Simultaneous multiplexed quantification of caffeine and its major metabolites theobromine and paraxanthine using surface-enhanced Raman scattering

The Role of Hydrogen Bonding in the Raman Spectral Signals of Caffeine in Aqueous Solution

The identification and quantification of caffeine is a common need in the food and pharmaceutical industries and lately also in the field of environmental science. For that purpose, Raman spectroscopy has been used as an analytical technique, but the interpretation of the spectra requires reliable and accurate computational protocols, especially as regards the Resonance Raman (RR) variant. Herein, caffeine solutions are sampled using Molecular Dynamics simulations. Upon quantification of the strength of the non-covalent intermolecular interactions such as hydrogen bonding between caffeine and water, UV-Vis, Raman, and RR spectra are computed. The results provide general insights into the hydrogen bonding role in mediating the Raman spectral signals of caffeine in aqueous solution. Also, by analyzing the dependence of RR enhancement on the absorption spectrum of caffeine, it is proposed that the sensitivity of the RR technique could be exploited at excitation wavelengths moderately far from 266 nm, yet achieving very low detection limits in the quantification caffeine content.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Metabolomics analysis of urine from patients with alcohol-associated liver disease reveals dysregulated caffeine metabolism

Excess alcohol intake causes millions of deaths annually worldwide. Asymptomatic early-stage, alcohol-associated liver disease (ALD) is easily overlooked, and ALD is usually only diagnosed in more advanced stages. We explored the possibility of using polar urine metabolites as biomarkers of ALD for early-stage diagnosis and functional assessment of disease severity by quantifying the abundance of polar metabolites in the urine samples of healthy controls (n = 18), patients with mild or moderate liver injury (n = 21), and patients with severe alcohol-associated hepatitis (n = 25). The polar metabolites in human urine were first analyzed by untargeted metabolomics, showing that 209 urine metabolites are significantly changed in patients, and 17 of these are highly correlated with patients' model for end-stage liver disease (MELD) score. Pathway enrichment analysis reveals that the caffeine metabolic pathway is the most affected in ALD. We then developed a targeted metabolomics method and measured the concentration of caffeine and its metabolites in urine using internal and external standard calibration, respectively. The described method can quantify caffeine and its 14 metabolites in 35 min. The results of targeted metabolomics analysis agree with the results of untargeted metabolomics, showing that 13 caffeine metabolites are significantly decreased in patients. In particular, the concentrations of 1-methylxanthine, paraxanthine, and 5-acetylamino-6-amino-3-methyluracil are markedly decreased with increased disease severity. We suggest that these three metabolites could serve as functional biomarkers for differentiating early-stage ALD from more advanced liver injury.NEW & NOTEWORTHY Our study using both untargeted and targeted metabolomics reveals the caffeine metabolic pathway is dysregulated in ALD. Three caffeine metabolites, 1-methylxanthine, paraxanthine, and 5-acetylamino-6-amino-3-methyluracil, can differentiate the severity of early-stage ALD.

Surface potential modulation as a tool for mitigating challenges in SERS-based microneedle sensors

Raman spectroscopic-based biosensing strategies are often complicated by low signal and the presence of multiple chemical species. While surface-enhanced Raman spectroscopy (SERS) nanostructured platforms are able to deliver high quality signals by focusing the electromagnetic field into a tight plasmonic hot-spot, it is not a generally applicable strategy as it often depends on the specific adsorption of the analyte of interest onto the SERS platform. This paper describes a strategy to address this challenge by using surface potential as a physical binding agent in the context of microneedle sensors. We show that the potential-dependent adsorption of different chemical species allows scrutinization of the contributions of different chemical species to the final spectrum, and that the ability to cyclically adsorb and desorb molecules from the surface enables efficient application of multivariate analysis methods. We demonstrate how the strategy can be used to mitigate potentially confounding phenomena, such as surface reactions, competitive adsorption and the presence of molecules with similar structures. In addition, this decomposition helps evaluate criteria to maximize the signal of one molecule with respect to others, offering new opportunities to enhance the measurement of analytes in the presence of interferants.

Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

Part-Per-Billion Level Chemical Sensing with a Gold-Based SERS-Active Substrate

We used surface-enhanced Raman spectroscopy (SERS) for the rapid and sensitive detection and quantification of caffeine in solution. Such a technique incorporated into a portable device is finding wide applications in trace chemical analysis in various fields, including law enforcement, medicine, environmental monitoring, and food quality control. To realize such applications, we are currently developing portable and handheld trace chemical analyzers based on SERS, which are integrated with a sensor embedded with activated gold nanoparticles in a porous glass matrix. In this study, we used this gold SERS-active substrate to measure aqueous solutions of the drug caffeine as a test chemical to benchmark sensor performance by defining sensitivity (lowest measured concentration (LMC) and estimated limit of detection (LOD)), determining concentration dependence and quantification capabilities by constructing calibration curves; by evaluating the effects of pH values of 3, 7, and 11; and by examining the reproducibility of the SERS measurements. The results demonstrate that the SERS sensor is sensitive, with caffeine detected at an LMC of 50 parts per billion (ppb) with an LOD of 0.63 ppb. The results further show that the sensor is very stable and can be used to make reproducible measurements, even under extremely acidic to basic pH conditions. Vibrational assignments of all observed SERS peaks are made and reported for the first time for caffeine on a gold substrate.

Human metabolite detection by surface-enhanced Raman spectroscopy

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

Fast and non-destructive determination of water content in ionic liquids at varying temperatures by Raman spectroscopy and multivariate regression analysis

Imidazolium acetate ionic liquids (ILs) have been utilized as promising solvents in many applications that involve varying water content and temperature. These experimental variables affect the anion-cation intermolecular interactions, which in turn influence the performance of the ILs in these applications. This paper shows Raman spectroscopy can be used as an operando method to measure water content in IL solvents when simultaneous temperature changes may occur. The Raman spectra of 1-alkyl-3-methylimidazolium acetate ILs (alkyl chain length n = 2, 4, 6, 8) with varying water content (from 0.028 to 0.899 water mole fraction) and temperature (from 78.1 K to 423.1 K) were measured. Increasing the water content or decreasing the temperature of the tested ILs weakens the anion-cation intermolecular interactions. The water content of these ILs can be quantified even in conditions when the temperature is changing using Raman spectroscopy combined with multivariate regression analysis, including principal component regression (PCR), partial-least-squares regression (PLSR), and artificial neural networks (ANNs). The ANN model combined with partial-least-squares (PLS) achieved the highest prediction accuracy of water content in ILs at varying temperatures (RMSECV = 0.017, R²_CV = 99.1%, RMSEP = 0.019, R²_P = 98.8%, RPD = 8.93). Raman spectroscopy provides a potential fast non-destructive operando method to monitor the water content of ILs even in applications when the temperature may be simultaneously altered; this information can lead to the optimized use of these ILs in many applications.

Discriminative and quantitative analysis of norepinephrine and epinephrine by surface-enhanced Raman spectroscopy with gold nanoparticle suspensions

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique capable of increasing the Raman signal of an analyte using specific nanostructures. The close contact between those nanostructures, usually a suspension of nanoparticles, and the molecule of interest produces an important exaltation of the intensity of the Raman signal. Even if the exaltation leads to an improvement of Raman spectroscopy sensitivity, the complexity of the SERS signal and the numbers of parameters to be controlled allow the use of SERS for detection rather than quantification. The aim of this study was to develop a robust discriminative and quantitative analysis in accordance with pharmaceutical standards. In this present work, we develop a discriminative and quantitative analysis based on the previous optimized parameters obtained by the design of experiments fixed for norepinephrine (NOR) and extended to epinephrine (EPI) which are two neurotransmitters with very similar structures. Studying the short evolution of the Raman signal intensity over time coupled with chemometric tools allowed the identification of outliers and their removal from the data set. The discriminant analysis showed an excellent separation of EPI and NOR. The comparative analysis of the data showed the superiority of the multivariate analysis after logarithmic transformation. The quantitative analysis allowed the development of robust quantification models from several gold nanoparticle batches with limits of quantification of 32 µg/mL for NOR and below 20 µg/mL for EPI even though no Raman signal is observable for such concentrations. This study improves SERS analysis over ultrasensitive detection for discrimination and quantification using a handheld Raman spectrometer.

Sub-Part-Per-Billion Level Sensing of Fentanyl Residues from Wastewater Using Portable Surface-Enhanced Raman Scattering Sensing

