Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Utilizing pulse dynamics for non-invasive Raman spectroscopy of blood analytes

Non-invasive measurement methods offer great benefits in the field of medical diagnostics with molecular-specific techniques such as Raman spectroscopy which is increasingly being used for quantitative measurements of tissue biochemistry in vivo. However, some important challenges still remain for label-free optical spectroscopy to be incorporated into the clinical laboratory for routine testing. In particular, non-analyte-specific variations in tissue properties introduce significant variability of the spectra, thereby preventing reliable calibration. For measurements of blood analytes such as glucose, we propose to decrease the interference from individual tissue characteristics by exploiting the known dynamics of the blood-tissue matrix. We reason that by leveraging the natural blood pulse rhythm, the signals from the blood analytes can be enhanced while those from the static components can be effectively suppressed. Here, time-resolved measurements with subsequent pulse frequency estimation and phase-sensitive detection are proposed to recover the Raman spectra correlated with the dynamic changes at blood-pulse frequency. Pilot in vivo study results are presented to establish the benefits as well as outline the challenges of the proposed method in terms of instrumentation and signal processing.

Very Selective Detection of Low Physiopathological Glucose Levels by Spontaneous Raman Spectroscopy with Univariate Data Analysis

After decades of research on non-invasive glucose monitoring, invasive devices based on finger blood sampling are still the predominant reference for diabetic patients for accurately measuring blood glucose levels. Meanwhile, research continues improving point-of-care technology toward the development of painless and more accurate devices. Raman spectroscopy is well-known as a potentially valuable and painless approach for measuring glucose levels. However, previous Raman studies deal with glucose concentrations that are still order of magnitudes away with respect to human tissues’ physiological concentrations, or they propose enhancement methodologies either invasive or much complex to assure sufficient sensitivity in the physiological range. Instead, this study proposes an alternative non-enhanced Raman spectroscopy approach sensitive to glucose concentrations from 1 to 5 mmol/l, which correspond to the lowest physiopathological glucose level in human blood. Our findings suggest a very selective detection of glucose with respect to other typical metabolites, usually interfering with Raman spectroscopy’s glucose detection. We validate the proposed univariate sensing methodology on glucose solutions mixed with lactate and urea, the two most common molecules found in human serum with concentrations similar to glucose and similar features in the Raman spectra. Our findings clearly illustrate that reliable detection of glucose by Raman spectroscopy is feasible by exploiting the shifted peak at 1125 ± 10 cm–1 within physiopathological ranges.

Tissue Phantoms for Biomedical Applications in Raman Spectroscopy: A Review

Raman spectroscopy is a group of analytical techniques, currently applied in several research fields, including clinical diagnostics. Tissue-mimicking optical phantoms have been established as an essential intermediate stage for medical applications with their employment from spectroscopic techniques to be constantly growing. This review outlines the types of tissue phantoms currently employed in different biomedical applications of Raman spectroscopy, focusing on their composition and optical properties. It is therefore an attempt to present an informed range of options for potential use to the researchers.

Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy

In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient’s diabetic conditions. Each cluster is trained to build the unique error prediction model using an adaptive boosting (AdaBoost) algorithm. Later, these error prediction models undergo personalized calibration based on the patient’s characteristics. Once, the errors in predicted non-invasive values are within the acceptable error range, the device gets personalized for a patient to measure the blood glucose non-invasively. We verify PGMS on two different datasets. Performance analysis shows that the mean absolute relative difference (MARD) is reduced exceptionally to 7.3% and 7.1% for predicted values as compared to 25.4% and 18.4% for measured non-invasive glucose values. The Clarke error grid analysis (CEGA) plot for non-invasive predicted values shows 97% data in Zone A and 3% data in Zone B for dataset 1. Moreover, for dataset 2 results echoed with 98% and 2% in Zones A and B, respectively.

NIR photoacoustic spectroscopy for non-invasive glucose measurement

The use of near infra red (NIR) photoacoustic spectroscopy (PAS) for continuous non-invasive glucose measurement is outlined in the paper. A photoacoustic (PA) measurement apparatus was constructed and PA measurements were made on glucose solutions at multiple NIR excitation wavelengths. A variety of time and frequency domain features, including amplitude and area based features, were extracted from the PA measurements. These features were observed to be proportional to the glucose concentration of the sample. PA measurements from samples of whole blood at different glucose concentrations showed similar results. Subsequently, in vivo PA measurements made on a cohort of 30 volunteers were calibrated using a quadratic fit, and the results were compared to reference glucose concentrations made using a regular blood glucose meter. A comparison of 196 measurement pairs of predicted and reference glucose concentrations using a Clarke Error Grid gave a point distribution of 87.24% and 12.76% over zones A and B of the grid, with no measurement pairs falling in unacceptable zones C-E of the error grid. The predicted measurements had a mean absolute difference (MAD) of 12.57 ± 13.90 mg/dl and a mean absolute relative difference (MARD) of 9.61% ± 10.55%. This is an improvement over previous results obtained using PAS and other non-invasive techniques, validating the potential of PAS for continuous noninvasive glucose monitoring.

