SERS-based lateral flow assay combined with machine learning for highly sensitive quantitative analysis of Escherichia coli O157:H7

Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10-6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R2) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.

Lateral flow assays: Progress and evolution of recent trends in point-of-care applications

Paper based point-of-care (PoC) detection platforms applying lateral flow assays (LFAs) have gained paramount approval in the diagnostic domain as well as in environmental applications owing to their ease of utility, low cost, and rapid signal readout. It has centralized the aspect of self-evaluation exhibiting promising potential in the last global pandemic era of Covid-19 implementing rapid management of public health in remote areas. In this perspective, the present review is focused towards landscaping the current framework of LFAs along with integration of components and characteristics for improving the assay by pushing the detection limits. The review highlights the synergistic aspects of assay designing, sample enrichment strategies, novel nanomaterials-based signal transducers, and high-end analytical techniques that contribute significantly towards sensitivity and specificity enhancement. Various recent studies are discussed supporting the innovations in LFA systems that focus upon the accuracy and reliability of rapid PoC testing. The review also provides a comprehensive overview of all the possible difficulties in commercialization of LFAs subjecting its applicability to pathogen surveillance, water and food testing, disease diagnostics, as well as to agriculture and environmental issues.

Re-understanding of SERS for General and Standardized Quantitative Analysis

We proposed two physical concepts, i.e., an intramolecular relative cross section (RCS) and an intermolecular relative scattering ability (RSA), to re-understand and re-describe surface-enhanced Raman scattering (SERS) and established a general SERS quantification theory. Interestingly, RCS and RSA are intrinsic factors and are experimentally measurable to form datasheets of molecules, namely, SERS cards, with which a standard SERS quantification procedure was established. The validity of the theory and quantification procedure was confirmed by experiments. Surprisingly, RCS and RSA are also valid for complex systems being considered as virtual molecules and are experimentally measurable. This simplifies complex systems into analyte-virtual molecule binary systems. With this consideration, trace-level mitoxantrone (a typical cancer drug metabolite) in artificial urine was accurately predicted. The theory, the SERS cards, the standard quantification procedure, and the virtual molecule concept pave a way toward quantitative and standardized SERS spectroscopy in dealing with real-world problems and complex samples.

Application of artificial intelligence in cancer diagnosis and tumor nanomedicine

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

Review of Detection Limits for Various Techniques for Bacterial Detection in Food Samples

Foodborne illnesses can be infectious and dangerous, and most of them are caused by bacteria. Some common food-related bacteria species exist widely in nature and pose a serious threat to both humans and animals; they can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacterial detection are of paramount importance in food safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical methods have been widely used in food safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical methods for various bacterial species (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)), considering different food sample types, analytical performances and the reported limit of detection (LOD), are discussed. It was found that the bacteria species and food sample type contributed significantly to the analytical performance and LOD. Detection via LFIA has a higher average LOD (24 CFU/mL) than detection via electrochemical methods (12 CFU/mL) and PCR (6 CFU/mL). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. Gold and iron nanoparticles were the most studied in the reported articles for LFIA, and average LODs were 26 CFU/mL and 12 CFU/mL, respectively. The electrochemical method revealed that the average LOD was highest for cyclic voltammetry (CV) at 18 CFU/mL, followed by electrochemical impedance spectroscopy (EIS) at 12 CFU/mL and differential pulse voltammetry (DPV) at 8 CFU/mL. LOD usually decreases when the sample number increases until it remains unchanged. Exponential relations (R2 > 0.95) between LODs of Listeria in milk via LFIA and via the electrochemical method with sample numbers have been obtained. Finally, the review discusses challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve analytical performance for bacterial detection.