Detection of illicit drug residues from wastewater provides a new route toward community-level assessment of drug abuse that is critical to public health. However, traditional chemistry analytical tools such as high-performance liquid chromatography in tandem with mass spectrometry (HPLC-MS) cannot meet the large-scale testing requirement in terms of cost, promptness, and convenience of use. In this article, we demonstrated ultra-sensitive and portable surface-enhanced Raman scattering sensing (SERS) of fentanyl, a synthetic opioid, from sewage water and achieved quantitative analysis through principal component analysis and partial least-squares regression. The SERS substrates adopted in this application were synthesized by in situ growth of silver nanoparticles on diatomaceous earth films, which show ultra-high sensitivity down to 10 parts per trillion in artificially contaminated tap water in the lab using a commercial portable Raman spectrometer. Based on training data from artificially contaminated tap water, we predicted the fentanyl concentration in the sewage water from a wastewater treatment plant to be 0.8 parts per billion (ppb). As a comparison, the HPLC-MS confirmed the fentanyl concentration was below 1 ppb but failed to provide a specific value of the concentration since the concentration was too low. In addition, we further proved the validity of our SERS sensing technique by comparing SERS results from multiple sewage water treatment plants, and the results are consistent with the public health data from our local health authority. Such SERS sensing technique with ultra-high sensitivity down to sub-ppb level proved its feasibility for point-of-care detection of illicit drugs from sewage water, which is crucial to assess public health.

Detection of caffeine intake by means of EC-SERS spectroscopy of human saliva

This work presents the application of EC-SERS spectroscopy for the detection of caffeine consumption from human saliva. Caffeine and paraxanthine as the major metabolite of caffeine were tested. Model samples of saliva spiked with caffeine were investigated, and detection of caffeine in real-life saliva samples was tested in order to ensure the viability of the method for clinical applications. Two doses of caffeine (2 mg/kg and 3.5 mg/kg) were ingested by volunteers, and their saliva samples were taken at different time periods ranging from 1 h to 10 h after the consumption. Density functional theory calculations of caffeine and paraxanthine adsorbed on the silver surface were performed in order to better understand the adsorption of the investigated molecules and to make a correct assignment of the experimental spectral bands of the EC-SERS spectra. It was determined that a low dose caffeine consumption can be detected by the appearance of the SERS spectral marker band of caffeine and paraxanthine at 692 cm^-1. The intensity of this band is mostly reasoned by the paraxanthine concentration since the intensity changes of the band over time correlates to the concentration changes of paraxanthine determined by the pharmacokinetic studies of paraxanthine and caffeine in the human saliva. It was found that the limit of detection paraxanthine in saliva by means of EC-SERS is as low as 15 μM and can be further improved.

Chocolate composition and its crystallization process: A multidisciplinary analysis

In this work, the composition of different types of chocolate was studied by using microscopy (optical and confocal fluorescence) and vibrational spectroscopy (Raman) aimed at obtaining more chemical information about this important food. By combining these techniques, it is possible to distinguish different components of chocolate. It was not possible to obtain Raman spectra of dark chocolate due to the presence of fluorescent flavonoids in cocoa particles. However, silver nanoparticles quench this fluorescent signal, and thus it is possible to obtain a surface-enhanced Raman spectroscopy spectrum of dark chocolate. The effect of ultrasound on the crystallization process of cocoa butter was also studied. These samples were also analysed by X-ray diffraction and differential scanning calorimetry. Furthermore, the combination of all these techniques was very useful in the specific analysis of different components of chocolate and could have a high impact in the chocolate industry.

A surface-enhanced Raman spectroscopy database of 63 metabolites

Metabolomics, the study of metabolic profiles in a biological sample, has seen rapid growth due to advances in measurement technologies such as mass spectrometry (MS). While MS metabolite reference libraries have been generated for metabolomics applications, mass spectra alone are unable to unambiguously identify many metabolites in a sample; these unidentified compounds are typically annotated as "features". Surface-enhanced Raman spectroscopy (SERS) is an interesting technology for metabolite identification based on vibrational spectra. However, no reports have been published that present SERS metabolite spectra from chemical libraries. In this paper, we demonstrate that an untargeted approach utilizing citrate-capped silver nanoparticles yields SERS spectra for 20% of 80 compounds chosen randomly from a commercial metabolite library. Furthermore, prescreening of the metabolites according to chemical functionality allowed for the efficient identification of samples within the library that yield distinctive SERS spectra under our experimental conditions. Last, we present a reference database of 63 metabolite SERS spectra for use as an identification tool in metabolomics studies; this set includes 30 metabolites that have not had previously published SERS spectra.

Application of random forest based approaches to surface-enhanced Raman scattering data

Surface-enhanced Raman scattering (SERS) is a valuable analytical technique for the analysis of biological samples. However, due to the nature of SERS it is often challenging to exploit the generated data to obtain the desired information when no reporter or label molecules are used. Here, the suitability of random forest based approaches is evaluated using SERS data generated by a simulation framework that is also presented. More specifically, it is demonstrated that important SERS signals can be identified, the relevance of predefined spectral groups can be evaluated, and the relations of different SERS signals can be analyzed. It is shown that for the selection of important SERS signals Boruta and surrogate minimal depth (SMD) and for the analysis of spectral groups the competing method Learner of Functional Enrichment (LeFE) should be applied. In general, this investigation demonstrates that the combination of random forest approaches and SERS data is very promising for sophisticated analysis of complex biological samples.

