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.     

Quantitative determination of ethanol in heated plumes by passive Fourier transform infrared remote sensing measurements

Quantitative calibration models are developed for passive Fourier transform infrared (FT-IR) remote sensing measurements of open-air-generated vapors of ethanol. These experiments serve as a feasibility study for the use of passive FT-IR measurements in quantitative determinations of industrial stack emissions. A controlled-temperature plume generator is used to produce plumes of known concentrations of pure ethanol and mixtures of ethanol and methanol. Analyte plumes are generated over the path-averaged concentration range of 20-300 ppm-m and stack temperatures of 125, 150, 175, and 200 degrees C. A novel experimental setup is employed in which an ambient temperature polyvinyl chloride backdrop is placed behind the emission stack and used as a target for the passive IR measurements. An emission FT-IR spectrometer with telescope entrance optics is then employed to view the generated plumes against the backdrop. Signal processing techniques based on signal averaging and bandpass digital filtering are applied to both interferogram and single-beam spectral data obtained from these measurements, and the resulting filtered signals are used as inputs into the generation of multivariate partial least-squares (PLS) calibration models. Successful calibration models are obtained with both interferogram and spectral data, and neither analysis requires the collection of separate IR background data. For a set of validation data collected on a different day from the calibration measurements, standard errors of prediction of 30.6 and 32.2 ppm-m ethanol are obtained for the PLS models based on interferogram and spectral data, respectively.     

Robust absorbance computations in the analysis of glucose by near-infrared spectroscopy

Procedures for data acquisition and data processing are evaluated for the optimal computation of absorbance values based on Fourier transform near-infrared transmission spectra. Samples consisting of physiological levels (1–20 mM) of glucose in an aqueous matrix of variable levels of bovine serum albumin and triacetin are studied in the combination spectral region (5000–4000 cm⁻¹). The weak glucose signals in this region define a challenging analysis that is extremely sensitive to the effects of instrumental drift. The impact of different procedures for obtaining absorbance estimates is evaluated in the context of multivariate calibration models based on partial least-squares (PLS) regression. Replicate calibration and prediction data acquired over 6 months are used to study the robustness of PLS models with respect to time. The recommended protocol for the absorbance calculations is based on the collection of a large group of individual background spectra during the instrumental warm-up period. Seven procedures are tested for obtaining optimal backgrounds for use with either the calibration or prediction data sets. When the developed methodology is employed, standard errors of prediction are maintained in the range of 1.0 mM for spectra acquired up to 6 months after the collection of the calibration data. This level of performance compares favorably to daily internal cross-validation errors of 0.5–0.9 mM.     

Chemometrics: the issues of measurement and modelling

In this paper, two spectral data sets have been used to illustrate the importance of maintaining chemical information whilst generating predictive multivariate calibration models. The first data set is based on 26 duplicate UV/VIS spectra for four meal ions (Fe, Ni, Co, Cu) present at varying concentrations in aqueous solution. Spectra were collected across the range 180–800 nm at a resolution of 3.5 nm generating 211 data points for each sample. Calibration was carried out using multiple linear regression (MLR) and a K-matrix approach to demonstrate the advantages the latter method has in describing real spectral features. In addition, the limitation of MLR in accommodating noise and spectral overlap in the data is also illustrated. The second data set based on NIR spectroscopy, was generated using a four-level 2 factor Factorial design strategy and consisted of two additives present at a range of concentrations in an aqueous caustic system, with the spectra being collected over the range 10,000–3000 cm⁻¹. Whilst a conventional partial least squares (PLS) model was applied to the data, it was through the use of variable selection (VS) prior to PLS and the application of weighted ridge regression (WRR) techniques that the need to develop chemometric methodology which intuitively reflected chemical information has been demonstrated. The results will also illustrate how a poorly designed experimental design protocol and missing data can limit the performance of the calibration models generated. The aims of this paper are not to prescribe ideal calibration methodology but rather to demonstrate the relevance of selecting multivariate calibration methodology that relates more to the chem rather than just the metrics in chemometrics.     

Comparison of multivariate calibration models for glucose,urea, and lactate from near-infrared and Raman spectra

Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction were 0.24, 0.11, and 0.14 mmol L⁻¹ for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L⁻¹ for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute. This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from near-IR spectra. This is consistent with the lower standard errors of prediction.     

