Quantification of Analyte Concentration in the Single Molecule Regime Using Convolutional Neural Networks

RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components

Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

AI-enhanced biomedical micro/nanorobots in microfluidics

Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.

Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets. However, the high dimensionality of SERS data poses a challenge for traditional ML techniques, which can be prone to overfitting and poor generalization. Non-negative matrix factorization (NMF) reduces the dimensionality of SERS data while preserving information content. In this paper, we compared the performance of ML methods including convolutional neural network (CNN), support vector regression, and extreme gradient boosting combined with and without NMF for spectral unmixing of four-way multiplexed SERS spectra from iMS assays used for miRNA detection. CNN achieved high accuracy in spectral unmixing. Incorporating NMF before CNN drastically decreased memory and training demands without sacrificing model performance on SERS spectral unmixing. Additionally, models were interpreted using gradient class activation maps and partial dependency plots to understand predictions. These models were used to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, performed most accurately with RMSElabel = 0.101 and 9.68 × 10-2, respectively. We demonstrated that CNN-based ML shows great promise in spectral unmixing of multiplexed SERS spectra, and the effect of dimensionality reduction on performance and training speed.

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.

Modern Potentiometric Biosensing Based on Non-Equilibrium Measurement Techniques

Modern potentiometric sensors based on polymeric membrane ion-selective electrodes (ISEs) have achieved new breakthroughs in sensitivity, selectivity, and stability and have extended applications in environmental surveillance, medical diagnostics, and industrial analysis. Moreover, nonclassical potentiometry shows promise for many applications and opens up new opportunities for potentiometric biosensing. Here, we aim to provide a concept to summarize advances over the past decade in the development of potentiometric biosensors with polymeric membrane ISEs. This Concept article articulates sensing mechanisms based on non-equilibrium measurement techniques. In particular, we emphasize new trends in potentiometric biosensing based on attractive dynamic approaches. Representative examples are selected to illustrate key applications under zero-current conditions and stimulus-controlled modes. More importantly, fruitful information obtained from non-equilibrium measurements with dynamic responses can be useful for artificial intelligence (AI). The combination of ISEs with advanced AI techniques for effective data processing is also discussed. We hope that this Concept will illustrate the great possibilities offered by non-equilibrium measurement techniques and AI in potentiometric biosensing and encourage further innovations in this exciting field.

Nitroaromatic explosives' detection and quantification using an attention-based transformer on surface-enhanced Raman spectroscopy maps

Rapidly and accurately detecting and quantifying the concentrations of nitroaromatic explosives is critical for public health and security. Among existing approaches, explosives' detection with Surface-Enhanced Raman Spectroscopy (SERS) has received considerable attention due to its high sensitivity. Typically, a preprocessed single spectrum that is the average of the entire or a selected subset of a SERS map is used to train various machine learning models for detection and quantification. Designing an appropriate averaging and preprocessing procedure for SERS maps across different concentrations is time-consuming and computationally costly, and the averaging of spectra may lead to the loss of crucial spectral information. We propose an attention-based vision transformer neural network for nitroaromatic explosives' detection and quantification that takes raw SERS maps as the input without any preprocessing. We produce two novel SERS datasets, 2,4-dinitrophenols (DNP) and picric acid (PA), and one benchmark SERS dataset, 4-nitrobenzenethiol (4-NBT), which have repeated measurements down to concentrations of 1 nM to illustrate the detection limit. We experimentally show that our approach outperforms or is on par with the existing methods in terms of detection and concentration prediction accuracy. With the produced attention maps, we can further identify the regions with a higher signal-to-noise ratio in the SERS maps. Based on our findings, the molecule of interest detection and concentration prediction using raw SERS maps is a promising alternative to existing approaches.