Digital Surface-Enhanced Raman Spectroscopy-Lateral Flow Test Dipstick: Ultrasensitive, Rapid Virus Quantification in Environmental Dust

This study introduces a novel surface-enhanced Raman spectroscopy (SERS)-based lateral flow test (LFT) dipstick that integrates digital analysis for highly sensitive and rapid viral quantification. The SERS-LFT dipsticks, incorporating gold-silver core-shell nanoparticle probes, enable pixel-based digital analysis of large-area SERS scans. Such an approach enables ultralow-level detection of viruses that readily distinguishes positive signals from background noise at the pixel level. The developed digital SERS-LFTs demonstrate limits of detection (LODs) of 180 fg for SARS-CoV-2 spike protein, 120 fg for nucleocapsid protein, and 7 plaque forming units for intact virus, all within <30 min. Importantly, digital SERS-LFT methods maintain their robustness and their LODs in the presence of indoor dust, thus underscoring their potential for accurate and reliable virus diagnosis and quantification in real-world environmental settings.

AI-Enhanced Visual-Spectral Synergy for Fast and Ultrasensitive Biodetection of Breast Cancer-Related miRNAs

In biomedical testing, artificial intelligence (AI)-enhanced analysis has gradually been applied to the diagnosis of certain diseases. This research employs AI algorithms to refine the precision of integrative detection, encompassing both visual results and fluorescence spectra from lateral flow assays (LFAs), which signal the presence of cancer-linked miRNAs. Specifically, the color shift of gold nanoparticles (GNPs) is paired with the red fluorescence from nitrogen vacancy color centers (NV-centers) in fluorescent nanodiamonds (FNDs) and is integrated into LFA strips. While GNPs amplify the fluorescence of FNDs, in turn, FNDs enhance the color intensity of GNPs. This reciprocal intensification of fluorescence and color can be synergistically augmented with AI algorithms, thereby improving the detection sensitivity for early diagnosis. Supported by the detection platform based on this strategy, the fastest detection results with a limit of detection (LOD) at the fM level and the R2 value of ∼0.9916 for miRNA can be obtained within 5 min. Meanwhile, by labeling the capture probes for miRNA-21 and miRNA-96 (both of which are early indicators of breast cancer) on separate T-lines, simultaneous detection of them can be achieved. The miRNA detection methods employed in this study may potentially be applied in the future for the early detection of breast cancer.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

New application of bimetallic Ag/Pt nanoplates in a colorimetric biosensor for specific detection of E. coli in water

A fast and sensitive aptasensor was developed using nanoplates with peroxidase activity as a novel approach. E. coli detection is described using a silver/platinum nanoplate (Ag/Pt NPL) that interacts with an oligonucleotide aptamer as a bioreceptor. The size of the Ag/Pt NPLs was about 42 nm according to the FE-SEM images. The EDS result indicates that a thin layer of Pt ions was coated on the surface of the Ag NPLs. This nanobiosensor has the ability to specifically bind to E. coli, increasing the peroxidase activity of the apt-Ag/Pt NPL. Finally, the blue color of the solution in the contaminated water samples was increased in the presence of 3,3',5,5'-tetramethylbenzidine (TMB) as a substrate and H2O2. The assay can be completed in 30 min and the presence of E. coli levels can be distinguished with the naked eye. The absorbance at 652 nm is proportional to pathogen concentration from 10 to 108 CFU·mL-1, with a detection limit of 10 CFU·mL-1. The percent recovery for the water samples spiked with E. coli is 95%. The developed assay should serve as a general platform for detecting other pathogenic bacteria which affect water and food quality. The proposed E. coli detection strategy has appealing characteristics such as high sensitivity, simple operation, short testing time, and low cost.

Recent advances in SERS-based immunochromatographic assay for pathogenic microorganism diagnosis: A review

