Surface-Enhanced Raman Spectroscopy: Current Understanding, Challenges, and Opportunities.

While surface-enhanced Raman spectroscopy (SERS) has experienced substantial advancements since its discovery in the 1970s, it is an opportunity to celebrate achievements, consider ongoing endeavors, and anticipate the future trajectory of SERS. In this perspective, we encapsulate the latest breakthroughs in comprehending the electromagnetic enhancement mechanisms of SERS, and revisit CT mechanisms of semiconductors. We then summarize the strategies to improve sensitivity, selectivity, and reliability. After addressing experimental advancements, we comprehensively survey the progress on spectrum-structure correlation of SERS showcasing their important role in promoting SERS development. Finally, we anticipate forthcoming directions and opportunities, especially in deepening our insights into chemical or biological processes and establishing a clear spectrum-structure correlation.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Simple Matrices.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for highly sensitive qualitative and quantitative analyses of trace targets. However, sensitive SERS detection can only be facilitated with a suitable sample pretreatment in fields related to trace amounts for food safety and clinical diagnosis. Currently, the sample pretreatment for SERS detection is normally borrowed and improved from the ones in the lab, which yields a high recovery but is tedious and time-consuming. Rapid detection of trace targets in a complex environment is still a considerable issue for SERS detection. Herein, we proposed a liquid-liquid extraction method coupled with a back-extraction method for sample pretreatment based on the pH-sensitive reversible phase transition of the weak organic acids and bases, where the lowest detectable concentrations were identical before and after the pretreatment process. The sensitive (µg L-1 level) and rapid (within 5 min) SERS detection of either koumine, a weak base, or celastrol, a weak acid, was demonstrated in different drinking water samples and beverages. Furthermore, target generality was demonstrated for a variety of weak acids and bases (2 < pKa < 12), and the hydrophilicity/hydrophobicity of the target determines the pretreatment efficiency. Therefore, the LLE-BE coupled SERS was developed as an easy, rapid, and low-cost tool for the trace detection of the two types of targets in simple matrices, which paved the way toward trace targets in complex matrices.

Plasmonic-mediated SC arylation and SS coupling on nanostructured silver electrodes monitored by in situ surface-enhanced Raman spectroscopy.

Plasmon-mediated chemical reaction (PMCR) is a highly attractive field of research. Here we report in situ surface-enhanced Raman spectroscopic (SERS) monitoring of plasmonic-mediated SS bond-forming reaction. The reaction is thought to be a self-coupling reaction proceeding by photoinduced aromatic SC bond arylation. Surprisingly, the SC arylation and SS coupling are found to be occurred on both partially oxidized silver and silver nanoparticles. The results demonstrated that silver oxide or hydroxide and small molecule donor sacrifice agent played a crucial role in the reaction. This work facilitates the in-situ manipulation and characterization of the active silver electrode interface in conjunction with electrochemistry, and also establishes a promising new guideline for surface plasmon resonance photocatalytic reactions on metal nanostructures with high efficiency.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Positively Charged Silver and Gold Nanoparticles with Controllable Size Distribution for SERS Detection of Negatively Charged Molecules.

Surface-enhanced Raman spectroscopy (SERS) has been demonstrated as an ultrasensitive tool for various molecules. However, for the negatively charged molecules, the widely used SERS substrate [negatively charged Ag and Au nanoparticles (Ag or Au NPs (-)] showed either low sensitivity or poor stability. The best solution is to synthesize positively charged silver or gold nanoparticles [Ag or Au NPs (+)] with high stability and excellent SERS performance, which are currently unavailable. To this end, we revitalized the strategy of "charge reversal and seed growth". By selection of ascorbic acid as the reductant and surfactant, the surface charge of Ag or Au NP (-) seeds is adjusted to a balanced state, where the surface charge is negative enough to satisfy the stabilization of the NPs (-) but does not hinder the subsequent charge reversal. By optimization of the chain length and electric charge of polyamine molecules, the highly stable and size-controllable uniform Ag NPs (+) and Au NPs (+) were seed-growth synthesized with high reproducibility. More importantly, the SERS performance of both Ag NPs (+) and Au NPs (+) achieved the trace detection of negatively charged molecules at the level of 1 µg/L, demonstrating an improved SERS sensitivity of up to 3 orders of magnitude compared to the previously reported sensitivity. Promisingly, the introduction of polyamine-capped Ag NPs (+) and Au NPs (+) as SERS substrates with high stability (1 year shelf life) will significantly broaden the application of SERS.