Semi-Supervised Deep Learning-Based Multi-component Spectral Calibration Modeling for UV-vis and Near-Infrared Spectroscopy without Information Loss

Spectral analysis is an important method for characterizing and identifying chemical species. However, quantitative spectral analysis of multiple chemical properties in the real world has always been a challenging problem due to the strong correlation, massive noise, and serious information overlapping of the spectral features. Here, we present a new semi-supervised spectral calibration method based on information lossless decoupling of spectral features named NICEM. To realize the separation and extraction of key latent features, the method uses the flow-based model non-linear independent component estimation (NICE) to learn the sample distribution. The spectral data information is transformed into independent latent variables obeying Gaussian distribution by the reversible structure of deep network without information loss, so as to find the essential properties and realize the feature nonlinear decomposition. Moreover, the association between the input latent feature variables and attributes is evaluated by the maximum mutual information coefficient to eliminate the adverse effects of irrelevant information in the latent variable space and mine key information. Since the latent variables are independent in each dimension, the NICEM method is easier to establish an accurate semi-supervised multi-component calibration model even for high overlapping and complex spectral data. The applicability of the proposed spectral modeling method is demonstrated by using three ultraviolet-visible and near-infrared spectral data sets with 15 physical and chemical properties including diesel fuels, corn, and multi-metal ions solution. Results show that the proposed NICEM method has the highest determination coefficient (R2) and significantly improves extrapolation compared with the seven state-of-the-art methods. The proposed method is intuitive because it obviates complex feature engineering and prior knowledge and is a promising spectral calibration tool for quantitative analysis in other spectroscopy applications.

Recent Advances of Biosensors for Detection of Multiple Antibiotics

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.

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.

Machine learning for nanoplasmonics

Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

Decoupling Individual Optical Nanosensor Responses Using a Spin-Coated Hydrogel Platform

Significant advances have been made in fields such as nanotechnology and biomedicine using the unique properties of single-walled carbon nanotubes (SWCNTs). Specifically, SWCNTs are used as near-infrared fluorescence sensors in the solution phase to detect a wide array of biologically relevant analytes. However, solution-based sensing has several limitations, including limited sensitivity and poor spatial resolution. We have therefore devised a new spin-coated poly(ethylene glycol) diacrylate (PEG-DA) hydrogel platform to examine individual DNA-functionalized SWCNTs (DNA-SWCNTs) in their native aqueous state and have subsequently used this platform to investigate the temporal modulations of each SWCNT in response to a model analyte. A strong surfactant, sodium deoxycholate (SDC), was chosen as the model analyte as it rapidly exchanges with DNA oligonucleotides on the SWCNT surface, modulating several optical properties of the SWCNTs and demonstrating multiparameter analyte detection. Upon addition of SDC, we observed time-dependent spectral modulations in the emission center wavelengths and peak intensities of the individual SWCNTs, indicative of a DNA-to-surfactant exchange process. Interestingly, we found that the modulations in the peak intensities, as determined by kinetic data, were significantly delayed when compared to their center wavelength counterparts, suggesting a potential decoupling of the response of these two spectral features. We used a 1-D diffusion model to relate the local SDC concentration to the spectral response of each SWCNT and created dose-response curves. The peak intensity shifts at a higher SDC concentration than the center wavelength, indicating a potential change in the conformation of the surfactant molecules adsorbed to the SWCNT sidewall after the initial exchange process. This platform allows for a unique single-molecule analysis technique that is significantly more sensitive and modifiable than utilizing SWCNTs in the solution phase.

Wearable artificial intelligence biosensor networks

The demand for high-quality healthcare and well-being services is remarkably increasing due to the ageing global population and modern lifestyles. Recently, the integration of wearables and artificial intelligence (AI) has attracted extensive academic and technological attention for its powerful high-dimensional data processing of wearable biosensing networks. This work reviews the recent developments in AI-assisted wearable biosensing devices in disease diagnostics and fatigue monitoring demonstrating the trend towards personalised medicine with highly efficient, cost-effective, and accurate point-of-care diagnosis by finding hidden patterns in biosensing data and detecting abnormalities. The reliability of adaptive learning and synthetic data and data privacy still need further investigation to realise personalised medicine in the next decade. Due to the worldwide popularity of smartphones, they have been utilised for sensor readout, wireless data transfer, data processing and storage, result display, and cloud server communication leading to the development of smartphone-based biosensing systems. The recent advances have demonstrated a promising future for the healthcare system because of the increasing data processing power, transfer efficiency and storage capacity and diversifying functionalities.

