Label-free Surface-enhanced Raman Spectroscopy Research Articles

Multiwavelength Surface-Enhanced Raman Scattering Fingerprints of Human Urine for Cancer Diagnosis.

Label-free surface-enhanced Raman spectroscopy (SERS) is capable of capturing rich compositional information from complex biosamples by providing vibrational spectra that are crucial for biosample identification. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Herein, we introduce a multiwavelength SERS approach combined with machine learning (ML)-based classification to improve the discrimination accuracy of human urine specimens for bladder cancer (BCa) diagnosis. This strategy leverages the excitation-wavelength-dependent SERS spectral profiles of complex matrices, which are mainly attributed to wavelength-related vibrational changes in individual analytes and differences in the variation ratios of SERS intensity across different wavelengths among various analytes. By capturing SERS fingerprints under multiple excitation wavelengths, we can acquire more comprehensive and unique chemical information on complex samples. Further experimental examinations with clinical urine specimens, supported by ML algorithms, demonstrate the effectiveness of this multiwavelength strategy and improve the diagnostic accuracy of BCa and staging of its invasion with SERS spectra from increasing numbers of wavelengths. The multiwavelength SERS holds promise as a convenient, cost-effective, and broadly applicable technique for the precise identification of complex matrices and diagnosis of diseases based on body fluids.

Determination of multiple analytes in urine using label-free SERS coupled with simple sample pretreatments

BackgroundA key restriction of label-free surface-enhanced Raman spectroscopy (SERS) in analysis of objects with complex composition (including with several target analytes) is the competition of mixture components for interaction with SERS-active surface. This leads to poor selectivity of the analysis of such mixtures (e.g., body fluids) and the need to use advanced sample pretreatment procedures such as HPLC or TLC. Therefore, this work aims to develop a set of simple and fast pretreatment steps (dilution, pH correction, etc.) to increase the sorption of the target analyte, reduce the sorption of admixtures, and prevent suppression of the target analyte SERS signal. ResultsWe have developed label-free SERS assay suitable for the determination of three analytes (methotrexate, cephalosporin antibiotic, and creatinine) in one real urine sample as a model matrix with complex and deviating composition. The choice of drugs is justified by the need to monitor their concentration in urine during joint drug treatment of cancer patients with concomitant bacterial infection, while monitoring creatinine concentration helps to evaluate kidney function of the patients. Additionally, three cephalosporin representatives were used in the study to maximize versatility of the assay. As a results, the optimized pretreatment steps enable to eliminate the negative influence of excess of interferences (including other analytes) and achieve precise (≤12% RSD) and accurate (88–111% recovery) determination of several analytes in the therapeutically relevant ranges: 300–3000 μg mL–1 for creatinine, 20–200 μg mL–1 for methotrexate and cephalosporins. SignificanceTherefore, in addition to reporting a new SERS assay for the analysis of body fluids, this study clearly demonstrates the importance of taking into account competitive adsorption processes on the SERS substrate surface. We suggest making this practice mandatory when developing any label-free SERS assay because it enables to maximize the selectivity and accuracy of the analysis as well as to simplify the analysis procedure.

Electrochemical potential enhanced EC-SERS sensor for sensitive and label-free detection of acetamiprid

Label-free surface-enhanced Raman spectroscopy (SERS) holds promise for detecting pesticide residues, yet its broader application in food safety is limited by the weak affinity between pesticides and SERS substrates. This study introduces an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) sensor that utilizes potential strengthened molecular interactions and Ag@SiO2 nanospheres as SERS substrates, significantly enhancing the detection sensitivity for acetamiprid (AAP). The dense distribution of silver nanoparticles (Ag NPs) on the SiO2 surfaces creates numerous “hot spots,” significantly improving the SERS performance for AAP detection. A potential of −0.5 V substantially boosts the SERS signal intensity for AAP compared to without applied potential, notably achieving a 4.3-fold increase at the 631 cm−1 signal peak. Under optimal conditions, the EC-SERS method achieved a limit of detection (LOD) for AAP at 0.046 μM, spanning a linear range from 0.05 μM to 0.1 mM, which is 185 times more sensitive than conventional SERS approaches. When applied to vegetable samples, the method showed recoveries between 95.56 % and 109.33 %, with results corroborated by HPLC-MS analysis. Thus, this study provides an effective and facile strategy for the detection of AAP in the food safety field.

Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction

Nasopharyngeal cancer (NPC) is a malignant tumor with high prevalence in Southeast Asia and highly invasive and metastatic characteristics. Radiotherapy is the primary strategy for NPC treatment, however there is still lack of effect method for predicting the radioresistance that is the main reason for treatment failure. Herein, the molecular profiles of patient plasma from NPC with radiotherapy sensitivity and resistance groups as well as healthy group, respectively, were explored by label-free surface enhanced Raman spectroscopy (SERS) based on surface plasmon resonance for the first time. Especially, the components with different molecular weight sizes were analyzed via the separation process, helping to avoid the possible missing of diagnostic information due to the competitive adsorption. Following that, robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA) was employed to extract the feature of blood-SERS data and establish an effective predictive model with the accuracy of 96.7% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100% for identifying the NPC subjects from healthy ones. This work demonstrates the potential of molecular separation-assisted label-free SERS combined with machine learning for NPC screening and treatment strategy guidance in clinical scenario.

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer.

Label-free surface-enhanced Raman spectroscopy analysis method for liquid biopsy and its application in serum-based lung cancer diagnosis and classification

Liquid biopsy offers promise for the diagnosis of malignant tumors or their precancerous lesions at the subclinical stage, which is crucial for improving the survival rate of cancer. Label-free surface-enhanced Raman spectroscopy (SERS) has become an emerging detection method in liquid biopsy. The accuracy of SERS-based diagnosis relies on both precise measurement and effective data analysis. In our previous study, a large laser spot SERS method was proposed for the precise detection of complex liquids. In this work, a logical Raman data analysis method was developed, to address the lack of Raman characteristics and biological significance in current SERS-based diagnosis data analysis. The measured Raman data is classified to obtain a weighting matrix of classification criteria, and this matrix is used to establish a material correspondence with the Raman spectra, extract key Raman peaks, determine corresponding biomolecules and biological processes, thus enabling a logical analysis of the data. This method has been used to analyze the SERS data of lung cancer, it can not only show exceptional performance in diagnosing lung cancer and differentiating small cell lung cancer, but also obtain the Raman diagnostic and classification criteria with clear numerical values and reasonable biological significance, demonstrating its great potential value in precise biosensing.

Oppositely-charged silver nanoparticles enable selective SERS molecular enhancement through electrostatic interactions

Label-free surface-enhanced Raman spectroscopy (SERS) has attracted extensive attention as an emerging technique for molecular phenotyping of biological samples. However, the selective enhancement property of SERS mediated by complicated interactions between substrates and analytes is unfavorable for molecular profiling. The electrostatic force is among the most dominating interactions that can cause selective adsorption of molecules to charged substrates. This means if only negatively- or positively-charged SERS substrates are applied, then considerable SERS information from a portion of analytes would be lost, hindering comprehensive SERS sensing. In this work, we utilize both negatively- and positively-charged colloidal silver (Ag) nanoparticles (NPs) to detect various charged molecules. The negatively-charged citrate-stabilized Ag and the positively-charged Ag prepared via a cetyltrimethyl-ammonium chloride-based charge reversal protocol have been adopted as SERS substrates. The Ag NPs are all relatively well-dispersed with good uniformity. After applying the oppositely-charged NPs to the detection of charged molecules, we find the SERS results explicitly demonstrate the electrostatically-driven SERS selective enhancement, which is further supported and clarified by molecular electrostatic potential calculations. Our work highlights the importance of developing SERS substrates modified with appropriate surface charges for various analytes, and enlightens us that potentially more molecular SERS information can be acquired from complex bio-samples using combinations of oppositely-charged substrates.