Raman Spectroscopy of Blood and Blood Components

Blood is a bodily fluid that is vital for a number of life functions in animals. To a first approximation, blood is a mildly alkaline aqueous fluid (plasma) in which a large number of free-floating red cells (erythrocytes), white cells (leucocytes), and platelets are suspended. The primary function of blood is to transport oxygen from the lungs to all the cells of the body and move carbon dioxide in the return direction after it is produced by the cells' metabolism. Blood also carries nutrients to the cells and brings waste products to the liver and kidneys. Measured levels of oxygen, nutrients, waste, and electrolytes in blood are often used for clinical assessment of human health. Raman spectroscopy is a non-destructive analytical technique that uses the inelastic scattering of light to provide information on chemical composition, and hence has a potential role in this clinical assessment process. Raman spectroscopic probing of blood components and of whole blood has been on-going for more than four decades and has proven useful in applications ranging from the understanding of hemoglobin oxygenation, to the discrimination of cancerous cells from healthy lymphocytes, and the forensic investigation of crime scenes. In this paper, we review the literature in the field, collate the published Raman spectroscopy studies of erythrocytes, leucocytes, platelets, plasma, and whole blood, and attempt to draw general conclusions on the state of the field.

Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy

The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations.

Modelling, verification, and calibration of a photoacoustics based continuous non-invasive blood glucose monitoring system

This paper examines the use of photoacoustic spectroscopy (PAS) at an excitation wavelength of 905 nm for making continuous non-invasive blood glucose measurements. The theoretical background of the measurement technique is verified through simulation. An apparatus is fabricated for performing photoacoustic measurements in vitro on glucose solutions and in vivo on human subjects. The amplitude of the photoacoustic signals measured from glucose solutions is observed to increase with the solution concentration, while photoacoustic amplitude obtained from in vivo measurements follows the blood glucose concentration of the subjects, indicating a direct proportionality between the two quantities. A linear calibration method is applied separately on measurements obtained from each individual in order to estimate the blood glucose concentration. The estimated glucose values are compared to reference glucose concentrations measured using a standard glucose meter. A plot of 196 measurement pairs taken over 30 normal subjects on a Clarke error grid gives a point distribution of 82.65% and 17.35% over zones A and B of the grid with a mean absolute relative deviation (MARD) of 11.78% and a mean absolute difference (MAD) of 15.27 mg/dl (0.85 mmol/l). The results obtained are better than or comparable to those obtained using photoacoustic spectroscopy based methods or other non-invasive measurement techniques available. The accuracy levels obtained are also comparable to commercially available continuous glucose monitoring systems.

Ex vivo optical measurements of glucose diffusion kinetics in native and diabetic mouse skin

The aim of this study was to estimate the glucose diffusion coefficients ex vivo in skin of mice with diabetes induced in vivo by alloxan in comparison to non-diabetic mice. The temporal dependences of collimated transmittance of tissue samples immersed in glucose solutions were measured in the VIS-NIR spectral range to quantify the glucose diffusion/permeability coefficients and optical clearing efficiency of mouse skin. The average thickness of intact healthy and diabetic skin was 0.023 ± 0.006 cm and 0.019 ± 0.005 cm, respectively. Considerable differences in optical and kinetic properties of diabetic and non-diabetic skin were found: clearing efficiency was 1.5-fold better and glucose diffusivity was 2-fold slower for diabetic skin. Experimental Setup for measuring collimated transmittance spectra of mouse skin samples.

Emerging trends in optical sensing of glycemic markers for diabetes monitoring

In the past decade, considerable attention has been focused on the measurement of glycemic markers, such as glycated hemoglobin and glycated albumin, that provide retrospective indices of average glucose levels in the bloodstream. While these biomarkers have been regularly used to monitor long-term glucose control in established diabetics, they have also gained traction in diabetic screening. Detection of such glycemic markers is challenging, especially in a point-of-care setting, due to the stringent requirements for sensitivity and robustness. A number of non-separation based measurement strategies were recently proposed, including photonic tools that are well suited to reagent-free marker quantitation. Here, we critically review these methods while focusing on vibrational spectroscopic methods, which offer highly specific molecular fingerprinting capability. We examine the underlying principles and the utility of these approaches as reagentless assays capable of multiplexed detection of glycemic markers and also the challenges in their eventual use in the clinic.