Microfluidic SERS devices: brightening the future of bioanalysis

A new avenue has opened up for applications of surface-enhanced Raman spectroscopy (SERS) in the biomedical field, mainly due to the striking advantages offered by SERS tags. SERS tags provide indirect identification of analytes with rich and highly specific spectral fingerprint information, high sensitivity, and outstanding multiplexing potential, making them very useful in in vitro and in vivo assays. The recent and innovative advances in nanomaterial science, novel Raman reporters, and emerging bioconjugation protocols have helped develop ultra-bright SERS tags as powerful tools for multiplex SERS-based detection and diagnosis applications. Nevertheless, to translate SERS platforms to real-world problems, some challenges, especially for clinical applications, must be addressed. This review presents the current understanding of the factors influencing the quality of SERS tags and the strategies commonly employed to improve not only spectral quality but the specificity and reproducibility of the interaction of the analyte with the target ligand. It further explores some of the most common approaches which have emerged for coupling SERS with microfluidic technologies, for biomedical applications. The importance of understanding microfluidic production and characterisation to yield excellent device quality while ensuring high throughput production are emphasised and explored, after which, the challenges and approaches developed to fulfil the potential that SERS-based microfluidics have to offer are described.

Toward Quantitative Surface-Enhanced Raman Scattering with Plasmonic Nanoparticles: Multiscale View on Heterogeneities in Particle Morphology, Surface Modification, Interface, and Analytical Protocols

Surface-enhanced Raman scattering (SERS) provides significantly enhanced Raman scattering signals from molecules adsorbed on plasmonic nanostructures, as well as the molecules' vibrational fingerprints. Plasmonic nanoparticle systems are particularly powerful for SERS substrates as they provide a wide range of structural features and plasmonic couplings to boost the enhancement, often up to >10⁸-10¹⁰. Nevertheless, nanoparticle-based SERS is not widely utilized as a means for reliable quantitative measurement of molecules largely due to limited controllability, uniformity, and scalability of plasmonic nanoparticles, poor molecular modification chemistry, and a lack of widely used analytical protocols for SERS. Furthermore, multiscale issues with plasmonic nanoparticle systems that range from atomic and molecular scales to assembled nanostructure scale are difficult to simultaneously control, analyze, and address. In this perspective, we introduce and discuss the design principles and key issues in preparing SERS nanoparticle substrates and the recent studies on the uniform and controllable synthesis and newly emerging machine learning-based analysis of plasmonic nanoparticle systems for quantitative SERS. Specifically, the multiscale point of view with plasmonic nanoparticle systems toward quantitative SERS is provided throughout this perspective. Furthermore, issues with correctly estimating and comparing SERS enhancement factors are discussed, and newly emerging statistical and artificial intelligence approaches for analyzing complex SERS systems are introduced and scrutinized to address challenges that cannot be fully resolved through synthetic improvements.

Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSE_label = 6.42 × 10^-2. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method

Raman spectroscopy is a spectroscopic technique typically used to determine vibrational modes of molecules and is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified. With the help of deep learning, Raman spectroscopy can be analyzed more efficiently and thus provide more accurate molecular information. However, no general neural network is designed for one-dimensional Raman spectral data so far. Furthermore, different combinations of hyperparameters of neural networks lead to results with significant differences, so the optimization of hyperparameters is a crucial issue in deep learning modeling. In this work, we propose a deep learning model designed for Raman spectral data and a hyperparameter optimization method to achieve its best performance, i.e., a method based on the simulated annealing algorithm to optimize the hyperparameters of the model. The proposed model and optimization method have been fully validated in a glioma Raman spectroscopy dataset. Compared with other published methods including linear regression, support vector regression, long short-term memory, VGG and ResNet, the mean squared error is reduced by 0.1557 while the coefficient determination is increased by 0.1195 on average.