Seeing Is Not Necessarily Believing: Is the Surface-Enhanced Raman Spectroscopy Signal Really from the Target?

Reagent purity is crucial to experimental research, considering that the ignorance of ultratrace impurities may induce wrong conclusions in either revealing the reaction nature or qualifying the target. Specifically, in the field of surface science, the strong interaction between the impurity and the surface will bring a non-negligible negative effect. Surface-enhanced Raman spectroscopy (SERS) is a highly surface-sensitive technique, providing fingerprint identification and near-single molecule sensitivity. In the SERS analysis of trace chloromethyl diethyl phosphate (DECMP), we figured out that the SERS performance of DECMP is significantly distorted by the trace impurities from DECMP. With the aid of gas chromatography-based techniques, one strongly interfering impurity (2,2-dichloro-N,N-dimethylacetamide), the byproduct during the synthesis of DECMP, was confirmed. Furthermore, the nonignorable interference of impurities on the SERS measurement of NaBr, NaI, or sulfadiazine was also observed. The generality ignited us to refresh and consolidate the guideline for the reliable SERS qualitative analysis, by which the potential misleading brought by ultratrace impurities, especially those strongly adsorbed on Au or Ag surfaces, could be well excluded.

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning.

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.

Resolving nanostructure and chemistry of solid-electrolyte interphase on lithium anodes by depth-sensitive plasmon-enhanced Raman spectroscopy.

The solid-electrolyte interphase (SEI) plays crucial roles for the reversible operation of lithium metal batteries. However, fundamental understanding of the mechanisms of SEI formation and evolution is still limited. Herein, we develop a depth-sensitive plasmon-enhanced Raman spectroscopy (DS-PERS) method to enable in-situ and nondestructive characterization of the nanostructure and chemistry of SEI, based on synergistic enhancements of localized surface plasmons from nanostructured Cu, shell-isolated Au nanoparticles and Li deposits at different depths. We monitor the sequential formation of SEI in both ether-based and carbonate-based dual-salt electrolytes on a Cu current collector and then on freshly deposited Li, with dramatic chemical reconstruction. The molecular-level insights from the DS-PERS study unravel the profound influences of Li in modifying SEI formation and in turn the roles of SEI in regulating the Li-ion desolvation and the subsequent Li deposition at SEI-coupled interfaces. Last, we develop a cycling protocol that promotes a favorable direct SEI formation route, which significantly enhances the performance of anode-free Li metal batteries.

1D Gradient-Weighted Class Activation Mapping, Visualizing Decision Process of Convolutional Neural Network-Based Models in Spectroscopy Analysis.

Being characterized by the self-adaption and high accuracy, the deep learning-based models have been widely applied in the 1D spectroscopy-related field. However, the "black-box" operation and "end-to-end" working style of the deep learning normally bring the low interpretability, where a reliable visualization is highly demanded. Although there are some well-developed visualization methods, such as Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM), for the 2D image data, they cannot correctly reflect the weights of the model when being applied to the 1D spectral data, where the importance of position information is not considered. Here, aiming at the visualization of Convolutional Neural Network-based models toward the qualitative and quantitative analysis of 1D spectroscopy, we developed a novel visualization algorithm (1D Grad-CAM) to more accurately display the decision-making process of the CNN-based models. Different from the classical Grad-CAM, with the removal of the gradient averaging (GAP) and the ReLU operations, a significantly improved correlation between the gradient and the spectral location and a more comprehensive spectral feature capture were realized for 1D Grad-CAM. Furthermore, the introduction of difference (purity or linearity) and feature contribute in the CNN output in 1D Grad-CAM achieved a reliable evaluation of the qualitative accuracy and quantitative precision of CNN-based models. Facing the qualitative and adulteration quantitative analysis of vegetable oils by the combination of Raman spectroscopy and ResNet, the visualization by 1D Grad-CAM well reflected the origin of the high accuracy and precision brought by ResNet. In general, 1D Grad-CAM provides a clear vision about the judgment criterion of CNN and paves the way for CNN to a broad application in the field of 1D spectroscopy.

A Clinical Prediction Model of Overall Survival for Patients with Cervical Cancer Aged 25-69 Years.