Sweeteners determination in table top formulations using FT-Raman spectrometry and chemometric analysis

A partial least squares (PLS) Fourier transform Raman spectrometry procedure based on the measurement of solid samples contained inside standard glass vials, has been developed for direct and reagent-free determination of sodium saccharin and sodium cyclamate in table top sweeteners. A classical 2² design for standards was used for calibration, but this system provides accuracy errors higher than 13% w/w for the analysis of samples containing glucose monohydrate. So, an extended model incorporating glucose monohydrate (2³ standards) was assayed for the determination of sodium saccharin and sodium cyclamate in all the samples. Mean centering spectra data pre-treatment has been employed to eliminate common spectral information and root mean square error of calibration (RMSEC) of 0.0064 and 0.0596 was obtained for sodium saccharin and sodium cyclamate, respectively. A mean accuracy error of the order of 1.1 and 1.9% w/w was achieved for sodium saccharin and sodium cyclamate, in the validation of the method using actual table top samples, being lower than those obtained using an external monoparametric calibration. FT-Raman provides a fast alternative to the chromatographic method for the determination of the sweeteners with a three times higher sampling throughput than that obtained in HPLC. On the other hand, FT-Raman offers an environmentally friendly methodology which eliminates the use of solvents. Furthermore, the stability of samples and standards into chromatographic standard glass vials allows their storage for future analysis thus avoiding completely the waste generation.     

Evaluation of capillary electrophoresis for determining the concentration of dissolved silica in geothermal brines

The determination of silica concentrations in geothermal brines is widely recognized as a difficult analytical task due to its complex chemical polymerization kinetics that occurs during sample collection and chemical analysis. Capillary electrophoresis (CE) has been evaluated as a new reliable analytical method to measure silica (as silicates) in geothermal brines. Synthetic and geothermal brine samples were used to evaluate CE methodology. A capillary electrophoresis instrument, Quanta 4000 (Waters-Millipore) coupled with a Waters 820 workstation was used to carry out the experimental work. The separation of silicates was completed in approximately 5.5 min using a conventional fused-silica capillary (75 microm i.d. x 375 microm o.d. x 60 cm total length). A hydrostatic injection (10 cm for 20 s at 25 degrees C) was employed for introducing the samples. The carrier electrolyte consisted of 10 mM sodium chromate, 3 mM tetradecyltrimethyl-ammonium hydroxide (TTAOH), 2 mM sodium carbonate, and 1 mM sodium hydroxide, adjusted to a pH 11.0 +/- 0.1. Silicates were determined using an indirect UV detection at a wavelength of 254 nm with a mercury lamp and with a negative power supply (-15 kV). A good reproducibility in the migration times (%R.S.D. approximately 1.6%) based on six non-consecutive injections of synthetic brine solutions was obtained. A linear response between silica concentration and corrected peak area was observed. Ordinary (OLR) and weighted (WLR) linear regression models were used for calculating silica concentrations in all samples using the corresponding fitted calibration curves. The analytical results of CE were finally compared with the most probable values of synthetic reference standards of silica using the Student's t-test. No significant differences were found between them at P = 0.01. Similarly, the atomic absorption spectrometry (AAS) results were also compared with the most probable concentrations of the same reference standards, finding significant differences at P = 0.01.     

Automated determination of glucose in soluble coffee using Prussian Blue-glucose oxidase-Nafion((R)) modified electrode

A highly selective, fast and stable biosensor for determination of glucose in soluble coffee has been developed. The biosensor electrode consist of a thin film of ferric hexacyanoferrate (Prussian Blue or PB) electrodeposited on the glassy carbon electrode (GCE) (to provide a catalytic surface for the detection of hydrogen peroxide) glucose oxidase immobilized on top of the electrode and a Nafion^® polymer layer. The stability of the PB film and the biosensor was evaluated by injecting standard-solution (50 μM H₂O₂ and 0.5 mM glucose) during 4 h in a flow-injection system with the electrodes polarized at −50 mV versus Ag/AgCl. The system is able to handle about 60 samples per hour and is very stable and suitable for industrial control. Determination of glucose in the range 2.5 and 15% (w/v) in phosphate buffer with precision (r.s.d. < 1.5%) has been achieved and is in agreement with the conventional procedures. Linear calibration in the range of 0.15 and 2.50 mM with detection limits of ca. 0.03 mM has been obtained. The morphology of the enzyme glucose oxidase on the modified electrode has been analyzed by scanning electron microscopy (SEM) measurements.     