Deep Learning for Raman Spectroscopy: A Review

Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.

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.

Deep Learning-Based Spectral Extraction for Improving the Performance of Surface-Enhanced Raman Spectroscopy Analysis on Multiplexed Identification and Quantitation

Surface-enhanced Raman spectroscopy (SERS) has been recognized as a promising analytical technique for its capability of providing molecular fingerprint information and avoiding interference of water. Nevertheless, direct SERS detection of complicated samples without pretreatment to achieve the high-efficiency identification and quantitation in a multiplexed way is still a challenge. In this study, a novel spectral extraction neural network (SENN) model was proposed for synchronous SERS detection of each component in mixed solutions using a demonstration sample containing diquat dibromide (DDM), methyl viologen dichloride (MVD), and tetramethylthiuram disulfide (TMTD). A SERS spectra dataset including 3600 spectra of DDM, MVD, TMTD, and their mixtures was first constructed to train the SENN model. After the training step, the cosine similarity of the SENN model can achieve 0.999, 0.997, and 0.994 for DDM, MVD, and TMTD, respectively, which means that the spectra extracted from the mixture are highly consistent with those collected by the SERS experiment of the corresponding pure samples. Furthermore, a convolutional neural network model for quantitative analysis is combined with the SENN, which can simultaneously and rapidly realize the qualitative and quantitative SERS analysis of mixture solutions with lower than 8.8% relative standard deviation. The result demonstrates that the proposed strategy has great potential in improving SERS analysis in environmental monitoring, food safety, and so on.

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning

Raman spectroscopy, as a label-free detection technology, has been widely used in tumor diagnosis. However, most tumor diagnosis procedures utilize multivariate statistical analysis methods for classification, which poses a major bottleneck toward achieving high accuracy. Here, we propose a concept called the two-dimensional (2D) Raman figure combined with convolutional neural network (CNN) to improve the accuracy. Two-dimensional Raman figures can be obtained from four transformation methods: spectral recurrence plot (SRP), spectral Gramian angular field (SGAF), spectral short-time Fourier transform (SSTFT), and spectral Markov transition field (SMTF). Two-dimensional CNN models all yield more than 95% accuracy, which is higher than the PCA-LDA method and the Raman-spectrum-CNN method, indicating that 2D Raman figure inputs combined with CNN may be one reason for gaining excellent performances. Among 2D-CNN models, the main difference is the conversion, where SRP is based on the structure of wavenumber series with the best performances (98.9% accuracy, 99.5% sensitivity, 98.3% specificity), followed by SGAF on the wavenumber series, SSTFT on wavenumber and intensity information, and SMTF on wavenumber position information. The inclusion of external information in the conversion may be another reason for improvement in the accuracy. The excellent capability shows huge potential for tumor diagnosis via 2D Raman figures and may be applied in other spectroscopy analytical fields.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Large-Area Interfaces for Single-Molecule Label-free Bioelectronic Detection

Bioelectronic transducing surfaces that are nanometric in size have been the main route to detect single molecules. Though enabling the study of rarer events, such methodologies are not suited to assay at concentrations below the nanomolar level. Bioelectronic field-effect-transistors with a wide (μm²-mm²) transducing interface are also assumed to be not suited, because the molecule to be detected is orders of magnitude smaller than the transducing surface. Indeed, it is like seeing changes on the surface of a one-kilometer-wide pond when a droplet of water falls on it. However, it is a fact that a number of large-area transistors have been shown to detect at a limit of detection lower than femtomolar; they are also fast and hence innately suitable for point-of-care applications. This review critically discusses key elements, such as sensing materials, FET-structures, and target molecules that can be selectively assayed. The amplification effects enabling extremely sensitive large-area bioelectronic sensing are also addressed.

Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks

A critical study of CNN networks for Raman regression problems is presented. In evaluating performance on models where spectral information is missing, CNN performs as well as state-of-the-art methods, without the need for spectral pre-processing.