Aims: This study aims to develop a prediction tool for the overall survival of cervical cancer patients. Methods: We obtained 4116 female patients diagnosed with cervical cancer aged 25-69 during 2008-2019 from the Surveillance, Epidemiology, and End Results Program. The overall survival between groups was illustrated by the Kaplan-Meier method and compared by a log-rank test adjusted by the Bonferroni-Holm method. We first performed the multivariate Cox regression analysis to evaluate the predictive values of the variables. A prediction model was created using cox regression based on the training set, and the model was presented as a nomogram. The proposed nomogram was designed to predict the 1-year, 3-year, and 5-year overall survival of patients with cervical cancer. Besides the c-index, time-dependent receiver operating curves, and calibration curves were created to evaluate the accuracy of the nomogram at the timepoint of one year, three years, and five years. Results: With a median follow-up of 54 (28, 92) months, 1045 (25.39%) patients were deceased. Compared with alive individuals, the deceased were significantly older and the primary site was more likely to be the cervix uteri site, large tumor size, higher grade, and higher combined summary stage (all p values < 0.001). In the multivariate Cox regression, age at diagnosis, race, tumor size, grade, combined summary stage, pathology, and surgery treatment were significantly associated with the all-cause mortality for patients with cervical cancer. The proposed nomogram showed good performance with a C-index of 0.82 in the training set. The 1-year, 3-year, and 5-year areas under the curves (with 95% confidence interval) of the receiver operating curves were 0.88 (0.84, 0.91), 0.84 (0.81, 0.87), and 0.83 (0.80, 0.86), respectively. Conclusions: This study develops a prediction nomogram model for the overall survival of cervical cancer patients with a good performance. Further studies are required to validate the prediction model further.

NR1H4-mediated circRHOBTB3 modulates the proliferation, metastasis, and Warburg effects of cervical cancer through interacting with IGF2BP3.

Globally, cervical cancer (CC) ranks as the fourth most common cancer and the most lethal malignancy among females of reproductive age. The incidence of CC is increasing in low-income countries, with unsatisfactory outcomes and long-term survival for CC patients. Circular RNAs (CircRNAs) are promising therapeutics that target multiple cancers. In this study, we investigated the tumorigenic role of circRHOBTB3 in CC, showing that circRHOBTB3 is highly expressed in CC cells and circRHOBTB3 knockdown also repressed CC proliferation, migration, invasion, and the Warburg effects. CircRHOBTB3 interacted with the RNA-binding protein, IGF2BP3, to stabilize its expression in CC cells and is putatively transcriptionally regulated by NR1H4. In conclusion, this novel NR1H4/circRHOBTB3/IGF2BP3 axis may provide new insights into CC pathogenesis.

Microelectrode-Based Electrochemical Impedance Determination of Brain-Derived Neurotrophic Factor in Aqueous Humor for Diagnosis of Glaucoma.

The abundance of brain-derived neurotrophic factor (BDNF) in aqueous humor (AH) is an ideal biomarker for the diagnosis of glaucoma, a chronic progressive optic neuropathy and the most frequent cause of irreversible blindness. The difficulty of AH-based BDNF detection is from the small amount of extracted AH in a paracentesis (<100 µL) and the ultra-low abundance of BDNF. In this work, we systematically studied the non-specific adsorption of biofluids on the bare gold electrode by electrochemistry and Raman spectroscopy techniques, revealing the unexpected negative correlation of the extent of non-specific adsorption with the size of the electrode. Based on it, a simple microelectrode-based sensor without the introduction of the blocking layer was developed for the detection of BDNF in the AH sample. Using electrochemical impedance spectroscopy (EIS) and extracting the changes of electron-transfer resistance of the electrochemical probe [Fe(CN)6]3-/4- on the sensor surface, the BDNF was quantified. The dynamic range was from 0.5 to 50 pg·mL-1, with a detection limit of 0.3 pg·mL-1 and a sample consumption of 5 µL. The real AH sample analysis confirmed the significant decrease of BDNF abundance in the AH of glaucoma patients. Our microelectrode-based EIS sensor displayed prominent advantages on simplified preparation, sensitive response, and low sample consumption. This AH-based BDNF analysis is expected to be used for the screening and diagnosis of glaucoma, especially for the high-risk population who have ocular diseases and have to undergo surgeries.

Correlation coefficient-directed label-free characterization of native proteins by surface-enhanced Raman spectroscopy.