Focused ion beam-fabricated nanorod substrate for label-free surface-enhanced Raman spectroscopy and enabling dual virus detection

The COVID-19 pandemic presents global challenges, notably with co-infections in respiratory tract involving SARS-CoV-2 variants and influenza strains. Detecting multiple viruses simultaneously is crucial for accurate diagnosis, effective tracking infectious sources, and containment of the epidemic. This study uses a label-free surface-enhanced Raman spectroscopy (SERS) method using Au NPs/pZrO2 (250) and FIB-made Au NRs (100) to detect dual viruses, including SARS-CoV-2 Delta variant (D) and influenza A (A) or B (B) virus. Results demonstrate distinct peaks facilitating virus differentiation, especially between D and A or B, with clear disparities between substrates; specific peaks at 950 and 1337 cm−1 are pivotal for discerning viruses using Au NPs/pZrO2 (250), while those at 1050, 1394, and 1450 cm−1 and 1033, 1165, 1337, and 1378 cm−1 are key for validation using Au NRs (100). Differences in substrate surface morphology and spatial disposition of accommodating viruses significantly influence hotspot formation and Raman signal amplification efficiency, thereby affecting the ability to distinguish various viruses. Furthermore, both substrates offer insights, even in the presence of oxymetazoline hydrochloride (an interfering substance), with practical implications in viral diagnosis. The customized design and reproducibility underscore efficient Raman signal amplification, even in challenging environments, highlighting potential for widespread virus detection.

Rapid identification of living cancer cells based on label-free surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive vibration spectroscopy technique that can obtain fingerprint information of biomolecules in cells, and it has demonstrated significant potential in the early diagnosis of cancer. Here, we propose a rapid and efficient identification method of living cancer cells (within 10 min), which is based on the label-free SERS detection using iodide-modified Ag nanoparticles (Ag IMNPs) and the ultra-fast Raman mapping by line illumination. The Ag IMNPs achieve a better balance between sensitivity, reproducibility, and biocompatibility when compared to conventional Au or Ag nanoparticles. Statistical analysis of Raman peak frequency and intensity across substantial SERS spectra reveals a wealth of intracellular chemical information about the three cell lines 7701/7703/Caski. This method can not only accurately identify and classify normal cells, cancer cells, and various types of cancer cells, but it can also detect fingerprint changes in the distribution of intracellular chemicals. Furthermore, we discover that, among the three cell lines, liver cancer cells 7703 had the highest level of reactive oxygen species (ROS). This rapid SERS method can provide a new optical detection method for early screening, progression monitoring and prognosis assessment of tumors.

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

Surface-Enhanced Raman Spectroscopy Detection for Fenthion Pesticides Based on Gold Molecularly Imprinted Polymer Solid-State Substrates.

Current label-free surface-enhanced Raman spectroscopy (SERS) assay for the detection and analysis of organophosphorus pesticides has achieved initial success, but the application still faces constraints of substrate portability and specificity. To this end, this paper demonstrates a method for portable, rapid, and specific detection of low concentrations of fenthion pesticides based on a solid substrate of gold nanoparticle monolayers combined with molecularly imprinted polymers (MIPs). The nano-monolayers were transferred to the surface of mercapto-silicon wafers by interfacial self-assembly technique to form a stable connection with S-Au bonds and, at the same time, prevent nanoparticles from dropping off during the surfactant removal process. Then, the fenthion MIPs were directly generated on the surface of the monolayer film by spin-coating with a pre-polymerization solution and ultraviolet-induced polymerization. Tests showed that the molecular imprint was able to accurately bind to fenthion, but not other molecules, in a mixture of structural analogs, achieving a low concentration detection of 10-8 mol/L. The composite substrate maintained a signal uniformity of a relative standard deviation (RSD) = 7.05% and a batch-to-batch reproducibility of RSD = 10.40%, making it a potential pathway for the extended application of SERS technology.

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific “metabolic fingerprints” of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are “in-house” protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.

Clinical feasibility of a label-free SERS assay for therapeutic drug monitoring of methotrexate

We present a therapeutic drug monitoring (TDM) feasibility study using a label-free surface-enhanced Raman spectroscopy (SERS)-based assay for quantifying methotrexate (MTX) from samples collected from children with acute lymphoblastic leukemia undergoing high-dose MTX therapy. We show that, when combined with an appropriate sample preparation (solid phase extraction) and multivariate data analysis (partial least squares regression), the SERS assay demonstrated a good correlation with a reference chromatographic method (r = 0.889, p < 0.005). We also found that the SERS-based approach underestimated MTX concentration, with a 10% bias calculated, but both methods showed similar variability (RSD of 8.4% for HPLC and 11.6% for SERS). The presented results are strong evidence that SERS can be used for TDM of MTX, and this work brings us one step closer to implementing a SERS-based assay in clinics.

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid β (1–42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid β (1–42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.