Artificial intelligence-assisted enumeration of ultra-small viruses with dual dark-field plasmon resonance probes

Direct visual enumeration of viruses under dark-field microscope (DFM) using plasmon resonance probes (PRPs) is fast and convenient; however, it is greatly limited in the assay of real samples because of its inability to accurately identify false positives owing to non-specific adsorption. In this study, we propose an artificial intelligence (AI)-assisted DFM enumeration strategy for the accurate assay of Enterovirus A71 (an ultra-small human virus) using two PRPs; a 40 nm silver nanoparticle probe (SNP) that appears bright blue under DFM, and a 120 nm gold nanorod probe (GNP) that appears red under DFM. The capture chip was prepared by immobilizing the SNPs with antibodies on the glass to capture the target virus and to form dichromatic sandwich structures with the GNPs, followed by imaging under a dark field (DF). Subsequently, the DF images of the capture chip were subjected to a two-step screening: first, using image processing, and thereafter using the AI algorithm screening to eliminate false positive results and background noise. The results revealed that the data from the AI-assisted dual PRPs assay were highly consistent with those of quantitative PCR (qPCR), and that the sensitivity with a minimum detectable concentration of 3 copies/μL was 5 times higher than that of qPCR. The entire analysis was completed within 45 min. Therefore, our AI-assisted virus enumeration strategy with two DF PRPs holds great potential for ultra-sensitive and accurate quantification of viruses in real samples.

Catching COVID: Engineering Peptide-Modified Surface-Enhanced Raman Spectroscopy Sensors for SARS-CoV-2

COVID-19 remains an ongoing issue across the globe, highlighting the need for a rapid, selective, and accurate sensor for SARS-CoV-2 and its emerging variants. The chemical specificity and signal amplification of surface-enhanced Raman spectroscopy (SERS) could be advantageous for developing a quantitative assay for SARS-CoV-2 with improved speed and accuracy over current testing methods. Here, we have tackled the challenges associated with SERS detection of viruses. As viruses are large, multicomponent species, they can yield different SERS signals, but also other abundant biomolecules present in the sample can generate undesired signals. To improve selectivity in complex biological environments, we have employed peptides as capture probes for viral proteins and developed an angiotensin-converting enzyme 2 (ACE2) mimetic peptide-based SERS sensor for SARS-CoV-2. The unique vibrational signature of the spike protein bound to the peptide-modified surface is identified and used to construct a multivariate calibration model for quantification. The sensor demonstrates a 300 nM limit of detection and high selectivity in the presence of excess bovine serum albumin. This work provides the basis for designing a SERS-based assay for the detection of SARS-CoV-2 as well as engineering SERS biosensors for other viruses in the future.

Catching COVID: Engineering Peptide-Modified Surface-Enhanced Raman Spectroscopy Sensors for SARS-CoV-2

COVID-19 remains an ongoing issue across the globe, highlighting the need for a rapid, selective, and accurate sensor for SARS-CoV-2 and its emerging variants. The chemical specificity and signal amplification of surface-enhanced Raman spectroscopy (SERS) could be advantageous for developing a quantitative assay for SARS-CoV-2 with improved speed and accuracy over current testing methods. Here, we have tackled the challenges associated with SERS detection of viruses. As viruses are large, multicomponent species, they can yield different SERS signals, but also other abundant biomolecules present in the sample can generate undesired signals. To improve selectivity in complex biological environments, we have employed peptides as capture probes for viral proteins and developed an angiotensin-converting enzyme 2 (ACE2) mimetic peptide-based SERS sensor for SARS-CoV-2. The unique vibrational signature of the spike protein bound to the peptide-modified surface is identified and used to construct a multivariate calibration model for quantification. The sensor demonstrates a 300 nM limit of detection and high selectivity in the presence of excess bovine serum albumin. This work provides the basis for designing a SERS-based assay for the detection of SARS-CoV-2 as well as engineering SERS biosensors for other viruses in the future.