径向基函数网络在近红外人体无创伤血糖浓度检测基础研究中的应用

在近红外无创伤血糖浓度检测的基础研究中，对于多组分的混合物的分析，常因光谱与样品浓度之间呈现非线性响应，使得基于线性模型的校正方法失效。本文讨论了非线性校正方法径向基函数神经网络( RBFN )的有效性，并与线性校正方法中的主成分分析和偏最小二乘法作了对比研究。验证实验所用样品为①葡萄糖水溶液②包含牛血红蛋白和白蛋白的葡萄糖水溶液，结果表明：在①实验中PLS模型和RBFN预测标准偏差分别为8．2、8．9；在②实验中分别为15．6、8．8。可见在样品组分增多时，RBFN算法较线性PLS方法建立的模型预测能力强。     

Infrared emission spectroscopy of liquid samples with multivariate calibration as a model for process analytical applications

Infrared emission spectroscopy and multivariate calibration are used to provide a method for the quantitative analysis of liquid samples. Differing forms of the data including second derivative and interferogram representation are used for prediction of sample composition, thickness and temperature. Comparisons are made with transmission measurements of the same samples. In some situations emission measurements may be the preferred method of analysis.     

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     

Support vector regression with digital band pass filtering for the quantitative analysis of near‐infrared spectra

This paper proposes a novel calibration technique based on combining support vector regression with a digital band pass (DBP) filter for the quantitative analysis of near‐infrared spectra. The efficacy of the proposed method is investigated and validated in the determination of glucose from near‐infrared spectra of a mixture composed of urea, triacetin and glucose. In this paper, the DBP filtering was implemented as a pre‐processing technique in the frequency domain as a Gaussian band pass filter and in the time domain as a Chebyshev filter. The grid‐search optimization method was used to optimize the filter parameters. The results demonstrate that utilization of the optimized DBP filters as a pre‐processing technique improved the performance of the predictive models. Copyright © 2013 John Wiley & Sons, Ltd.     

Analysis of glycolytic intermediates in Saccharomyces cerevisiae using anion exchange chromatography and electrospray ionization with tandem mass spectrometric detection

The purpose of this study is to selectively and quantitatively analyze several glycolytic intermediates in cells of Saccharomyces cerevisiae using high-performance anion exchange chromatography (HPAEC) coupled to electrospray ionization tandem mass spectrometry for the analysis. A sodium hydroxide gradient is used to separate the glycolytic compounds and after the column sodium hydroxide is reduced by proton exchange with a membrane device prior to introduction to the mass spectrometer. The detection limits for 10 μl samples are down to the 0.4–5 pmol range. This corresponds for the intracellular metabolites to a range of 2–20 nmol per gram biomass dry weight (DW). Standard addition did reveal some influence of the sample matrix on the measured concentrations. Separation and analysis is hardly affected by the high sulfate and phosphate concentrations (1 mM) in the fermentation medium and by the intracellular matrix. Validation of the glucose-6-phosphosphate LC–MS–MS analysis results with enzymatic analysis showed an excellent agreement between the two methods. The suitability of the method was clearly shown by analyzing a series of steady state S. cerevisiae samples from a carbon limited aerobic chemostat culture.     

High-pass filters for spectral background suppression in airborne passive Fourier transform infrared spectrometry

High-pass (HP) digital filtering and second-derivative (SD) filtering are evaluated as methods of removing background contributions from spectra collected by passive Fourier transform infrared spectrometry. In measurements performed with a downward-looking spectrometer mounted on an aircraft platform, the effects of non-constant background radiance from the ground make it challenging to build automated classifiers for detecting an analyte of interest. Applying HP digital filtering to the spectra to remove background contributions is evaluated as a strategy to help improve classifier performance. This methodology is tested by building classifiers for detecting heated ethanol plumes released from a portable emission stack. The classifiers are trained with data collected on the ground with the spectrometer viewing the plumes against a synthetic backdrop designed to simulate a terrestrial radiance source. The resulting classifiers are tested with data collected by the same spectrometer mounted on an aircraft flying over the emission stack. Support vector machines are employed as a classification algorithm with HP filtered spectra used as input patterns. Butterworth filters are used to implement HP digital filtering, while Savitzky-Golay filters are used to implement SD filtering. Significant improvement in classification performance is achieved by use of the HP filters. Because of variation in backgrounds between the training and prediction data, the best classifier obtained with unfiltered spectra is unable to detect ethanol in 37% of the test cases. HP filtering of spectra with an optimized Butterworth filter (order 8, cutoff frequency 0.060) improves the prediction results, resulting in no missed ethanol detections and false positive rates of less than 0.4%.     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

