0.65 mmol l⁻¹ between the enzymatic assay and the NIRS technique. To study the parameters that impact the spectra baseline and the correlation between the calculated model and the data, referenced measurements of lactate against baseline spectrum were made for each individual. A correlation coefficient of 0.992 and a standard error of cross validation of 0.21 mmol l⁻¹ were found. The results suggest that NIRS may provide a valuable tool to assess physiological status for both research and clinical needs.     

Quantitative Determination of Glycine,Alanine, Aspartic Acid,Glutamic Acid,Phenylalanine, and Tryptophan by Raman Spectroscopy

The development of a new quantitative method for amino acids using Raman spectroscopy is reported. Raman spectra of glycine, alanine, aspartic acid, glutamic acid, phenylalanine, and tryptophan were measured. The band ratio between the Raman intensity of the amino acid and that of acetonitrile as an external standard was calculated to remove the influence of factors such as laser power intensity and instrumental effects. The calibration curves were obtained by plotting the band ratios against the concentrations of the amino acids. The curves were linear with coefficient correlations of over 0.99 for all amino acids. The Raman spectra of known concentration samples were measured to confirm the reproducibility of this method. The relative errors were small, indicating that the concentrations of amino acids can be determined using Raman spectroscopy. The limits of detection and quantitation were determined as thrice and 10 times the standard deviation of the background signal to be 0.007 and 0.02?mol?L^?1, respectively. Raman spectra of aspartic acid at 0.02?mol?L^?1 were measured several times and the uncertainty was 7%.     

PLS and first derivative of ratio spectra methods for determination of hydrochlorothiazide and propranolol hydrochloride in tablets

Two new analytical methods have been developed as convenient and useful alternatives for simultaneous determination of hydrochlorothiazide (HCT) and propranolol hydrochloride (PRO) in pharmaceutical formulations. The methods are based on the first derivative of ratio spectra (DRS) and on partial least squares (PLS) analysis of the ultraviolet absorption spectra of the samples in the 250–350-nm region. The methods were calibrated between 8.7 and 16.0 mg L⁻¹ for HCT and between 14.0 and 51.5 mg L⁻¹ for PRO. An asymmetric full-factorial design and wavelength selection (277–294 nm for HCT and 297–319 for PRO) were used for the PLS method and signal intensities at 276 and 322 nm were used in the DRS method for HCT and PRO, respectively. Performance characteristics of the analytical methods were evaluated by use of validation samples and both methods showed to be accurate and precise, furnishing near quantitative analyte recoveries (100.4 and 99.3% for HCT and PRO by use of PLS) and relative standard deviations below 2%. For PLS the lower limits of quantification were 0.37 and 0.66 mg L⁻¹ for HCT and PRO, respectively, whereas for DRS they were 1.15 and 3.05 mg L⁻¹ for HCT and PRO, respectively. The methods were used for quantification of HCT and PRO in synthetic mixtures and in two commercial tablet preparations containing different proportions of the analytes. The results of the drug content assay and the tablet dissolution test were in statistical agreement (p < 0.05) with those furnished by the official procedures of the USP 29. Preparation of dissolution profiles of the combined tablet formulations was also performed with the aid of the proposed methods. The methods are easy to apply, use relatively simple equipment, require minimum sample pre-treatment, enable high sample throughput, and generate less solvent waste than other procedures. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Selective removal of interference signals for near-infrared spectra of biomedical samples by using region orthogonal signal correction

New approach for chemometrics algorithm named region orthogonal signal correction (ROSC) has been introduced to improve the predictive ability of PLS models for biomedical components in blood serum developed from their NIR spectra in the 1280-1849 nm region. Firstly, a moving window partial least squares regression (MWPLSR) method was employed to locate the region due to water as a region of interference signals and to find the informative regions of glucose, albumin, cholesterol and triglyceride from NIR spectra of bovine serum samples. Next, a novel chemometrics method named searching combination moving window partial least squares (SCMWPLS) was used to optimize those informative regions. Then, the specific regions that contained the information of water, glucose, albumin, cholesterol and triglyceride were obtained. When an interested component in the bovine serum solution, such as glucose, albumin, cholesterol or triglyceride is being an analyte, the other three interests and water are considered as the interference factors. Thus, new approach for ROSC has employed for each specific region of interference signal to calculate the orthogonal components to the concentrations of analyte that were removed specifically from the NIR spectra of bovine serum in the region of 1280-1849 nm and the highest interference signal for model of analyte will be revealed. The comparison of PLS results for glucose, albumin, cholesterol and triglyceride built by using the whole region of original spectra and those developed by using the optimized regions suggested by SCMWPLS of original spectra, spectra treated OSC for orthogonal components of 1-3 and spectra treated ROSC using selected removing the highest interference signals from the spectra for orthogonal components of 1-3 are reported. It has been found that new approach of ROSC to remove the highest interference signal located by SCMWPLS improves of the performance of PLS modeling, yielding the lower RMSECV and smaller number of PLS factors.     