Investigation of proteins in their native state is the core of proteomics towards better understanding of their structures and functions. Surface-enhanced Raman spectroscopy (SERS) has shown its unique advantages in protein characterization with fingerprint information and high sensitivity, which makes it a promising tool for proteomics. It is still challenging to obtain SERS spectra of proteins in the native state and evaluate the native degree. Here, we constructed 3D physiological hotspots for a label-free dynamic SERS characterization of a native protein with iodide-modified 140 nm Au nanoparticles. We further introduced the correlation coefficient to quantitatively evaluate the variation of the native degree, whose quantitative nature allows us to explicitly investigate the Hofmeister effect on the protein structure. We realized the classification of a protein of SARS-CoV-2 variants in 15 min, which has not been achieved before. This study offers an effective tool for tracking the dynamic structure of proteins and biomedical research.

[Rapid Detection of Trace Enrofloxacin and Ciprofloxacin in Drinking Water by SERS].

In recent years, the abuse of antibiotics has led to the spread and diffusion of antibiotic resistance genes in the environment, which poses a potential threat to the ecosystem and human health. In particular, the related reports of antibiotic contamination in drinking water have aroused great social concerns. Therefore, realizing the rapid detection of trace antibiotics in emergency events has become a research hotspot. Here, in combination with magnetic solid phase extraction (MSPE), we established a rapid detection strategy for ng·L-1 level quinolones in drinking water using surface-enhanced Raman spectroscopy (SERS). With the help of the high enrichment capacity provided by the high adsorption capacity of the magnetic graphene oxide composite nanomaterial (Fe3O4@SiO2-GO), the spiked detection of 1.0 ng·L-1 enrofloxacin (ENR) and 5.0 ng·L-1 ciprofloxacin (CIP) in drinking water was successfully achieved, with recoveries ranging from 77.5% to 91.5%, which met the current requirements of drinking water testing. For environmental water samples such as lake water, the selectivity of extraction materials needs to be further improved due to the strong interference of the complex organic matrix.

Revealing Trace Nanoplastics in Food Packages─An Electrochemical Approach Facilitated by Synergistic Attraction of Electrostatics and Hydrophobicity.

Most food packages are made of plastics, nanoplastics released from which can be directly ingested and induce serious damage to organisms. Therefore, it is urgent to develop an effective and convenient method for nanoplastic determinations in food packages. In this work, we present a sandwich-based electrochemical strategy for nanoplastic determination. Positively charged Au nanoparticles were coated onto a Au electrode to selectively capture negatively charged nanoplastics in an aqueous environment. Subsequently, the nanoplastics were recognized by the signal molecule ferrocene via the hydrophobic interaction and determined by differential pulse voltammetry. Our sandwich-type detection depends on both electronegativity and hydrophobicity of nanoplastics, which make the method applicable for the assays of packages made of widely commercialized polystyrene (PS), polypropylene (PP), polyethylene (PE), and polyamide (PA). The method displays different sensitivities to above four nanoplastics but the same dynamic range from 1 to 100 µg·L-1. Based on it, the nanoplastics released from several typical food packages were assayed. Teabags were revealed with significant nanoplastic release, while instant noodle boxes, paper cups, and take-out boxes release slightly. The good recoveries in nanoplastic-spiked samples confirm the accuracy and applicability of this method. This work provides a sensitive, low-cost, and simple method without complicated instruments and pretreatment, which is of great significance for the determination of nanoplastics released from food packages.

Progress of advanced nanomaterials in diagnosis of neurodegenerative diseases.

Neurodegenerative diseases (NDDs) encompass a wide range of clinically and pathologically diverse diseases characterized by progressive long-term cognitive decline, memory and function loss in daily life. Due to the lack of effective drugs and therapeutic strategies for preventing or delaying neurodegenerative progression, it is urgent to diagnose NDDs as early and accurately as possible. Nanomaterials, emerged as one of the most promising materials in the 21st century, have been widely applied and play a significant role in diagnosis and treatment of NDDs because of their remarkable properties including stability, prominent biocompatibility, unique structure, novel physical and chemical characteristics. In this review, we outlined general strategies for the application of different types of advanced materials in early and staged diagnosis of NDDs in vivo and in vitro. According to applied technology, in vivo research mainly involves magnetic resonance, fluorescence, and surface enhanced Raman imaging on structures of brain tissues, cerebral vessels and related distributions of biomarkers. In vitro research is focused on the detection of fluid biomarkers in cerebrospinal fluid and peripheral blood based on fluorescence, electrochemical, Raman and surface plasmon resonance techniques. Finally, we discussed the current challenges and future perspectives of biomarker-based NDDs diagnosis as well as potential applications regarding advanced nanomaterials.

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS.

Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.