NIR analysis of pharmaceutical samples without reference data: improving the calibration

Using an appropriate set of samples to construct the calibration set is crucial with a view to ensuring accurate multivariate calibration of NIR spectroscopic data. In this work, we developed and optimized a new methodology for incorporating physical variability in pharmaceutical production based on the NIR spectrum for the process. Such a spectrum contains the spectral changes caused by each treatment applied to the component mixture during the production process. The proposed methodology involves adding a set of process spectra (viz. difference spectra between those for production tablets and a laboratory mixture of identical nominal composition) to the set of laboratory samples, which span the wanted concentration range, in order to construct a calibration set incorporating all physical changes undergone by the samples in each step of the production process. The best calibration model among those tested was selected by establishing the influence of spectral pretreatments used to obtain the process spectrum and construct the calibration models, and also by determining the multiplying factor m to be applied to the process spectra in order to ensure incorporation of all variability sources into the calibration model. The specific samples to be included in the calibration set were selected by principal component analysis (PCA). To this end, the new methodology for constructing calibration sets for determining the Active Principle Ingredients (API) and excipients was applied to Irbesartan tablets and validated by application to the API and excipients of paracetamol tablets. The proposed methodology provides simple, robust calibration models for determining the different components of a pharmaceutical formulation.     

近红外光谱技术用于花生油中棕榈油含量的测定

本文采用近红外光谱技术采集样品的近红外光谱数据,光谱经一阶求导后,采用偏最小二乘法(PLS)建立花生油中棕榈油含量的定标模型,并用交互验证法对模型进行了验证。模型相关系数为0.9963,校正均方根(RMSEC)为0.937。该模型应用于实际样品的检测,结果令人满意。     

Control production of polyester resins by NIR spectroscopy

The control of the esterification reaction for production of polyester saturated resins is followed usually by determination of the acid value (AV) and hydroxyl value (OHV).These parameters are determined by titrimetry, but these methods are slow, intensity working and produce waste. In this paper an alternative methodology is proposed, based in the construction of multivariate models on NIR spectroscopic data and different models are constructed in order to apply to different steps of the production process. The ensuing methodology provides models of good predictive ability and constitute an advantageous alternative to existing titrimetric reference methods as regards expeditiousness and environmentally compatible. The multivariate calibration models established were also used with a different instrument; to this end, the spectra recorded with the original equipment were subjected to Piecewise Direct Standardization (PDS) in order to make them equivalent to those provided by the new equipment. Also, PLS calibration was reproduced by using the same samples, spectral treatment, wavenumber range and number of factors as in the original model, and the AV and OHV results thus obtained were similarly good.     

Control production of polyester resins by NIR spectroscopy

The control of the esterification reaction for production of polyester saturated resins is followed usually by determination of the acid value (AV) and hydroxyl value (OHV).These parameters are determined by titrimetry, but these methods are slow, intensity working and produce waste. In this paper an alternative methodology is proposed, based in the construction of multivariate models on NIR spectroscopic data and different models are constructed in order to apply to different steps of the production process. The ensuing methodology provides models of good predictive ability and constitute an advantageous alternative to existing titrimetric reference methods as regards expeditiousness and environmentally compatible. The multivariate calibration models established were also used with a different instrument; to this end, the spectra recorded with the original equipment were subjected to Piecewise Direct Standardization (PDS) in order to make them equivalent to those provided by the new equipment. Also, PLS calibration was reproduced by using the same samples, spectral treatment, wavenumber range and number of factors as in the original model, and the AV and OHV results thus obtained were similarly good.     

Development of a solid-phase microextraction-gas chromatography-tandem mass spectrometry method for the analysis of chlorinated toluenes in environmental waters

In the present work, a simple and fast methodology has been developed for the analysis of chlorotoluenes in water samples using solid-phase microextraction (SPME) coupled to gas chromatography-tandem mass spectrometry (GC/MS/MS). A multifactorial experimental design strategy was used for studying the influence on extraction yield of factors such as fiber coating, extraction mode, temperature, and addition of sodium chloride. Quantitative recoveries (>/=84%) and satisfactory precision (relative standard deviations (RSD)相似文献