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network

In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrate that the use of CNN could efficiently overcome the complex matrix effects, and thus allows for on-site Li quantitative determination in brine samples by LIBS. Specifically, two CNN models with different input data (M-CNN with matrix emission lines, and DP-CNN with double Li lines) were constructed based on the primary matrix features on spectrum and Boltzmann equation, respectively. It was observed that DP-CNN model could greatly improve the accuracy of Li analysis. We also compared the quantitative analysis capabilities of DP-CNN model with partial least squares regression (PLSR) and principal component analysis-support vector regression (PCA-SVR) model, and the results clearly showed DP-CNN offers the best quantification results (higher accuracy and less matrix interference). Finally, five real brine samples were successfully analyzed by the proposed DP-CNN model, confirming by the average absolute error of the prediction of 0.28 mg L^-1 and the average relative error of 3.48%. These results clearly demonstrate that input data plays an important role in the training of CNN model in LIBS analysis, and the proposed DP-CNN provides an effective approach to solve the matrix effects encountered in LIBS for Li measurement in brine samples.

Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope

The electrochemical biosensors are a class of biosensors which convert biological information such as analyte concentration that is a biological recognition element (biochemical receptor) into current or voltage. Electrochemical biosensors depict propitious diagnostic technology which can detect biomarkers in body fluids such as sweat, blood, feces, or urine. Combinations of suitable immobilization techniques with effective transducers give rise to an efficient biosensor. They have been employed in the food industry, medical sciences, defense, studying plant biology, etc. While sensing complex structures and entities, a large data is obtained, and it becomes difficult to manually interpret all the data. Machine learning helps in interpreting large sensing data. In the case of biosensors, the presence of impurity affects the performance of the sensor and machine learning helps in removing signals obtained from the contaminants to obtain a high sensitivity. In this review, we discuss different types of biosensors along with their applications and the benefits of machine learning. This is followed by a discussion on the challenges, missing gaps in the knowledge, and solutions in the field of electrochemical biosensors. This review aims to serve as a valuable resource for scientists and engineers entering the interdisciplinary field of electrochemical biosensors. Furthermore, this review provides insight into the type of electrochemical biosensors, their applications, the importance of machine learning (ML) in biosensing, and challenges and future outlook.

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives

With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

Biosensors are essential tools which have been traditionally used to monitor environmental pollution and detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication-enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to, potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization-induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors, as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology-enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications. The article aims to provide the reader a holistic overview of different nanostructures which have been used for biosensing purposes along with some specific applications in the field of cancer detection and the Internet of things (IoT), as well as a brief overview of machine-learning-based biosensing.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Tap water fingerprinting using a convolutional neural network built from images of the coffee-ring effect

A low-cost tap water fingerprinting technique was evaluated using the coffee-ring effect, a phenomenon by which tap water droplets leave distinguishable "fingerprint" residue patterns after water evaporates. Tap waters from communities across southern Michigan dried on aluminum and photographed with a cell phone camera and 30× loupe produced unique and reproducible images. A convolutional neural network (CNN) model was trained using the images from the Michigan tap waters, and despite the small size of the image dataset, the model assigned images into groups with similar water chemistry with 80% accuracy. Synthetic solutions containing only the majority species measured in Detroit, Lansing, and Michigan State University tap waters did not display the same residue patterns as collected waters; thus, the lower concentration species also influence the tap water "fingerprint". Residue pattern images from salt mixtures with an array of sodium, calcium, magnesium, chloride, bicarbonate, and sulfate concentrations were analyzed by measuring features observed in the photographs as well as using principal component analysis (PCA) on the image files and particles measurements. These analyses together highlighted differences in the residue patterns associated with the water chemistry in the sample. The results of these experiments suggest that the unique and reproducible residue patterns of tap water samples that can be imaged with a cell phone camera and a loupe contain a wealth of information about the overall composition of the tap water, and thus, the phenomenon should be further explored for potential use in low-cost tap water fingerprinting.

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.