Infectious diseases caused by bacteria, viruses, fungi, and other pathogenic microorganisms are among the most harmful public health problems in the world, causing tens of millions of deaths and incalculable economic losses every year. The establishment of rapid, simple, and highly sensitive diagnostic methods for pathogenic microorganisms is important for the prevention and control of infectious diseases, guidance of timely treatment, and the reduction of public safety risks. Lateral flow immunoassay (LFA) based on the colorimetric signal of colloidal gold is the most popular point-of-care testing technology at present, but it is limited by poor sensitivity and low throughput and hardly meets the needs of the highly sensitive screening of pathogenic microorganisms. In recent years, the combination of surface-enhanced Raman scattering (SERS) and LFA technology has developed into a novel analytical platform with high sensitivity and multiple detection capabilities and has shown great advantages in the detection of pathogenic microorganisms and infectious diseases. This review summarizes the working principle, design ideas, and application of the existing SERS-based LFA methods in pathogenic microorganism detection and further introduces the effect of new technologies such as Raman signal encoding, magnetic enrichment, novel membrane nanotags, and integrated Raman reading equipment on the performance of SERS-LFA. Finally, the main challenges and the future direction of development in this field of SERS-LFA are discussed.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Nanostructured biosensing platforms for the detection of food- and water-borne pathogenic Escherichia coli

Pathogenic bacterial infection is one of the principal causes affecting human health and ecosystems. The accurate identification of bacteria in food and water samples is of significant interests to maintain safety and health for humans. Culture-based tests are practically tedious and may produce false-positive results, while viable but non-culturable microorganisms (NCMs) cannot be retrieved. Thus, it requires fast, reliable, and low-cost detection strategies for on-field analysis and point-of-care (POC) monitoring. The standard detection methods such as nucleic acid analysis (RT-PCR) and enzyme-linked immunosorbent assays (ELISA) are still challenging in POC practice due to their time-consuming (several hours to days) and expensive laboratory operations. The optical (surface plasmon resonance (SPR), fluorescence, and surface-enhanced Raman scattering (SERS)) and electrochemical-based detection of microbes (early stage of infective diseases) have been considered as alternative routes in the emerging world of nanostructured biosensing since they can attain a faster and concurrent screening of several pathogens in real samples. Moreover, optical and electrochemical detection strategies are opening a new route for the ability of detecting pathogens through the integration of cellphones, which is well fitted for POC analysis. This review article covers the current state of sensitive mechanistic approaches for the screening and detection of Escherichia coli O157:H7 (E. coli) pathogens in food and water samples, which can be potentially applied in clinical and environmental monitoring.

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications across multiple disciplines. However, onsite detection with SERS is still limited, given the unwanted glitches of signal reliability and blinking. SERS has inherent signal fluctuations due to multiple factors such as analyte adsorption, inhomogeneous distribution of hotspots, molecule orientation etc. making it a stochastic process. Given these signal fluctuations, validating a signal as a representation of the analyte often relies on an expert's knowledge. Here we present a neural network-aided SERS model (NNAS) without expert interference to efficiently identify reliable SERS spectra of trace explosives (tetryl and picric acid) and a dye molecule (crystal violet). The model uses the signal-to-noise ratio approach to label the spectra as representative (RS) and non-representative (NRS), eliminating the reliability of the expert. Further, experimental conditions were systematically varied to simulate general variations in SERS instrumentation, and a deep-learning model was trained. The model has been validated with a validation set followed by out-of-sample testing with an accuracy of 98% for all the analytes. We believe this model can efficiently bridge the gap between laboratory and on-site detection using SERS.

SERS-based immunosensor for E. coli contaminants detection in milk using silver-coated nanoporous silicon substrates

The dairy sector is frequently affected by contagious and environmental factors that spread between animals by numerous means and induce the inflammatory disease of bovine mastitis (BM). Herein, silver decorated porous silicon (Ag-pSi) SERS platform was designed for rapid and reliable Escherichia coli (predominant BM pathogen) detection in various milk origins. The inherent surface void and pore morphology were physically optimized to augment the SERS effect using 4-aminothiphenol (4ATP) while achieving an enhancement factor >4.6 × 10⁷. An indirect immunoassay evaluated the residual unreacted antibodies using an optimized 4ATP/Ag-pSi SERS platform modified with secondary antibodies. Under optimized conditions, the porous substrate offered high sensitivity toward target bacteria detection of 3 CFU mL^-1 and linear response of 10¹-10⁵ CFU mL^-1. Moreover, the selectivity and specificity of the designed sensing platform were cross-validated against other interfering bacteria without compromising its performance efficiencies. Finally, the applicability of the developed system for real-life conditions was elucidated in different milk samples (bovine, goat, sheep) with recovery values of 78-115% compared to the conventional culture technique. Considering the complex media analysis, the miniaturized SERS platform is highly reliable, rapid and accurate that could be applicable for routine on-site analysis of various emerging pathogens relevant to BM management.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Recent advances of Au@Ag core-shell SERS-based biosensors