A Statistical Approach of Background Removal and Spectrum Identification for SERS Data

SERS (surface-enhanced Raman scattering) enhances the Raman signals, but the plasmonic effects are sensitive to the chemical environment and the coupling between nanoparticles, resulting in large and variable backgrounds, which make signal matching and analyte identification highly challenging. Removing background is essential, but existing methods either cannot fit the strong fluctuation of the SERS spectrum or do not consider the spectra’s shape change across time. Here we present a new statistical approach named SABARSI that overcomes these difficulties by combining information from multiple spectra. Further, after efficiently removing the background, we have developed the first automatic method, as a part of SABARSI, for detecting signals of molecules and matching signals corresponding to identical molecules. The superior efficiency and reproducibility of SABARSI are shown on two types of experimental datasets.

Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage

Taking advantage of surface-enhanced Raman scattering (SERS) methodology with its unique ability to collect abundant intrinsic fingerprint information and noninvasive data acquisition we set up a SERS-based approach for recognition of physically induced DNA damage with further incorporation of artificial neural network (ANN). As a proof-of-concept application, we used the DNA molecules, where the one oligonucleotide (OND) was grafted to the plasmonic surface while complimentary OND was exposed to UV illumination with various exposure doses and further hybridized with the grafted counterpart. All SERS spectra of entrapped DNA were collected by several operators using the portable spectrometer, without any optimization of measurements procedure (e.g., optimization of acquisition time, laser intensity, finding of optimal place on substrate, manual baseline correction, etc.) which usually takes a significant amount of operator's time. The SERS spectra were employed as input data for ANN training, and the performance of the system was verified by predicting the class labels for SERS validation data, using a spectra dataset, which has not been involved in the training process. During that phase, accuracy higher than 98% was achieved with a level of confidence exceeding 95%. It should be noted that utilization of the proposed functional-SERS/ANN approach allows identifying even the minor DNA damage, almost invisible by control measurements, performed with common analytical procedures. Moreover, we introduce the advanced ANN design, which allows not only classifying the samples but also providing the ANN analysis feedback, which associates the spectral changes and chemical transformations of DNA structure.

Rapid Detection and Quantification of Novel Psychoactive Substances (NPS) Using Raman Spectroscopy and Surface-Enhanced Raman Scattering

With more than a million seizures of illegal drugs reported annually across Europe, the variety of psychoactive compounds available is vast and ever-growing. The multitude of risks associated with these compounds are well-known and can be life threatening. Hence the need for the development of new analytical techniques and approaches that allow for the rapid, sensitive, and specific quantitative detection and discrimination of such illicit materials, ultimately with portability for field testing, is of paramount importance. The aim of this study was to demonstrate the application of Raman spectroscopy and surface-enhanced Raman scattering (SERS) combined with chemometrics approaches, as rapid and portable techniques for the quantitative detection and discrimination of a wide range of novel psychoactive substances (methcathinone and aminoindane derivatives), both in powder form and in solution. The Raman spectra of the psychoactive compounds provided clear separation and classification of the compounds based on their core chemical structures; viz. methcathinones, aminoindanes, diphenidines, and synthetic cannabinoids. The SERS results also displayed similar clustering patterns, with improved limits of detections down to ~2 mM (0.41 g L⁻¹). As mephedrone is currently very popular for recreational use we performed multiplexed quantitative detection of mephedrone (4-methylmethcathinone), and its two major metabolites (nor-mephedrone and 4-methylephedrine), as tertiary mixtures in water and healthy human urine. These findings readily illustrate the potential application of SERS for simultaneous detection of multiple NPS as mixtures without the need for lengthy prior chromatographic separation or enrichment methods.

Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) has recently gained increasing attention for the detection of trace quantities of biomolecules due to its excellent molecular specificity, ultrasensitivity, and quantitative multiplex ability. Specific single or multiple biomarkers in complex biological environments generate strong and distinct SERS spectral signals when they are in the vicinity of optically active nanoparticles (NPs). When multivariate chemometrics are applied to decipher underlying biomarker patterns, SERS provides qualitative and quantitative information on the inherent biochemical composition and properties that may be indicative of healthy or diseased states. Moreover, SERS allows for differentiation among many closely-related causative agents of diseases exhibiting similar symptoms to guide early prescription of appropriate, targeted and individualised therapeutics. This review provides an overview of recent progress made by the application of SERS in the diagnosis of cancers, microbial and respiratory infections. It is envisaged that recent technology development will help realise full benefits of SERS to gain deeper insights into the pathological pathways for various diseases at the molecular level.