Advances in uremic toxin detection and monitoring in the management of chronic kidney disease progression to end-stage renal disease.

Patients with end-stage kidney disease (ESKD) rely on dialysis to remove toxins and stay alive. However, hemodialysis alone is insufficient to completely remove all/major uremic toxins, resulting in the accumulation of specific toxins over time. The complexity of uremic toxins and their varying clearance rates across different dialysis modalities poses significant challenges, and innovative approaches such as microfluidics, biomarker discovery, and point-of-care testing are being investigated. This review explores recent advances in the qualitative and quantitative analysis of uremic toxins and highlights the use of innovative methods, particularly label-mediated and label-free surface-enhanced Raman spectroscopy, primarily for qualitative detection. The ability to analyze uremic toxins can optimize hemodialysis settings for more efficient toxin removal. Integration of multiple omics disciplines will also help identify biomarkers and understand the pathogenesis of ESKD, provide deeper understanding of uremic toxin profiling, and offer insights for improving hemodialysis programs. This review also highlights the importance of early detection and improved understanding of chronic kidney disease to improve patient outcomes.

Label-free SERS method with size-matched selectivity for analytes of varying sizes

Label-free Surface-enhanced Raman spectroscopy (SERS) methods have great potential for detecting analytes of various sizes and dimensions with high sensitivity and selectivity. To mitigate the impact of interfering substances, SERS-active substrates with size-matched selectivity were designed by coating Au nanoparticles (Au NPs) on ZrO2 multilayer nanofibers (Au NPs/fZrO2) and referencing with Au NPs embedded on ZrO2 nanobowl (Au NPs/pZrO2). The detection efficiency study covered four small pesticide molecules, two live SARS-CoV-2 virus variants (Alpha and Delta), and three interfering substances. The results demonstrate that the fibrous structure of Au NPs/fZrO2 could effectively wet the sample and detect larger target molecules, such as live SARS-CoV-2 virus, thus improving efficiency by reducing unwanted molecules in the SERS signal. This substrate also showed high discrimination between Alpha and Delta variants. Au NPs/fZrO2 demonstrated similar trace detection capabilities to Au NPs/pZrO2 for pesticide molecules and virus variants; however, it particularly exhibited stronger peak intensities and more unique SERS peaks assigned to the variants. Thus, differences in substrate morphology affect the generation of hotspots and the distance between analyte and hotspots. These findings hold promise for the development of SERS-based label-free analytical method for trace detection of a variety of virus particles.

Label-Free Single-Cell SERS Detection and Fluorescence Imaging of Molecular Responses to Endoplasmic Reticulum Stress under Electrical Stimulation.

The endoplasmic reticulum (ER) is one of the most important organelles in eukaryotic cells, in which most proteins and lipids are synthesized to regulate complex cellular processes. Generally, the excessive accumulation of unfolded or misfolded proteins can disturb ER homeostasis and induce endoplasmic reticulum stress (ERS). Howbeit, the molecular stress responses within ERS and metastatic behaviors of tumor cells during electrical stimulation (ES) are still poorly investigated and remain a challenge. In this study, by the combined use of fluorescence imaging, ER-targeting plasmonic nanoprobes were developed to trace molecular stress response profiling within the ER during a constant-voltage ES process at ∼1 V based on label-free surface-enhanced Raman spectroscopy (SERS). The excess accumulation of β-misfolded proteins was found after the ES, leading to breaking of the ER homeostasis and further inducing mitochondrial dysfunction. Notably, the excessive stress of ER under ES can destroy the calcium ion balance and induce significant upregulation of calreticulin expression. Importantly, the content ratio of two kinds of cadherin between E-cadherin and N-cadherin was gradually improved with the voltages boosted. Meanwhile, the epithelial adhesion factor expression was ascended with voltages amplified, leading to inhibiting tumor cell migration at low voltages or death under higher voltages (∼1 V). This study provides cellular insights into the ES approach for tumor therapy and also provides a simple and effective method for detecting molecular stress responses in endoplasmic reticulum stress.

