Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health.

Recent Plasmonic Gold- and Silver-Assisted Raman Spectra for Advanced SARS-CoV-2 Detection

COVID-19 has become one of the deadliest epidemics in the past years. In efforts to combat the deadly disease besides vaccines, drug therapies, and facemasks, significant focus has been on designing specific methods for the sensitive and accurate detection of SARS-CoV-2. Of these, surface-enhanced Raman scattering (SERS) is an attractive analytical tool for the identification of SARS-CoV-2. SERS is the phenomenon of enhancement of Raman intensity signals from molecular analytes anchored onto the surfaces of roughened plasmonic nanomaterials. This work gives an updated summary of plasmonic gold nanomaterials (AuNMs) and silver nanomaterials (AgNMs)-based SERS technologies to identify SARS-CoV-2. Due to extreme "hot spots" promoting higher electromagnetic fields on their surfaces, different shapes of AuNMs and AgNMs combined with Raman probes have been reviewed for enhancing Raman signals of probe molecules for quantifying the virus. It also reviews progress made recently in the design of certain specific Raman probe molecules capable of imparting characteristic SERS response/tags for SARS-CoV-2 detection.

Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry

Cell ion channels, cell proliferation and metastasis, and many other life activities are inseparable from the regulation of trace or even single copper ion (Cu+ and/or Cu2+). In this work, an electrochemical sensor for sensitive quantitative detection of 0.4-4 amol L-1 copper ions is developed by adopting: (1) copper ions catalyzing the click-chemistry reaction to capture numerous signal units; (2) special adsorption assembly method of signal units to ensure signal generation efficiency; and (3) fast scan voltammetry at 400 V s-1 to enhance signal intensity. And then, the single-atom detection of copper ions is realized by constructing a multi-layer deep convolutional neural network model FSVNet to extract hidden features and signal information of fast scan voltammograms for 0.2 amol L-1 of copper ions. Here, we show a multiple signal amplification strategy based on functionalized nanomaterials and fast scan voltammetry, together with a deep learning method, which realizes the sensitive detection and even single-atom detection of copper ions.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions. These unique optical properties of plasmonic NPs have been used to design chemical and biological sensors. Over the last few decades, colloidal plasmonic NPs have been greatly exploited in sensing applications through LSPR shifts (colorimetry), surface-enhanced Raman scattering, surface-enhanced fluorescence, and chiroptical activity. Although colloidal plasmonic NPs have emerged at the forefront of nanobiosensors, there are still several important challenges to be addressed for the realization of plasmonic NP-based sensor kits for routine use in daily life. In this comprehensive review, researchers of different disciplines (colloidal and analytical chemistry, biology, physics, and medicine) have joined together to summarize the past, present, and future of plasmonic NP-based sensors in terms of different sensing platforms, understanding of the sensing mechanisms, different chemical and biological analytes, and the expected future technologies. This review is expected to guide the researchers currently working in this field and inspire future generations of scientists to join this compelling research field and its branches.

Innovative and versatile surface-enhanced Raman spectroscopy-inspired approaches for viral detection leading to clinical applications: A review

The evolution of analytical techniques has opened the possibilities of accurate analyte detection through a straightforward method and short acquisition time, leading towards their applicability to identify medical conditions. Surface-enhanced Raman spectroscopy (SERS) has long been proven effective for rapid detection and relies on SERS spectra that are unique to each specific analyte. However, the complexity of viruses poses challenges to SERS and hinders further progress in its practical applications. The principle of SERS revolves around the interaction among substrate, analyte, and Raman laser, but most studies only emphasize the substrate, especially label-free methods, and the synergy among these factors is often ignored. Therefore, issues related to reproducibility and consistency of results, which are crucial for medical diagnosis and are the main highlights of this review, can be understood and largely addressed when considering these interactions. Viruses are composed of multiple surface components and can be detected by label-free SERS, but the presence of non-target molecules in clinical samples interferes with the detection process. Appropriate spectral data processing workflow also plays an important role in the interpretation of results. Furthermore, integrating machine learning into data processing can account for changes brought about by the presence of non-target molecules when analyzing spectral features to accurately group the data, for example, whether the sample corresponds to a positive or negative patient, and whether a virus variant or multiple viruses are present in the sample. Subsequently, advances in interdisciplinary fields can bring SERS closer to practical applications.

Unlocking Predictive Capability and Enhancing Sensing Performances of Plasmonic Hydrogen Sensors via Phase Space Reconstruction and Convolutional Neural Networks

This study innovates plasmonic hydrogen sensors (PHSs) by applying phase space reconstruction (PSR) and convolutional neural networks (CNNs), overcoming previous predictive and sensing limitations. Utilizing a low-cost and efficient colloidal lithography technique, palladium nanocap arrays are created and their spectral signals are transformed into images using PSR and then trained using CNNs for predicting the hydrogen level. The model achieves accurate predictions with average accuracies of 0.95 for pure hydrogen and 0.97 for mixed gases. Performance improvements observed are a reduction in response time by up to 3.7 times (average 2.1 times) across pressures, SNR increased by up to 9.3 times (average 3.9 times) across pressures, and LOD decreased from 16 Pa to an extrapolated 3 Pa, a 5.3-fold improvement. A practical application of remote hydrogen sensing without electronics in hydrogen environments is actualized and achieves a 0.98 average test accuracy. This methodology reimagines PHS capabilities, facilitating advancements in hydrogen monitoring technologies and intelligent spectrum-based sensing.

