An enhanced algorithm for linear multivariate calibration

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing

Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements. Graphical abstract.

Ramanomics: New Omics Disciplines Using Micro Raman Spectrometry with Biomolecular Component Analysis for Molecular Profiling of Biological Structures

Modern instrumentation for Raman microspectroscopy and current techniques in analysis of spectral data provide new opportunities to study molecular interactions and dynamics at subcellular levels in biological systems. Implementation of biomolecular component analysis (BCA) to microRaman spectrometry provides basis for the emergence of Ramanomics, a new biosensing discipline with unprecedented capabilities to measure concentrations of distinct biomolecular groups in live cells and organelles. Here we review the combined use of microRaman-BCA techniques to probe absolute concentrations of proteins, DNA, RNA and lipids in single organelles of live cells. Assessing biomolecular concentration profiles of organelles at the single cell level provides a physiologically relevant set of biomarkers for cellular heterogeneity. In addition, changes to an organelle's biomolecular concentration profile during a cellular transformation, whether natural, drug induced or disease manifested, can provide molecular insight into the nature of the cellular process.

Raman spectroscopic analysis of Lactobacillus rhamnosus GG in response to dehydration reveals DNA conformation changes

Dehydration of bacterial cells elicits cellular stress responses in bacteria. Microencapsulation has been used to protect cells against the environmental stress. In this study, Confocal Raman Spectroscopy was used to examine DNA changes in the chemical composition of non-encapsulated and microencapsulated Lactobacillus rhamnosus GG and the reversibility of these changes upon freeze drying and rehydration. The viability of cells upon freeze drying was also enumerated using culture methods and membrane integrity was measured using BacLight Live/Dead staining. Raman analyses show changes in the spectral features associated with various biochemical compounds, which are interpreted as the result of detrimental freeze drying effects on the bacterial cells. Specifically, analyses based on Principal Components Analysis (PCA) of Raman spectra, confirm that microencapsulation protects cells from environmental stress. The results also reveal a B- to A-like DNA conformation change in dormant cells that provided insights into the extent of reversibility of this transition upon rehydration. The extent of this reversibility is less in non-encapsulated than in microencapsulated cells. These findings indicate the potential application of Raman spectroscopy in rapid sensing of microbial dehydration stress responses.

Effect of Drying Methods on Protein and DNA Conformation Changes in Lactobacillus rhamnosus GG Cells by Fourier Transform Infrared Spectroscopy

Microencapsulation protects cells against environmental stress encountered during the production of probiotics, which are used as live microbial food ingredients. Freeze-drying and spray-drying are used in the preparation of powdered microencapsulated probiotics. This study examines the ability of Fourier transform infrared (FTIR) spectroscopy to detect differences in cells exposed to freeze-drying and spray-drying of encapsulated Lactobacillus rhamnosus GG cells. The FTIR analysis clearly demonstrated there were more significant molecular changes in lipid, fatty acid content, protein, and DNA conformation of nonencapsulated compared to encapsulated bacterial cells. The technique was also able to differentiate between spray-dried and freeze-dried cells. The results also revealed the extent of protection from a protein-carbohydrate-based encapsulant matrix on the cells depending on the type drying process. The extent of this protection to the dehydration stress was shown to be less in spray-dried cells than in freeze-dried cells. This suggests that FTIR could be used as a rapid, noninvasive, and real-time measurement technique to detect detrimental drying effects on cells.

Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy

As adulteration of foodstuffs with Sudan dye, especially paprika- and chilli-containing products, has been reported with some frequency, this issue has become one focal point for addressing food safety. FTIR spectroscopy has been used extensively as an analytical method for quality control and safety determination for food products. Thus, the use of FTIR spectroscopy for rapid determination of Sudan dye in paprika powder was investigated in this study. A net analyte signal (NAS)-based methodology, named HLA/GO (hybrid linear analysis in the literature), was applied to FTIR spectral data to predict Sudan dye concentration. The calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results had a high determination coefficient (R²) of 0.98 and low root mean square error (RMSE) of 0.026% for the calibration set, and an R² of 0.97 and RMSE of 0.05% for the validation set. The model was further validated using a second validation set and through the figures of merit, such as sensitivity, selectivity, and limits of detection and quantification. The proposed technique of FTIR combined with HLA/GO is rapid, simple and low cost, making this approach advantageous when compared with the main alternative methods based on liquid chromatography (LC) techniques.