Detection of micro-plasma-induced exosomes secretion in a fibroblast-melanoma co-culture model

BackgroundMelanoma is a highly aggressive tumor and a significant cause of skin cancer-related death. Timely diagnosis and treatment require identification of specific biomarkers in exosomes secreted by melanoma cells. In this study, label-free surface-enhanced Raman spectroscopy (SERS) method with size-matched selectivity was used to detect membrane proteins in exosomes released from a stimulated environment of fibroblasts (L929) co-cultured with melanoma cells (B16–F10). To promote normal secretion of exosomes, micro-plasma treatment was used to gently induce the co-cultured cells and slightly increase the stress level around the cells for subsequent detection using the SERS method. Results and discussionFirstly, changes in reactive oxygen species/reactive nitrogen species (ROS/RNS) concentrations in the cellular microenvironment and the viability and proliferation of healthy cells are assessed. Results showed that micro-plasma treatment increased extracellular ROS/RNS levels while modestly reducing cell proliferation without significantly affecting cell survival. Secondly, the particle size of secreted exosomes isolated from the culture medium of L929, B16–F10, and co-cultured cells with different micro-plasma treatment time did not increase significantly under single-cell conditions at short treatment time but might be changed under co-culture condition or longer treatment time. Third, for SERS signals related to membrane protein biomarkers, exosome markers CD9, CD63, and CD81 can be assigned to significant Raman shifts in the range of 943–1030 and 1304-1561 cm-1, while the characteristics SERS peaks of L929 and B16–F10 cells are most likely located at 1394/1404, 1271 and 1592 cm-1 respectively. Significance and noveltyTherefore, this micro-plasma-induced co-culture model provides a promising preclinical approach to understand the diagnostic potential of exosomes secreted by cutaneous melanoma/fibroblasts. Furthermore, the label-free SERS method with size-matched selectivity provides a novel approach to screen biomarkers in exosomes secreted by melanoma cells, aiming to reduce the use of labeling reagents and the processing time traditionally required.

Advancing Mycotoxin Detection: Multivariate Rapid Analysis on Corn Using Surface Enhanced Raman Spectroscopy (SERS).

Mycotoxin contamination on food and feed can have deleterious effect on human and animal health. Agricultural crops may contain one or more mycotoxin compounds; therefore, a good multiplex detection method is desirable to ensure food safety. In this study, we developed a rapid method using label-free surface-enhanced Raman spectroscopy (SERS) to simultaneously detect three common types of mycotoxins found on corn, namely aflatoxin B1 (AFB1), zearalenone (ZEN), and ochratoxin A (OTA). The intrinsic chemical fingerprint from each mycotoxin was characterized by their unique Raman spectra, enabling clear discrimination between them. The limit of detection (LOD) of AFB1, ZEN, and OTA on corn were 10 ppb (32 nM), 20 ppb (64 nM), and 100 ppb (248 nM), respectively. Multivariate statistical analysis was used to predict concentrations of AFB1, ZEN, and OTA up to 1.5 ppm (4.8 µM) based on the SERS spectra of known concentrations, resulting in a correlation coefficient of 0.74, 0.89, and 0.72, respectively. The sampling time was less than 30 min per sample. The application of label-free SERS and multivariate analysis is a promising method for rapid and simultaneous detection of mycotoxins in corn and may be extended to other types of mycotoxins and crops.

Diagnosis and staging of cervical cancer using label-free surface-enhanced Raman spectroscopy and BWRPCA-TLNN model

This paper presents a label-free and highly accurate classification serum analytical platform, which will be used to identify cervical cancer at different stages. In detail, the microarray chip fabricated based on the ordered Ag-AuNRs substrate was prepared to measure the surface-enhanced Raman scattering (SERS) spectra of serum of healthy subjects and cervical cancer patients (Ⅰ, Ⅱ, Ⅲ, and Ⅳ), and then a binary weight robust principal component analysis (BWRPCA)-two-layer nearest neighbor (TLNN) model was designed as the diagnosis and recognition model of SERS spectra. The results revealed that the microarray chip can realize rapid, sensitive, high-throughput detection of SERS spectra of serum. The BWRPCA-TLNN successfully differentiated the SERS spectra and accurately captured the key characteristics for classification. The established BWRPCA-TLNN model achieved an excellent classification accuracy of 91.2 %, a diagnostic sensitivity of over 84.0 % and a specificity of over 97.0 %. This exploratory work demonstrated that SERS combined with BWRPCA-TLNN as a diagnostic screening method has potential for improving cervical cancer detection and screening.