Accurate and reliable surface-enhanced Raman spectroscopy assay for early detection of SARS-CoV-2 RNA with exceptional sensitivity

This research proposes a highly sensitive and simple surface-enhanced Raman spectroscopy (SERS) assay for the detection of SARS-CoV-2 RNA using suitably designed probes specific for RdRp and N viral genes attached to a Raman marker. The sensitivity of the assay was optimized through precise adjustments to the conditions of immobilization and hybridization processes of the target RNA, including modifications to factors such as time and temperature. The assay achieved a remarkable sensitivity down to 58.39 copies/mL, comparable to or lower than the sensitivities reported for commercial fluorescent polymerase chain reaction (PCR) based methods. It has good selectivity in discriminating SARS-CoV-2 RNA against other respiratory viruses, respiratory syncytial virus (RSV), and influenza A virus. The reliability of the assay was validated by testing 24 clinical samples, including 12 positive samples with varying cycle threshold (Ct) values and 12 negative samples previously tested using real-time PCR. The assay consistently predicted true results that were in line with the PCR results for all samples. Furthermore, the assay demonstrated a notable limit of detection (LOD) of Ct (38 for RdRp gene and 37.5 for N-gene), indicating its capability to detect low concentrations of the target analyte and potentially facilitating early detection of the pathogen.

Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO2 array SERS sensor. SERS spectra with concentrations of different variants were collected using a portable Raman system. After appropriate spectral preprocessing, a deep learning algorithm, CoVari, is developed to predict both the viral variant species and concentrations. Using a 10-fold cross-validation strategy, the model achieves an average accuracy of 99.9% in discriminating between different virus variants and R2 values larger than 0.98 for quantifying viral concentrations of the three viruses, demonstrating the high quality of the detection. The limit of detection of the ACE2 SERS sensor is determined to be 10.472, 11.882, and 21.591 PFU/mL for SARS-CoV-2, SARS-CoV-2 B1, and CoV-NL63, respectively. The feature importance of virus classification and concentration regression in the CoVari algorithm are calculated based on a permutation algorithm, which showed a clear correlation to the biochemical origins of the spectra or spectral changes. In an unknown specimen test, classification accuracy can achieve >90% for concentrations larger than 781 PFU/mL, and the predicted concentrations consistently align with actual values, highlighting the robustness of the proposed algorithm. Based on the CoVari architecture and the output vector, this algorithm can be generalized to predict both viral variant species and concentrations simultaneously for a broader range of viruses. These results demonstrate that the SERS + CoVari strategy has the potential for rapid and quantitative detection of virus variants and potentially point-of-care diagnostic platforms.

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates

Recently, there has been an exponential growth in the number of publications focusing on surface-enhanced Raman scattering (SERS), primarily driven by advancements in nanotechnology and the increasing demand for chemical and biological detection. While many of these publications have focused on the development of new substrates and detection-based applications, there is a noticeable lack of attention given to various practical issues related to SERS measurements and detection. This review aims to fill this gap by utilizing silver nanorod (AgNR) SERS substrates fabricated through the oblique angle deposition method as an illustrative example. The review highlights and addresses a range of practical issues associated with SERS measurements and detection. These include the optimization of SERS substrates in terms of morphology and structural design, considerations for measurement configurations such as polarization and the incident angle of the excitation laser, and exploration of enhancement mechanisms encompassing both intrinsic properties induced by the structure and materials, as well as extrinsic factors arising from wetting/dewetting phenomena and analyte size. The manufacturing and storage aspects of SERS substrates, including scalable fabrication techniques, contamination control, cleaning procedures, and appropriate storage methods, are also discussed. Furthermore, the review delves into device design considerations, such as well arrays, flow cells, and fiber probes, and explores various sample preparation methods such as drop-cast and immersion. Measurement issues, including the effect of excitation laser wavelength and power, as well as the influence of buffer, are thoroughly examined. Additionally, the review discusses spectral analysis techniques, encompassing baseline removal, chemometric analysis, and machine learning approaches. The wide range of AgNR-based applications of SERS, across various fields, is also explored. Throughout the comprehensive review, key lessons learned from collective findings are outlined and analyzed, particularly in the context of detailed SERS measurements and standardization. The review also provides insights into future challenges and perspectives in the field of SERS. It is our hope that this comprehensive review will serve as a valuable reference for researchers seeking to embark on in-depth studies and applications involving their own SERS substrates.

