Quantitative detection of acyclovir by surface enhanced Raman spectroscopy using a portable Raman spectrometer coupled with multivariate data analysis

A breakthrough in phytochemical profiling: ultra-sensitive surface-enhanced Raman spectroscopy platform for detecting bioactive components in medicinal and edible plants

A perennial challenge in harnessing the rich biological activity of medicinal and edible plants is the accurate identification and sensitive detection of their active compounds. In this study, an innovative, ultra-sensitive detection platform for plant chemical profiling is created using surface-enhanced Raman spectroscopy (SERS) technology. The platform uses silver nanoparticles as the enhancing substrate, excess sodium borohydride prevents substrate oxidation, and methanol enables the tested molecules to be better adsorbed onto the silver nanoparticles. Subsequently, nanoparticle aggregation to form stable "hot spots" is induced by Ca2+, and the Raman signal of the target molecule is strongly enhanced. At the same time, deuterated methanol was used as the internal standard for quantitative determination. The method has excellent reproducibility, RSD ≤ 1.79%, and the enhancement factor of this method for the detection of active ingredients in the medicinal plant Coptis chinensis was 1.24 × 109, with detection limits as low as 3 fM. The platform successfully compared the alkaloid distribution in different parts of Coptis chinensis: root > leaf > stem, and the difference in content between different batches of Coptis chinensis decoction was successfully evaluated. The analytical technology adopted by the platform can speed up the determination of Coptis chinensis and reduce the cost of analysis, not only making better use of these valuable resources but also promoting development and innovation in the food and pharmaceutical industries. This study provides a new method for the development, evaluation, and comprehensive utilization of both medicinal and edible plants. It is expected that this method will be extended to the modern rapid detection of other medicinal and edible plants and will provide technical support for the vigorous development of the medicinal and edible plants industry.

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.

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning-convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.

The plasma membrane of focal adhesions has a high content of cholesterol and phosphatidylcholine with saturated acyl chains

Cellular functions, such as differentiation and migration, are regulated by the extracellular microenvironment, including the extracellular matrix (ECM). Cells adhere to ECM through focal adhesions (FAs) and sense the surrounding microenvironments. Although FA proteins have been actively investigated, little is known about the lipids in the plasma membrane at FAs. In this study, we examine the lipid composition at FAs with imaging and biochemical approaches. Using the cholesterol-specific probe D4 with total internal reflection fluorescence microscopy and super-resolution microscopy, we show an enrichment of cholesterol at FAs simultaneously with FA assembly. Furthermore, we establish a method to isolate the lipid from FA-rich fractions, and biochemical quantification of the lipids reveals that there is a higher content of cholesterol and phosphatidylcholine with saturated fatty acid chains in the lipids of the FA-rich fraction than in either the plasma membrane fraction or the whole-cell membrane. These results demonstrate that plasma membrane at FAs has a locally distinct lipid composition compared to the bulk plasma membrane.

Surface-Enhanced Raman scattering (SERS) filter paper substrates decorated with silver nanoparticles for the detection of molecular vibrations of Acyclovir drug

Acyclovir (ACV) drug, a common antiviral agent, is frequently used as the primary clinical treatment method for treating hepatitis B, herpes simplex, and varicella zoster viruses due to its potent therapeutic effect. In patients with compromised immune systems, this medication can stop cytomegalovirus infections, and high doses of this drug are required; however, such prescription leads to kidney toxicity. Therefore, timely and accurate detection of ACV is crucial in many areas. Surface-Enhanced Raman Scattering (SERS) is a reliable, rapid, and precise approach for the identification of trace biomaterials and chemicals. Filter paper substrates decorated with silver nanoparticles (AgNPs) were applied as SERS biosensors to detect ACV and control its adverse effects. Initially, a chemical reduction procedure was utilized to produce AgNPs. Afterward, UV-Vis, FE-SEM, XRD, TEM, DLS, and AFM were employed to examine the properties of prepared AgNPs. In order to prepare SERS-active filter paper substrates (SERS-FPS) to detect Molecular vibrations of ACV, AgNPs prepared by immersion method were coated on filter paper substrates. Moreover, the UV-Vis DRS analysis was carried out to assess the stability of filter paper substrates and SERS-FPS. The AgNPs reacted with ACV after being coated on SERS-active plasmonic substrates and could sensitively detect ACV in small concentrations. It was discovered that the limit of detection of SERS plasmonic substrates was 10^-12 M. Moreover, the mean RSD for ten repeated tests was calculated as 4.19%. The enhancement factor for detecting ACV using the developed biosensors was calculated to be 3.024 × 10⁵ and 3.058 × 10⁵ experimentally and via simulation, respectively. According to the Raman results, SERS-FPS for the detection of ACV, fabricated by the present methods, showed promising results for SERS-based investigations. Furthermore, these substrates showed significant disposablity, reproducibility, and chemical stability. Therefore, the fabricated substrates are capable to be employed as potential SERS biosensors to detect trace substances.

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.

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.

Optical biosensors - Illuminating the path to personalized drug dosing

Optical biosensors are low-cost, sensitive and portable devices that are poised to revolutionize the medical industry. Healthcare monitoring has already been transformed by such devices, with notable recent applications including heart rate monitoring in smartwatches and COVID-19 lateral flow diagnostic test kits. The commercial success and impact of existing optical sensors has galvanized research in expanding its application in numerous disciplines. Drug detection and monitoring seeks to benefit from the fast-approaching wave of optical biosensors, with diverse applications ranging from illicit drug testing, clinical trials, monitoring in advanced drug delivery systems and personalized drug dosing. The latter has the potential to significantly improve patients' lives by minimizing toxicity and maximizing efficacy. To achieve this, the patient's serum drug levels must be frequently measured. Yet, the current method of obtaining such information, namely therapeutic drug monitoring (TDM), is not routinely practiced as it is invasive, expensive, time-consuming and skilled labor-intensive. Certainly, optical sensors possess the capabilities to challenge this convention. This review explores the current state of optical biosensors in personalized dosing with special emphasis on TDM, and provides an appraisal on recent strategies. The strengths and challenges of optical biosensors are critically evaluated, before concluding with perspectives on the future direction of these sensors.

Rapid and simultaneous quality analysis of the three active components in Lonicerae Japonicae Flos by near-infrared spectroscopy

Lonicerae Japonicae Flos (LJF) has historically been widely utilized as a tea and health food. To better understand and evaluate its quality evaluate its quality, a near-infrared spectroscopy (NIRS) method was developed for the rapid and simultaneous analysis of the 3 main active components (chlorogenic acid, isochlorogenic acid A and isochlorogenic acid C). The NIRS model was built using 2 different strategies: partial least squares (PLS) as a linear regression method and artificial neural networks (ANN) as a nonlinear regression method. Furthermore, the NIRS method was applied to analyze the 4 main quality factors, which included 5 processing methods (shade drying, sun drying, vacuum drying, freeze drying and hot-air drying), 2 kinds of harvest time (flower bud stage and florescence stage), 2 species and 8 geographical origins. Collectively, NIRS is a promising method for the quality analysis of LJF.

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.