Wavelet analysis used for spectral background removal in the determination of glucose from near-infrared single-beam spectra

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than 6 months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum.     

Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: case study for the analysis of an etching solution containing hydrogen peroxide

Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH₄OH, H₂O₂ and H₂O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000-7500 cm⁻¹) were used to build PLS models. The selective determination of H₂O₂ without influences from NH₄OH and H₂O was a key issue since its molecular structure is similar to that of H₂O and NH₄OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH₄OH and H₂O₂ were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH₄OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H₂O₂ was broadly overlapping and much less distinct than that from NH₄OH, the selectivity and prediction performance for the H₂O₂ calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H₂O₂ calibration. A robust H₂O₂ calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.     

Quantitative analysis of methyl green using surface-enhanced resonance Raman scattering

Surface-enhanced resonance Raman scattering (SERRS) spectra of aqueous solutions of the triphenylmethane dye methyl green have been obtained for the first time by use of citrate-reduced silver colloids and a laser excitation wavelength of 632.8 nm. Given the highly fluorescent nature of the analyte, which precluded collection of normal Raman spectra of the dye in solution and powdered state, it was highly encouraging that SERRS spectra showed no fluorescence due to quenching by the silver sol. The pH conditions for SERRS were optimised over the pH range 0.5–10 and the biggest enhancement for SERRS of this charged dye was found to be at pH 2.02, thus this condition was used for quantitative analysis. SERRS was found to be highly sensitive and enabled quantitative determination of methyl green over the range 10⁻⁹ to 10⁻⁷ mol dm⁻³. Good fits to correlation coefficients were obtained over this range using the areas under the vibrational bands at 1615 and 737 cm⁻¹. Finally, a limit of detection of 83 ppb was calculated, demonstrating the sensitivity of the technique.     

Electron-transfer properties of different carbon nanotube materials,and their use in glucose biosensors

Different types of carbon nanotube material (single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) of different internal diameter) have been used for preparation of CNT-modified glassy-carbon electrodes. Redox reactions involving ferricyanide and hydrogen peroxide were examined at the CNT-modified electrodes. Electrodes modified with SWCNTs usually had better electron-transfer properties than MWCNT-modified electrodes. Glucose biosensors were also prepared with electropolymerized polyphenylenediamine films, CNT materials, and glucose oxidase. Amperometric behavior in glucose determination was examined. SWCNT-modified glucose biosensors usually had a wider dynamic range (from 0.1 to 5.5 mmol L⁻¹) and greater sensitivity in glucose determination. The detection limit was estimated to be 0.05 mmol L⁻¹.     

Comparison of infrared attenuated total reflection and Raman spectroscopy in the quantitative analysis of diclofenac sodium in tablets

The quantification of diclofenac sodium (DS) in tablets was performed using partial least squares (PLS) models based on FTIR ATR (Fourier transform infrared attenuated total reflection) and FT-Raman spectra. Separate calibration models were built for two groups of tablets, standard and sustained release, containing different excipients. To compare the predictive ability of these models the relative standard errors of prediction (RSEP) were calculated. In the case of DS determination from the Raman data, RSEP error values in the range of 2.4-2.8% (2.7-2.9%) for the calibration (validation) data sets were obtained. For ATR models constructed using spectra registered three times for each sample, RSEP errors in the range of 3.6-3.7% (4.2-4.3%) were found. These errors decreased to 2.8% (3.0%) when spectra collected six times were applied. Five commercial products containing 25, 50, 75 and 100 mg of DS per tablet were quantified. Concentrations derived from the elaborated models correlated strongly with the results of reference analyses and gave recoveries of 99.1-101.3% and 99.1-101.7% for the ATR and Raman data, respectively. Although both spectroscopic techniques can be used as fast and convenient alternatives to the standard pharmacopeial methods of DS quantification in solid dosage forms, in the case of the ATR technique, it is necessary to repeat measurements at least a few times to obtain acceptable quantification errors.     

Influence of different laser irradiation methods on determination of composition content in polypropylene/low-density polyethylene blends using Raman spectroscopy

Sample movement makes a difference to raw Raman spectra and determination of composition content using Raman spectroscopy. Therefore, it is necessary to have further studies in this aspect. In this paper, different laser irradiation methods were investigated for determination of composition content in polypropylene (PP)/low-density polyethylene (LDPE) blends using Raman spectroscopy. Raw Raman spectra of PP sample were firstly collected using different laser irradiation methods. It was shown that the relative standard deviations (RSD) of PP sample under circle irradiation were ten times bigger than that under point irradiation at the little sacrifice of signal-to-noise ratio (SNR). In other words, rotating (or moving) PP sample during Raman spectra collection could signally improve sample representation. Owing to this, in combined with partial least squares (PLS), Raman quantitative analysis of PP concentration in PP/LDPE blends were performed by different laser irradiation methods. The results validated that blend samples with rotation during Raman measurement led to lower prediction errors in prediction of PP concentration. The best root-mean-square error of prediction (RMSEP) and coefficient of determination (R²) that were obtained for PP were respectively 2.10% and 0.9884.     

Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pretreatments and estimation of multivariate detection limits

Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600 ppm in an industrial ester. The detection limit obtained, round 100 ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes.     

Simultaneous determination of copper, nickel, cobalt and zinc using zincon as a metallochromic indicator with partial least squares

The partial least squares (PLS) applied to the simultaneous determination of the divalent ions of copper, nickel, cobalt and zinc based on the formation of their complexes with 2-carboxy-2′-hydroxy-5′-sulfoformazyl benzene (zincon). The absorption spectra were recorded from 515 through 750 nm. The effect of pH on sensitivity and the selectivity was studied in the range 3.0-10.0 and the pH 8.0 was choused according to net analyte signal (NAS) as a function of pH. The concentration range for Cu²⁺, Ni²⁺, Co²⁺and Zn²⁺ in solution calibration sets were 0-2.6, 0-4.6, 0-3.0 and 0-4.92 ppm, respectively. The root mean squares differences (RMSD) for copper, nickel, cobalt and zinc were 0.0181, 0.0488, 0.0309 and 0.0463, respectively.     

A feasibility study on the use of visible and short wavelengths in the near-infrared region for the non-destructive measurement of wine composition

The aim of this study was to explore the capability of spectroscopy in the visible (Vis) and short wavelength near-infrared (NIR) regions for the non-destructive measurement of wine composition in intact bottles. In this study we analysed a wide range of commercial wines obtained in Australia in different types of bottles (e.g. colours, diameters and heights), including different wine styles and varieties. Wine bottles were scanned in the Vis-NIR region (600–1,100 nm) in a monochromator instrument in transflectance mode. Principal component analysis (PCA) and partial least-squares (PLS) regression were used to interpret the spectra and develop calibrations for wine composition. Due to the relatively small number of samples available full cross-validation (leave-one-out) was used as validation. The coefficient of correlation in calibration and the standard error of cross-validation (SECV) were 0.67 (SECV: 0.48%), 0.83 (SECV: 4.01 mg L⁻¹), 0.70 (SECV: 28.6 mg L⁻¹) and 0.50 (SECV: 0.15) for alcohol content, total SO₂, free SO₂ and pH, respectively, in the set of wine samples analysed. These preliminary results showed that the assessment of wine composition by Vis and short wavelengths in the NIR is possible for either qualitative analysis (e.g. low-, medium- and high-quality grading), or for screening of composition during bottling and storage. Although low accuracy and precision were obtained for the chemical parameters routinely analysed in wine, calibration models for the chemical parameters were considered acceptable for screening purposes in terms of the standard errors obtained.     

Multidimensional Raman spectroscopic signature of sweat and its potential application to forensic body fluid identification

This proof-of-concept study demonstrated the potential of Raman microspectroscopy for nondestructive identification of traces of sweat for forensic purposes. Advanced statistical analysis of Raman spectra revealed that dry sweat was intrinsically heterogeneous, and its biochemical composition varies significantly with the donor. As a result, no single Raman spectrum could adequately represent sweat traces. Instead, a multidimensional spectroscopic signature of sweat was built that allowed for the presentation of any single experimental spectrum as a linear combination of two fluorescent backgrounds and three Raman spectral components dominated by the contribution from lactate, lactic acid, urea and single amino acids.     

Quantitative Analysis of Chromium(VI) in Dilute Solution by Using Adsorption and Diffuse Reflectance Near‐infrared Spectroscopy

A method for quantitative determination of metal element in aqueous solution was developed by using adsorption and diffuse reflectance near‐infrared spectroscopy (DRNIRS). In this method, the analyte is firstly adsorbed onto the resin from the dilute solution, and then the adsorbed analyte is directly determined in the sorbent by using DRNIRS. Enrichment of the analyte is achieved by the adsorption from the dilute solution, and quantitative determination is accomplished by using multivariate calibration technique. Taking chromium(VI) in river water as the analytical target, adsorption conditions and the partial least squares (PLS) model was optimized. The results show that chromium(VI) can be immobilized onto the adsorbent and quantitatively measured by DRNIRS and multivariate calibration. With cross validation and external validation, the correlation coefficient between the reference and predicted concentration was found to be above 0.98 in the range of 0.75–29.90 mg·L⁻¹ for the PLS model, and the interference of the coexisting matrix was eliminated with the aid of multivariate calibration.     

Simultaneous determination of paracetamol,phenylephrine hydrochloride and chlorpheniramine maleate in pharmaceutical preparations using multivariate calibration 1

Resolution of binary mixtures of paracetamol, phenylephrine hydrochloride and chlorpheniramine maleate with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable, principal component regression and hybrid linear analysis. Data of analysis were obtained from UV–vis spectra of the above compounds. The method of central composite design was used in the ranges of 1–15 mg L^?1 for both calibration and validation sets. The models refinement procedure and their validation were performed by cross-validation. Figures of merit such as selectivity, sensitivity, analytical sensitivity and limit of detection were determined for all three compounds. The procedure was successfully applied to simultaneous determination of the above compounds in pharmaceutical tablets.     

基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。