Determination of hydrogen sulfide and hydrogen peroxide in complex samples of milk and urine by spectroscopic standard addition data and chemometrics methods

Spectroscopic data of standard additions were collected and used to calculate the spectrum of the unknown interferent(s) based on the net analyte signal.

Surgical Guidance via Multiplexed Molecular Imaging of Fresh Tissues Labeled with SERS-Coded Nanoparticles

The imaging of dysregulated cell-surface receptors (or biomarkers) is a potential means of identifying the presence of cancer with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer biomarkers. Recently, surface-enhanced Raman-scattering (SERS) nanoparticles (NPs) have attracted wide interest due to their potential for sensitive and multiplexed biomarker detection. In this review, we focus on the most recent advances in tumor imaging using SERS-coded NPs. A brief introduction of the structure and optical properties of SERS NPs is provided, followed by a detailed discussion of key imaging issues such as the administration of NPs in tissue (topical versus systemic), the optical configuration and imaging approach of Raman imaging systems, spectral demultiplexing methods for quantifying NP concentrations, and the disambiguation of specific vs. nonspecific sources of contrast through ratiometric imaging of targeted and untargeted (control) NP pairs. Finally, future challenges and directions are briefly outlined.

Co-localized confocal Raman spectroscopy and optical coherence tomography (CRS-OCT) for depth-resolved analyte detection in tissue

We report the development of a combined confocal Raman spectroscopy (CRS) and optical coherence tomography (OCT) instrument (CRS-OCT) capable of measuring analytes in targeted biological tissues with sub-100-micron spatial resolution. The OCT subsystem was used to measure depth-resolved tissue morphology and guide the acquisition of chemically-specific Raman spectra. To demonstrate its utility, the instrument was used to accurately measure depth-resolved, physiologically-relevant concentrations of Tenofovir, a microbicide drug used to prevent the sexual transmission of HIV, in ex vivo tissue samples.

A novel algorithm for linear multivariate calibration based on the mixed model of samples

We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles.

Joint derivation method for determining optical properties based on steady-state spatially resolved diffuse reflectance measurement at small source-detector separations and large reduced albedo range: theory and simulation

Accurate determination of the optical properties (the absorption coefficient μ(a) and the reduced scattering coefficient μ(s) (')) of tissues is very important in a variety of diagnostic and therapeutic procedures. Optical diffusion theory is frequently used as the forward model for describing the photon transfer in media with large reduced albedos (a(')) and in large source-detector separations (SDS). Several other methods (PN approximation, hybrid diffusion-P3 approximation) have also been published that describe photon transfer in media with low a(') or small SDSs. We studied the theoretical models for the steady-state spatially resolved diffuse reflectance measurement to accurately determine μ(a) and μ(s) (') at large a(') range but small SDSs. Instead of using a single model, a joint derivation method is proposed. The developed method uses one of the best aforementioned theoretical methods separately in five ranges of a(') determined from several forward models. In the region of small SDSs (the range between 0.4 and 8 mm) and large a(') range (between 0.5 and 0.99), the best theoretical derivation model was determined. The results indicate that the joint derivation method can improve the derivation accuracy and that a(') range can be determined by the steady-state spatially resolved diffuse reflectance measurement.

Rapid and accurate determination of tissue optical properties using least-squares support vector machines

Diffuse reflectance spectroscopy (DRS) has been extensively applied for the characterization of biological tissue, especially for dysplasia and cancer detection, by determination of the tissue optical properties. A major challenge in performing routine clinical diagnosis lies in the extraction of the relevant parameters, especially at high absorption levels typically observed in cancerous tissue. Here, we present a new least-squares support vector machine (LS-SVM) based regression algorithm for rapid and accurate determination of the absorption and scattering properties. Using physical tissue models, we demonstrate that the proposed method can be implemented more than two orders of magnitude faster than the state-of-the-art approaches while providing better prediction accuracy. Our results show that the proposed regression method has great potential for clinical applications including in tissue scanners for cancer margin assessment, where rapid quantification of optical properties is critical to the performance.

Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose

Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as partial least-squares (PLS). Using transcutaneous blood glucose detection as an example, we demonstrate that application of SVM enables a significant improvement (at least 30%) in cross-validation accuracy over PLS when measurements from multiple human volunteers are employed in the calibration set. Furthermore, using physical tissue models with randomized analyte concentrations and varying turbidities, we show that the fluctuations in turbidity alone causes curved effects which can only be adequately modeled using nonlinear regression techniques. The enhanced levels of accuracy obtained with the SVM based calibration models opens up avenues for prospective prediction in humans and thus for clinical translation of the technology.

Accurate spectroscopic calibration for noninvasive glucose monitoring by modeling the physiological glucose dynamics

The physiological lag between blood and interstitial fluid (ISF) glucose is a major challenge for noninvasive glucose concentration measurements. This is a particular problem for spectroscopic techniques, which predominantly probe ISF glucose, creating inconsistencies in calibration, where blood glucose measurements are used as a reference. To overcome this problem, we present a dynamic concentration correction (DCC) scheme, based on the mass transfer of glucose between blood and ISF, to ensure consistency with the spectral measurements. The proposed formalism allows the transformation of glucose in the concentration domain, ensuring consistency with the acquired spectra in the calibration model. Taking Raman spectroscopy as a specific example, we demonstrate that the predicted glucose concentrations using the DCC-based calibration model closely match the measured glucose concentrations, while those generated with the conventional calibration methods show significantly larger deviations from the measured values. In addition, we provide an analytical formula for a previously unidentified source of limiting uncertainty arising in spectroscopic glucose monitoring from a lack of knowledge of glucose kinetics in prediction samples. A study with human volunteers undergoing glucose tolerance tests indicates that this lag uncertainty, which is comparable in magnitude to the uncertainty arising from noise and nonorthogonality in the spectral data set, can be reduced substantially by employing the DCC scheme in spectroscopic calibration.

A novel storage method for near infrared spectroscopy chemometric models

Chemometric Modeling Markup Language (CMML) is developed by us for containing chemometrics models within one document through converting binary data into strings by base64 encode/decode algorithms to solve the interoperability issue in sharing chemometrics models. It provides a base functionality for storage of sampling, variable selection, pretreating, outlier and modeling parameters and data. With the help of base64 algorithm, the usability of CMML is in equilibrium with size by transforming the binary data into base64 encoded string. Due to the advantages of Extensible Markup Language (XML), models stored in CMML can be easily reused in various other software and programming languages as long as the programming language has XML parsing library. One can also use the XML Path Language (XPath) query language to select desired data from the CMML file effectively. The application of this language in near infrared spectroscopy model storage is implemented as a class in C++ language and available as open source software (http://code.google.com/p/cmml), and the implementations in other languages, such as MATLAB and R are in progress.

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.

Net analyte signal standard addition method (NASSAM) as a novel spectrofluorimetric and spectrophotometric technique for simultaneous determination, application to assay of melatonin and pyridoxine

In this work a new modification of the standard addition method called "net analyte signal standard addition method (NASSAM)" is presented for the simultaneous spectrofluorimetric and spectrophotometric analysis. The proposed method combines the advantages of standard addition method with those of net analyte signal concept. The method can be applied for the determination of analyte in the presence of known interferents. The accuracy of the predictions against H-point standard addition method is not dependent on the shape of the analyte and interferent spectra. The method was successfully applied to simultaneous spectrofluorimetric and spectrophotometric determination of pyridoxine (PY) and melatonin (MT) in synthetic mixtures and in a pharmaceutical formulation.

Net analyte signal standard addition method for the simultaneous determination of cadmium and nickel

A novel net analyte signal standard addition method (NASSAM) is presented for the simultaneous determination of Cd2+ and Ni2+ in their mixture by differential pulse polarography. The method combines the advantages of the standard addition method with the net analyte signal concept, which enables the extraction of information concerning a certain analyte from voltammograms of multi-component mixtures. This method has some advantages, such as: the use of a full voltammogram, realization in a single step, therefore it does not require calibration and prediction steps and only a few measurements are required for the determination. The simultaneous determination of Cd2+ and Ni2+ was performed in Britton-Robinson buffer (pH 2.87) and 0.40 M potassium thiocyanate solution.

Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques

Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-vis spectra. The UV-vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10-80 mg L(-1) for vitamin B12 and methylcobalamin and 20-130 mg L(-1) for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L(-1) for vitamin B12 with PLS1, 1.33 mg L(-1) for methylcobalamin with OSC/PLS and 3.24 mg L(-1) for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.

Nitrogen cycling in natural waters using in situ, reagentless UV spectrophotometry with simultaneous determination of nitrate and nitrite

Reliable, high temporal, and spatial resolution data is essential for enhancing our understanding of aquatic nitrogen biogeochemical cycling. This paper describes a novel UV spectrophotometric sensor (ProPS, TriOS GmbH, Oldenburg, Germany) for the real time, in situ, high resolution simultaneous mapping of nitrate/nitrite (linearity 0.01 - 6 mg N L(-1), RSD's NO3-N 4-10%, NO2-N 7-14%) in fresh and estuarine waters. Good agreement (t test at p = 0.05) was found with MOOS-1 certified reference material and with reference segmented flow analysis data. River Taw deployments identified a diurnal cycle for NO3-N (0.22-0.63 mg L(-1), RSD 3.9%) and for NO2-N (0.01-0.28 mg L(-1), RSD 12.4%) with the photo-oxidation of dissolved organic nitrogen a source of diurnal nitrate/nitrite, and a large cyclical amplitude (30-62% of mean nitrate/nitrite). In situ Tamar Estuary nitrate/nitrite concentrations, mapped through the salinity gradient, were strongly correlated with suspended particulate material and inversely correlated with dissolved oxygen and pH, indicating midestuarine, bacterially mediated nitrification/denitrification, with the raised estuarine nitrite also significantly correlated with particulate organic nitrogen. Such previously unquantified inputs have important implications for N loadings calculated from coarse scale sampling and laboratory analysis, pollution assessment, and our understanding of the biological rhythms of aquatic organisms.

Spectral simulation methodology for calibration transfer of near-infrared spectra

A spectrum simulation method is described for use in the development and transfer of multivariate calibration models from near-infrared spectra. By use of previously measured molar absorptivities and solvent displacement factors, synthetic calibration spectra are computed using only background spectra collected with the spectrometer for which a calibration model is desired. The resulting synthetic calibration set is used with partial least squares regression to form the calibration model. This methodology is demonstrated for use in the analysis of physiological levels of glucose (1-30 mM) in an aqueous matrix containing variable levels of alanine, ascorbate, lactate, urea, and triacetin. Experimentally measured data from two different Fourier transform spectrometers with different noise levels and stabilities are used to evaluate the simulation method. With the more stable instrument (A), well-performing calibration models are obtained, producing a standard error of prediction (SEP) of 0.70 mM. With the less stable instrument (B), the calibration based solely on synthetic spectra is less successful, producing an SEP value of 1.58 mM. For cases in which the synthetic spectra do not describe enough spectral variance, an augmentation protocol is evaluated in which the synthetic calibration spectra are augmented with the spectra of a small number of experimentally measured calibration samples. For instruments A and B, respectively, augmentation with measured spectra of nine samples lowers the SEP values to 0.64 and 0.85 mM.

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.

Measurements and quality assessments of near-infrared plasma glucose spectra in the combination band region using a scanning filter spectrometer

Near-infrared measurements of glucose in human plasma are performed using a custom, rapid, high-throughput filter-based spectrometer covering a spectral range between 2080 and 2315 nm. Quality of the measured glucose signals is quantified through the use of two figures of merit: selectivity and limit of detection. Selectivity measures the uniqueness of the glucose spectrum from among the interfering spectra. Limit of detection measures the smallest glucose concentration change detectable. The proposed system, which includes the spectroscopic hardware and a spectral preprocessing algorithm, is shown to produce a selectivity value of 0.57, with zero being nonselective and one being fully selective, and a limit of detection value of 2.2 mM. Prediction of an independent dataset is also performed using net analyte signal-based and partial least-squares multivariate calibration techniques, which produce standard error of prediction values of 1.14 and 1.45 mM, respectively.