The methodological advancements in surface-enhanced Raman scattering (SERS) technique with nanoscale materials based on noble metals, Au, Ag, and their bimetallic alloy Au-Ag, has enabled the highly efficient sensing of chemical and biological molecules at very low concentration values. By employing the innovative various type of Au, Ag nanoparticles and especially, high efficiency Au@Ag alloy nanomaterials as substrate in SERS based biosensors have revolutionized the detection of biological components including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc. This review is about SERS-based Au/Ag bimetallic biosensors and their Raman enhanced activity by focusing on different factors related to them. The emphasis of this research is to describe the recent developments in this field and conceptual advancements behind them. Furthermore, in this article we apex the understanding of impact by variation in basic features like effects of size, shape varying lengths, thickness of core-shell and their influence of large-scale magnitude and morphology. Moreover, the detailed information about recent biological applications based on these core-shell noble metals, importantly detection of receptor binding domain (RBD) protein of COVID-19 is provided.

Multifunctional Au@Pt@Ag NPs with color-photothermal-Raman properties for multimodal lateral flow immunoassay

Multimodal lateral flow immunoassay (LFIA) has displayed its potential to improve practicability and elasticity of point-of-care testing. Herein, multifunctional core-shell-shell Au@Pt@Ag NPs loaded with dual-layer Raman reporter molecules of 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB) with a characteristic combination of color-photothermal-Raman performance were constructed for colorimetric LFIA (CM-LFIA), photothermal LFIA (PT-LFIA) and surface-enhanced Raman scattering-based LFIA (SERS-LFIA), respectively. The highly specific nanoprobes, being obtained through the combination of the resulted dual-layer DTNB modified Au@Pt@Ag NPs with the antibody, were triumphantly utilized in exploring multimodal LFIA with one visual qualitative and two optional quantitative modes with excellent sensing sensitivity. Under optimal conditions, the limit of detection (LOD) for the model hazardous analyte dehydroepiandrosterone (DHEA) were 1.0 ng mL^-1 for CM-LFIA, 0.42 ng mL^-1 for PT-LFIA, and 0.013 ng mL^-1 for SERS-LFIA, three of which were over 100-fold, 200-fold and 7 000-fold more sensitive than conventional visual AuNPs-based LFIA, respectively. In addition, the quantitative PT-LFIA and SERS-LFIA sensors worked well in spiked real samples with acceptable recoveries of 96.2 - 106.7% and 98.2 - 105.2%, respectively. This assay demonstrated that the developed multimodal LFIA had a great potential to be a powerful tool for accurate tracing hazardous analytes in complex samples.

Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma

Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.

How Surface-Enhanced Raman Spectroscopy Could Contribute to Medical Diagnoses

In the last decade, there has been a rapid increase in the number of surface-enhanced Raman scattering (SERS) spectroscopy applications in medical research. In this article we review some recent, and in our opinion, most interesting and promising applications of SERS spectroscopy in medical diagnostics, including those that permit multiplexing within the range important for clinical samples. We focus on the SERS-based detection of markers of various diseases (or those whose presence significantly increases the chance of developing a given disease), and on drug monitoring. We present selected examples of the SERS detection of particular fragments of DNA or RNA, or of bacteria, viruses, and disease-related proteins. We also describe a very promising and elegant ‘lab-on-chip’ approach used to carry out practical SERS measurements via a pad whose action is similar to that of a pregnancy test. The fundamental theoretical background of SERS spectroscopy, which should allow a better understanding of the operation of the sensors described, is also briefly outlined. We hope that this review article will be useful for researchers planning to enter this fascinating field.