A nanogap-enhanced SERS nanotag-based lateral flow assay for ultrasensitive and simultaneous monitoring of SARS-CoV-2 S and NP antigens

A lateral flow assay (LFA) strip based on dual 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB)-encoded satellite Fe3O4@Au (Mag@Au) SERS tags with nanogap is reported for  ultrasensitive and simultaneous diagnosis of two SARS-CoV-2 functional proteins. Composed of Fe3O4 core, satellite gold shell with nanogaps, and double-layer DTNB, the Mag@Au nanoparticles with an average size of 238 nm were designed as multifunctional tags to efficiently enrich the target SARS-CoV-2 protein from complex samples, significantly enhancing the SERS signal of the LFA strip and provide quantitative SERS detection of analyte on test lines. The developed dual DTNB-encoded satellite Mag@Au-based LFA allowed simultaneous quantification of spike (S) protein and nucleocapsid (NP) protein with detection limits of 23 pg mL-1 and 2 pg mL-1, respectively, lower than commercial ELISA kits and reported SERS-LFA detection system-based Au NPs and Fe3O4@3 nm Au MNPs. This magnetic SERS-LFA also showed high performance of multi-variant strain detection and further distinguished clinical samples of Omicron variant infection, demonstrating the potential of in situ detection of respiratory virus diseases.

Rapid classification of SARS-CoV-2 variant strains using machine learning-based label-free SERS strategy

The spread of COVID-19 over the past three years is largely due to the continuous mutation of the virus, which has significantly impeded global efforts to prevent and control this epidemic. Specifically, mutations in the amino acid sequence of the surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have directly impacted its biological functions, leading to enhanced transmission and triggering an immune escape effect. Therefore, prompt identification of these mutations is crucial for formulating targeted treatment plans and implementing precise prevention and control measures. In this study, the label-free surface-enhanced Raman scattering (SERS) technology combined with machine learning (ML) algorithms provide a potential solution for accurate identification of SARS-CoV-2 variants. We establish a SERS spectral database of SARS-CoV-2 variants and demonstrate that a diagnostic classifier using a logistic regression (LR) algorithm can provide accurate results within 10 min. Our classifier achieves 100% accuracy for Beta (B.1.351/501Y.V2), Delta (B.1.617), Wuhan (COVID-19) and Omicron (BA.1) variants. In addition, our method achieves 100% accuracy in blind tests of positive and negative human nasal swabs based on the LR model. This method enables detection and classification of variants in complex biological samples. Therefore, ML-based SERS technology is expected to accurately discriminate various SARS-CoV-2 variants and may be used for rapid diagnosis and therapeutic decision-making.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning

Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.

SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics

In this study, the intrinsic surface-enhanced Raman spectroscopy (SERS)-based approach coupled with chemometric analysis was adopted to establish the biochemical fingerprint of SARS-CoV-2 infected human fluids: saliva and nasopharyngeal swabs. The numerical methods, partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC), facilitated the spectroscopic identification of the viral-specific molecules, molecular changes, and distinct physiological signatures of pathetically altered fluids. Next, we developed the reliable classification model for fast identification and differentiation of negative CoV(-) and positive CoV(+) groups. The PLS-DA calibration model was described by a great statistical value-RMSEC and RMSECV below 0.3 and R²_cal at the level of ~0.7 for both type of body fluids. The calculated diagnostic parameters for SVMC and PLS-DA at the stage of preparation of calibration model and classification of external samples simulating real diagnostic conditions evinced high accuracy, sensitivity, and specificity for saliva specimens. Here, we outlined the significant role of neopterin as the biomarker in the prediction of COVID-19 infection from nasopharyngeal swab. We also observed the increased content of nucleic acids of DNA/RNA and proteins such as ferritin as well as specific immunoglobulins. The developed SERS for SARS-CoV-2 approach allows: (i) fast, simple and non-invasive collection of analyzed specimens; (ii) fast response with the time of analysis below 15 min, and (iii) sensitive and reliable SERS-based screening of COVID-19 disease.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

A Multiplexed SERS Microassay for Accurate Detection of SARS-CoV-2 and Variants of Concern

Severe acute respiratory syndrome coronavirus 2 variants play an important role in predicting patient outcome during postinfection, and with growing fears of COVID-19 reservoirs in domestic and wild animals, it is necessary to adapt detection systems for variant detection. However, variant-specific detection remains challenging. Surface-enhanced Raman scattering is a sensitive and multiplexing technique that allows the simultaneous detection of multiple targets for accurate identification. Here we propose the development of a multiplex SERS microassay to detect both the spike and nucleocapsid structural proteins of SARS-CoV-2. The designed SERS microassay integrates gold-silver hollow nanobox barcodes and electrohydrodynamically induced nanomixing which in combination enables highly specific and sensitive detection of SARS-CoV-2 and the S-protein epitopes to delineate between ancestral prevariant strains with the newer variants of concern, Delta and Omicron. The microassay allows detection from as low as 20 virus/μL and 50 pg/mL RBD protein and can clearly identify the virus among infected versus healthy nasopharyngeal swabs, with the potential to identify between variants. The detection of both S- and N-proteins of SARS-CoV-2 and the differentiation of variants on the SERS microassay can aid the early detection of COVID-19 to reduce transmission rates and lead into adequate treatments for those severely affected by the virus.