Anatomy of noise in quantitative biological Raman spectroscopy

Raman spectroscopy is a fundamental form of molecular spectroscopy that is widely used to investigate structures and properties of molecules using their vibrational transitions. It relies on inelastic scattering of monochromatic laser light irradiating the specimen. After appropriate filtering the scattered light is dispersed onto a detector to determine the shift from the excitation wavelength, which appears in the form of characteristic spectral patterns. The technique can investigate biological samples and provide real-time diagnosis of diseases. However, despite its intrinsic advantages of specificity and minimal perturbation, the Raman scattered light is typically very weak and limits applications of Raman spectroscopy due to measurement (im)precision, driven by inherent noise in the acquired spectra. In this article, we review the principal noise sources that impact quantitative biological Raman spectroscopy. Further, we discuss how such noise effects can be reduced by innovative changes in the constructed Raman system and appropriate signal processing methods.

Continuous noninvasive monitoring of changes in human skin optical properties during oral intake of different sugars with optical coherence tomography

The objective of this study was to evaluate the effects of blood glucose concentration (BGC) on in vivo human skin optical properties after oral intake of different sugars. In vivo optical properties of human skin were measured with a spectral domain optical coherence tomography (SD-OCT). Experimental results show that increase of BGC causes a decrease in the skin attenuation coefficient. And the maximum decrements in mean attenuation coefficient of skin tissue after drinking glucose, sucrose and fructose solution are 47.0%, 36.4% and 16.5% compared with that after drinking water, respectively (p < 0.05). The results also show that blood glucose levels of the forearm skin tissue are delayed compared with finger-stick blood glucose, and there are significant differences in the time delays after oral intake of different sugars. The time delay between mean attenuation coefficient and BGC after drinking glucose solution is evidently larger than that after drinking sucrose solution, and that after drinking sucrose solution is larger than that after drinking fructose solution. Our pilot studies indicate that OCT technique is capable of non-invasive, real-time, and sensitive monitoring of skin optical properties in human subjects during oral intake of different sugars.

Real-time estimation of glucose concentration in algae cultivation system using Raman spectroscopy

This work proposes a soft-sensor design for real-time estimation of glucose concentration under mixotrophic conditions using Raman spectroscopy. The suggested approach applies a Rolling-Circle Filter (RCF), Partial Least Squares (PLS), and a successive Savitzky-Golay (SG) smoothing filter. RCF is used to remove the background effects of Raman spectrum in the pre-processing step. PLS is used to reduce the dimensionality of spectrum data and relate them to the concentration. The SG filter is further employed as a post-processing step in a successive manner to adjust predicted glucose concentrations. Two sets of experiments using artificial assays and samples from a microalgae cultivation system were performed for verification. The proposed approach showed improved prediction performances compared to other data processing and regression techniques.

Development and comparative assessment of Raman spectroscopic classification algorithms for lesion discrimination in stereotactic breast biopsies with microcalcifications

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. Here, we develop and compare different approaches for developing Raman classification algorithms to diagnose invasive and in situ breast cancer, fibrocystic change and fibroadenoma that can be associated with microcalcifications. In this study, Raman spectra were acquired from tissue cores obtained from fresh breast biopsies and analyzed using a constituent-based breast model. Diagnostic algorithms based on the breast model fit coefficients were devised using logistic regression, C4.5 decision tree classification, k-nearest neighbor (k -NN) and support vector machine (SVM) analysis, and subjected to leave-one-out cross validation. The best performing algorithm was based on SVM analysis (with radial basis function), which yielded a positive predictive value of 100% and negative predictive value of 96% for cancer diagnosis. Importantly, these results demonstrate that Raman spectroscopy provides adequate diagnostic information for lesion discrimination even in the presence of microcalcifications, which to the best of our knowledge has not been previously reported.

Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring

Over the past decade, optical spectroscopy has been employed in combination with multivariate chemometric models to investigate a wide variety of diseases and pathological conditions, primarily due to its excellent chemical specificity and lack of sample preparation requirements. Despite promising results in several proof-of-concept studies, its translation to the clinical setting has often been hindered by inadequate accuracy of the conventional spectroscopic models. To address this issue and the possibility of curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture spectra (due to fluctuations in sample and environmental conditions), support vector machine-based least-squares nonlinear regression (LS-SVR) has been recently proposed. In this paper, we investigate the robustness of this methodology to noise-induced instabilities and present an analytical formula for estimating modeling precision as a function of measurement noise and model parameters. This formalism can be readily used to evaluate uncertainty in information extracted from spectroscopic measurements, particularly important for rapid-acquisition biomedical applications. Subsequently, using field data (Raman spectra) acquired from a glucose clamping study on an animal model subject, we perform the first systematic investigation of the relative effect of additive interference components (namely, noise in prediction spectra, calibration spectra, and calibration concentrations) on the prediction error of nonlinear spectroscopic models. Our results show that the LS-SVR method gives more accurate results and is substantially more robust to additive noise when compared with conventional regression methods such as partial least-squares regression (PLS), when careful selection of the LS-SVR model parameters are performed. We anticipate that these results will be useful for uncertainty estimation in similar biomedical applications where the precision of measurements and its response to noise in the data set is as important, if not more so, than the generic accuracy level.

Precision of Raman spectroscopy measurements in detection of microcalcifications in breast needle biopsies

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. We developed Raman spectroscopy decision algorithms to detect breast microcalcifications, based on fit coefficients (FC) derived by modeling tissue Raman spectra as a linear combination of the Raman spectra of 9 chemical and morphologic components of breast tissue. However, little or no information is available on the precision of such measurements and its effect on the ability of Raman spectroscopy to make predictions for breast microcalcification detection. Here we report the precision, that is, the closeness of agreement between replicate Raman spectral measurements--and the model FC derived from them--obtained ex vivo from fresh breast biopsies from patients undergoing stereotactic breast needle biopsy, using a compact clinical Raman system. The coefficients of variation of the model FC averaged 0.03 for normal breast tissue sites, 0.12 for breast lesions without, and 0.22 for breast lesions with microcalcifications. Imprecision in the FC resulted in diagnostic discordance among replicates only for line-sitters, that is, tissue sites with FC values near the decision line or plane. The source of this imprecision and their implications for the use of Raman spectroscopy for guidance of stereotactic breast biopsies for microcalcifications are also discussed. In summary, we conclude that the precision of Raman spectroscopy measurements in breast tissue obtained using our compact clinical system is more than adequate to make accurate and repeatable predictions of microcalcifications in breast tissue using decision algorithms based on model FC. This provides strong evidence of the potential of Raman spectroscopy guidance of stereotactic breast needle biopsies for microcalcifications.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin

In recent years, glycated hemoglobin (HbA1c) has been increasingly accepted as a functional metric of mean blood glucose in the treatment of diabetic patients. Importantly, HbA1c provides an alternate measure of total glycemic exposure due to the representation of blood glucose throughout the day, including post-prandially. In this article, we propose and demonstrate the potential of Raman spectroscopy as a novel analytical method for quantitative detection of HbA1c, without using external dyes or reagents. Using the drop coating deposition Raman (DCDR) technique, we observe that the nonenzymatic glycosylation (glycation) of the hemoglobin molecule results in subtle but discernible and highly reproducible changes in the acquired spectra, which enable the accurate determination of glycated and nonglycated hemoglobin using standard chemometric methods. The acquired Raman spectra display excellent reproducibility of spectral characteristics at different locations in the drop and show a linear dependence of the spectral intensity on the analyte concentration. Furthermore, in hemolysate models, the developed multivariate calibration models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detection that is a factor of ~15 smaller than the lowest physiological concentrations encountered in clinical practice. The excellent accuracy and reproducibility achieved in this proof-of-concept study opens substantive avenues for characterization and quantification of the glycosylation status of (therapeutic) proteins, which are widely used for biopharmaceutical development. We also envision that the proposed approach can provide a powerful tool for high-throughput HbA1c sensing in multicomponent mixtures and potentially in hemolysate and whole blood lysate samples.

Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation

We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen (∼777 nm) to nitrogen (742.36 nm, 744.23 nm and 746.83 nm) compositional values yielded an optimal value at 746.83 nm with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry.

Raman spectroscopy: a real-time tool for identifying microcalcifications during stereotactic breast core needle biopsies

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. We present here a Raman spectroscopic tool for detecting microcalcifications in breast tissue based on their chemical composition. We collected ex vivo Raman spectra from 159 tissue sites in fresh stereotactic breast needle biopsies from 33 patients, including 54 normal sites, 75 lesions with microcalcifications and 30 lesions without microcalcifications. Application of our Raman technique resulted in a positive predictive value of 97% for detecting microcalcifications. This study shows that Raman spectroscopy has the potential to detect microcalcifications during stereotactic breast core biopsies and provide real-time feedback to radiologists, thus reducing non-diagnostic and false negative biopsies.