Development of surface-enhanced Raman spectroscopy application for determination of illicit drugs: Towards a practical sensor

Surface-enhanced Raman spectroscopy (SERS) has been widely applied to identify or detect illicit drugs, because of the ability for highly specific molecular fingerprint and independence of aqueous solutions impact. We summarize the progress in determination of illicit drugs using SERS, including trace illicit drugs, suspicious objects and drugs or their metabolites in real biological system (urine, saliva and so on). Even though SERS detection of illicit drugs in real samples still remains a huge challenge because of the complex unknown environment, the efficient sample separation and the improved hand-held Raman analyzer will provide the possibility to make SERS a practically analytical technique. Moreover, we put forward a prospective overview for future perspectives of SERS as a practical sensor for illicit drugs determination. Perhaps the review is not exhaustive, we expect to help researchers to understand the evolution and challenges in this field and further interest in promoting Raman and SERS as a practical analyzer for convenient and automated illicit drugs identification.

Bare carbon electrodes as simple and efficient sensors for the quantification of caffeine in commercial beverages

Food quality control is a mandatory task in the food industry and relies on the availability of simple, cost-effective and stable sensing platforms. In the present work, the applicability of bare glassy carbon electrodes for routine analysis of food samples was evaluated as a valid alternative to chromatographic techniques, using caffeine as test analyte. A number of experimental parameters were optimized and a differential pulse voltammetry was applied for quantification experiments. The detection limit was found to be 2 × 10^-5 M (3σ criterion) and repeatability was evaluated by the relative standard deviation of 4.5%. The influence of sugars, and compounds structurally related to caffeine on the current response of caffeine was evaluated and found to have no significant influence on the electrode performance. The suitability of bare carbon electrodes for routine analysis was successfully demonstrated by quantifying caffeine content in seven commercially available drinks and the results were validated using a standard ultra-high performance liquid chromatography method. This work demonstrates that bare glassy carbon electrodes are a simple, reliable and cost-effective platform for rapid analysis of targets such as caffeine in commercial products and they represent therefore a competitive alternative to the existing analytical methodologies for routine food analysis.

SERS-based quantification of albuminuria in the normal-to-mildly increased range

We developed a SERS-based method for the screening of albuminuria in the sub-microalbuminuria interval. We show the potential of SERS for absolute quantification of albumin.

Bioanalytical applications of surface-enhanced Raman spectroscopy: de novo molecular identification

Surface enhanced Raman scattering (SERS) has become a powerful technique for trace analysis of biomolecules. The use of SERS-tags has evolved into clinical diagnostics, the enhancement of the intrinsic signal of biomolecules on SERS active materials shows tremendous promise for the analysis of biomolecules and potential biomedical assays. The detection of the de novo signal from a wide range of biomolecules has been reported to date. In this review, we examine different classes of biomolecules for the signals observed and experimental details that enable their detection. In particular, we survey nucleic acids, amino acids, peptides, proteins, metabolites, and pathogens. The signals observed show that the interaction of the biomolecule with the enhancing nanostructure has a significant influence on the observed spectrum. Additional experiments demonstrate that internal standards can correct for intensity fluctuations and provide quantitative analysis. Experimental methods that control the interaction at the surface are providing for reproducible SERS signals. Results suggest that combining advances in methodology with the development of libraries for SERS spectra may enable the characterization of biomolecules complementary to other existing methods.

Absolute Quantification of Uric Acid in Human Urine Using Surface Enhanced Raman Scattering with the Standard Addition Method

High levels of uric acid in urine and serum can be indicative of hypertension and the pregnancy related condition, preeclampsia. We have developed a simple, cost-effective, portable surface enhanced Raman scattering (SERS) approach for the routine analysis of uric acid at clinically relevant levels in urine patient samples. This approach, combined with the standard addition method (SAM), allows for the absolute quantification of uric acid directly in a complex matrix such as that from human urine. Results are highly comparable and in very good agreement with HPLC results, with an average <9% difference in predictions between the two analytical approaches across all samples analyzed, with SERS demonstrating a 60-fold reduction in acquisition time compared with HPLC. For the first time, clinical prepreeclampsia patient samples have been used for quantitative uric acid detection using a simple, rapid colloidal SERS approach without the need for complex data analysis.

Multiplex in vitro detection using SERS

This review focuses on the recent advances in SERS and its potential to detect multiple biomolecules in clinical samples.