Noninvasive and minimally invasive methods for transdermal glucose monitoring

Noninvasive and minimally invasive techniques for monitoring glucose via the skin are reviewed. These approaches rely either on the interaction of electromagnetic radiation with the tissue or on the extraction of fluid across the barrier. The structure and physiology of the skin make the technical realization of transdermal glucose monitoring a difficult challenge. The techniques involving transdermal fluid extraction circumvent and/or compromise the barrier function of skin's outermost and least permeable layer, the stratum corneum, by the application of physical energy. While sonophoresis and microporation methods, for example, are in relatively early-stage development, a device using reverse iontophoresis [the GlucoWatch Biographer (Cygnus, Inc., Redwood City, CA)] is already commercially available. Optical techniques to monitor glucose are truly noninvasive. The tissue is irradiated, the absorbed or scattered radiation is analyzed, and the information is processed, to provide a measure proportional to the concentration of glucose in the dermal tissue. These techniques include near-infrared and Raman spectroscopy, polarimetry, light scattering, and photoacoustic spectroscopy. By contrast, impedance spectroscopy measures changes in the dielectric properties of the tissue induced by blood glucose variation. Large-scale studies in support of efficacy of these methodologies are as yet unavailable. At present, therefore, transdermal fluid extraction technologies are offering greater promise in terms of practical and realizable devices for patient use. The truly noninvasive allure of the optical approach assures continued and intense research activity--for the moment, however, an affordable, efficient, and portable system is not on the immediate horizon.

Non-invasive glucose measurement technologies: an update from 1999 to the dawn of the new millennium

There are three main issues in non-invasive (NI) glucose measurements: namely, specificity, compartmentalization of glucose values, and calibration. There has been progress in the use of near-infrared and mid-infrared spectroscopy. Recently new glucose measurement methods have been developed, exploiting the effect of glucose on erythrocyte scattering, new photoacoustic phenomenon, optical coherence tomography, thermo-optical studies on human skin, Raman spectroscopy studies, fluorescence measurements, and use of photonic crystals. In addition to optical methods, in vivo electrical impedance results have been reported. Some of these methods measure intrinsic properties of glucose; others deal with its effect on tissue or blood properties. Recent studies on skin from individuals with diabetes and its response to stimuli, skin thermo-optical response, peripheral blood flow, and red blood cell rheology in diabetes shed new light on physical and physiological changes resulting from the disease that can affect NI glucose measurements. There have been advances in understanding compartmentalization of glucose values by targeting certain regions of human tissue. Calibration of NI measurements and devices is still an open question. More studies are needed to understand the specific glucose signals and signals that are due to the effect of glucose on blood and tissue properties. These studies should be performed under normal physiological conditions and in the presence of other co-morbidities.

Spectrophotometric determination of sorbic and benzoic acids in fruit juices by a net analyte signal-based method with selection of the wavelength range to avoid non-modelled interferences

Sorbic (SOR) and benzoic (BEN) acids were determined in fruit juice samples by using a net analyte signal-based methodology named HLA/GO (an hybrid linear analysis presented by Goicoechea and Olivieri) applied to spectroscopic signals. The calibration set was built with several fruit juices in order to take into account the natural variability and concentrations of both analytes covering the range usually present in commercial samples. Relative errors of prediction (REP %) of 3.6 and 5.2% were calculated for SOR and BEN respectively. Several figures of merit were calculated-sensitivity, selectivity, analytical sensitivity, and limit of detection. The method is quantitative, with reasonably good recoveries and excellent precision (less than 1%). Wavelength selection was applied, based on the concept of net analyte signal regression, and it allowed us to improve the method performance in samples containing non-modelled interferences, e.g. fruit juices different to those used to build the calibration model.

Raman spectroscopy-based creatinine measurement in urine samples from a multipatient population

Spectroscopic methods of urinalysis offer several advantages over chemical methods, including less sample contact and higher information content. In particular, urine creatinine has been the subject of several spectroscopic studies. We report the first use of Raman spectroscopy to measure creatinine concentrations in unaltered urine samples from a multipatient population. Using near-infrared excitation and a hybrid linear analysis calibration method, a root mean squared error of cross-validation (RMSECV) of 4.9 mg/dL was obtained. The error in the reference chemical method was 1.1 mg/dL. This result shows that the Raman spectroscopy can measure creatinine at clinical levels even in the presence of patient-to-patient variations. Because most assays in urine require creatinine concentration in order to correct for fluctuations in water content, measurement of creatinine is the first step towards more extensive Raman-based urinalysis.