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates

Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an R² error of 0.9629 for CV and 0.9472 for PA. In conjunction with a portable Raman spectrometer and a computation time of less than <10 s, we believe that this is an affordable and rapid method for quantification of analytes using the SERS technique.

Paper-based genetic assays with bioconjugated gold nanorods and an automated readout pipeline

Paper-based biosensors featuring immunoconjugated gold nanoparticles have gained extraordinary momentum in recent times as the platform of choice in key cases of field applications, including the so-called rapid antigen tests for SARS-CoV-2. Here, we propose a revision of this format, one that may leverage on the most recent advances in materials science and data processing. In particular, we target an amplifiable DNA rather than a protein analyte, and we replace gold nanospheres with anisotropic nanorods, which are intrinsically brighter by a factor of ~ 10, and multiplexable. By comparison with a gold-standard method for dot-blot readout with digoxigenin, we show that gold nanorods entail much faster and easier processing, at the cost of a higher limit of detection (from below 1 to 10 ppm in the case of plasmid DNA containing a target transgene, in our current setup). In addition, we test a complete workflow to acquire and process photographs of dot-blot membranes with custom-made hardware and regression tools, as a strategy to gain more analytical sensitivity and potential for quantification. A leave-one-out approach for training and validation with as few as 36 sample instances already improves the limit of detection reached by the naked eye by a factor around 2. Taken together, we conjecture that the synergistic combination of new materials and innovative tools for data processing may bring the analytical sensitivity of paper-based biosensors to approach the level of lab-grade molecular tests.

Highly sensitive time-resolved fluoroimmunoassay for the quantitative onsite detection of Alternaria longipes in tobacco

AIMS

Alternaria longipes is a causal agent of brown spot of tobacco, which remains a serious threat to tobacco production. Herein, we established a detection method for A. longipes in tobacco samples based on the principle of time-resolved fluoroimmunoassay, in order to fulfil the requirement of rapid, sensitive and accurate detection in situ.

METHODS AND RESULTS

A monoclonal antibody against A. longipes was generated, and its purity and titration were assessed using western blot and ELISA. The size of europium (III) nanospheres was measured to confirm successful antibody conjugation. The method described here can detect A. longipes protein lysates as low as 0.78 ng ml-1 , with recovery rates ranging from 85.96% to 99.67% in spiked tobacco. The specificity was also confirmed using a panel of microorganisms.

CONCLUSIONS

The fluorescent strips allow rapid and sensitive onsite detection of A. longipes in tobacco samples, with high accuracy, specificity, and repeatability.

SIGNIFICANCE AND IMPACT OF THE STUDY

This novel detection method provides convenience of using crude samples without complex procedures, and therefore allows rapid onsite detection by end users and quick responses towards A. longipes, which is critical for disease control and elimination of phytopathogens.

SERS-based lateral flow immunoassay for sensitive and simultaneous detection of anti-SARS-CoV-2 IgM and IgG antibodies by using gap-enhanced Raman nanotags

The lateral flow immunoassay (LFIA) has played a crucial role in early diagnosis during the current COVID-19 pandemic owing to its simplicity, speed and affordability for coronavirus antibody detection. However, the sensitivity of the commercially available LFIAs needs to be improved to better prevent the spread of the infection. Here, we developed an ultra-sensitive surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-based LFIA) strip for simultaneous detection of anti-SARS-CoV-2 IgM and IgG by using gap-enhanced Raman nanotags (GERTs). The GERTs with a 1 nm gap between the core and shell were used to produce the "hot spots", which provided about 30-fold enhancement as compared to conventional nanotags. The COVID-19 recombinant antigens were conjugated on GERTs surfaces and replaced the traditional colloidal gold for the Raman sensitive detection of human IgM and IgG. The LODs of IgM and IgG were found to be 1 ng/mL and 0.1 ng/mL (about 100 times decrease was observed as compared to commercially available LFIA strips), respectively. Moreover, under the condition of common nano-surface antigen, precise SERS signals proved the unreliability of quantitation because of the interference effect of IgM on IgG.

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp

Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 10⁴ CFU/mL commercially available lateral-flow assays.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.