Surface-enhanced Raman spectroscopy for the characterization of pellets of biofilm forming bacterial strains of Staphylococcus epidermidis

Rapid and accurate identification of foodborne bacteria: a combined approach using confocal Raman micro-spectroscopy and explainable machine learning

This study proposes a rapid identification method for foodborne pathogens by combining Raman spectroscopy with explainable machine learning. Spectral data of nine common foodborne pathogens are collected using a laser confocal Raman spectrometer, and their characteristic Raman peaks are identified and analyzed. Key spectral features are extracted using competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA), while t-distributed stochastic neighbor embedding (t-SNE) is employed for visualization. Subsequently, classification models, including support vector machine (SVM) and random forest (RF), are developed, and the optimal model is selected based on classification accuracy (ACC), with the RF model achieving a test accuracy of 98.91%. To enhance the interpretability of the model, Shapley Additive exPlanations (SHAP) analysis is applied to evaluate the contribution of each spectral feature to the classification results, identifying critical Raman shifts significantly influencing pathogen classification. The results demonstrate that CARS-SPA feature selection not only improves the accuracy and efficiency of the classification model but also enhances its transparency and reliability. This study optimizes the workflow for food safety testing, reduces the risk of foodborne diseases, and provides robust technical support for public health and safety.

SERS-assisted characterization of cell biomass from biofilm-forming Acinetobacter baumannii strains using chemometric tools

Acinetobacter baumannii (A. baumannii) is an emerging Gram-negative nosocomial pathogen responsible for infection on a global scale. It has the ability to develop biofilms on different surfaces, especially abiotic surfaces, which is considered a major contributor of its pathogenicity. Surface-enhanced Raman spectroscopy (SERS) holds great potential as an effective method for identifying and characterizing the biochemical composition of biofilm-forming species. In this study, cell mass samples from different strains of A. baumannii, categorized based on their biofilm-forming ability (strong, medium and non-biofilm forming) using a 96-well microtiter plate assay (MTP), were analyzed by SERS. The identified spectral features of the SERS spectra were used to characterize bacterial strains capable of producing biofilms. Silver nanoparticles (Ag-NPs) served as the SERS substrate to differentiate biofilm-forming strains of A. baumannii. Chemometric tools, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were employed for the classification and differentiation of SERS spectra from bacterial strains with varying biofilm-producing capacities, achieving 100% sensitivity, 94.3% specificity, and an area under the curve (AUC) value of 0.81 through Monte Carlo cross-validation. Furthermore, K-fold (Leave-K-out cross-validation (LKOCV)) was applied to verify the robustness of the PLS-DA model, and the AUC value was found to be 0.90, with a sensitivity of 100% and specificity of 98%. These results demonstrate that the PLS-DA model is highly effective for the differentiation and classification of bacterial strains with varying capacities for biofilm production.

Evaluation of Cinnamon Essential Oil and Its Emulsion on Biofilm-Associated Components of Acinetobacter baumannii Clinical Strains

BACKGROUND/OBJECTIVES

Acinetobacter baumannii, one of the most dangerous pathogens, is able to form biofilm structures and aggravate its treatment. For that reason, new antibiofilm agents are in need, and new sources of antibiofilm compounds are being sought from plants and their products. Cinnamon essential oil is associated with a wide spectrum of biological activities, but with a further improvement of its physicochemical properties it could provide even better bioavailability. The aim of this work was the evaluation of the antibiofilm properties of cinnamon essential oil and its emulsion.

METHODS

In order to evaluate the antibiofilm activity, crystal violet assay was performed to determine biofilm biomass. The main components of the biofilm matrix were measured as well as the motile capacity of the tested strains. Gene expression was monitored with RT-qPCR, while treated biofilms were observed with Raman spectroscopy.

RESULTS

A particularly strong potential against pre-formed biofilm with a decreased biomass of up to 66% was found. The effect was monitored not only with regard to the whole biofilm biomass, but also on the individual components of the biofilm matrix such as exopolysaccharides, proteins, and eDNA molecules. Protein share drops in treated biofilms demonstrated the most consistency among strains and rose to 75%. The changes in strain motility and gene expressions were investigated after the treatments were carried out. Raman spectroscopy revealed the influence of the studied compounds on chemical bond types and the components present in the biofilm matrix of the tested strains.

CONCLUSIONS

The results obtained from this research are promising regarding cinnamon essential oil and its emulsion as potential antibiofilm agents, so further investigation of their activity is encouraged for their potential use in biomedical applications.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Microfluidics for studying the deep underground biosphere: from applications to fundamentals

In this review, selected examples are presented to demonstrate how microfluidic approaches can be utilized for investigating microbial life from deep geological environments, both from practical and fundamental perspectives. Beginning with the definition of the deep underground biosphere and the conventional experimental techniques employed for these studies, the use of microfluidic systems for accessing critical parameters of deep life in geological environments at the microscale is subsequently addressed (high pressure, high temperature, low volume). Microfluidics can simulate a range of environmental conditions on a chip, enabling rapid and comprehensive studies of microbial behavior and interactions in subsurface ecosystems, such as simulations of porous systems, interactions among microbes/microbes/minerals, and gradient cultivation. Transparent microreactors allow real-time, noninvasive analysis of microbial activities (microscopy, Raman spectroscopy, FTIR microspectroscopy, etc.), providing detailed insights into biogeochemical processes and facilitating pore-scale analysis. Finally, the current challenges and opportunities to expand the use of microfluidic methodologies for studying and monitoring the deep biosphere in real time under deep underground conditions are discussed.

Detection methods for antibiotics in wastewater: a review

Antibiotics are widely used as fungicides because of their antibacterial and bactericidal effects. However, it is necessary to control their dosage. If the amount of antbiotics is too much, it cannot be completely metabolized and absorbed, will pollute the environment, and have a great impact on human health. Many antibiotics usually left in factory or aquaculture wastewater pollute the environment, so it is vital to detect the content of antibiotics in wastewater. This article summarizes several common methods of antibiotic detection and pretreatment steps. The detection methods of antibiotics in wastewater mainly include immunoassay, instrumental analysis method, and sensor. Studies have shown that immunoassay can detect deficient concentrations of antibiotics, but it is affected by external factors leading to errors. The detection speed of the instrumental analysis method is fast, but the repeatability is poor, the price is high, and the operation is complicated. The sensor is a method that is currently increasingly studied, including electrochemical sensors, optical sensors, biosensors, photoelectrochemical sensors, and surface plasmon resonance sensors. It has the advantages of fast detection speed, high accuracy, and strong sensitivity. However, the reproducibility and stability of the sensor are poor. At present, there is no method that can comprehensively integrate the advantages. This paper aims to review the enrichment and detection methods of antibiotics in wastewater from 2020 to the present. It also aims to provide some ideas for future research directions in this field.

Differentiation of different dibenzothiophene (DBT) desulfurizing bacteria via surface-enhanced Raman spectroscopy (SERS)

Fossil fuels are considered vital natural energy resources on the Earth, and sulfur is a natural component present in them. The combustion of fossil fuels releases a large amount of sulfur in the form of SO x in the atmosphere. SO x is the major cause of environmental problems, mainly air pollution. The demand for fuels with ultra-low sulfur is growing rapidly. In this aspect, microorganisms are proven extremely effective in removing sulfur through a process known as biodesulfurization. A major part of sulfur in fossil fuels (coal and oil) is present in thiophenic structures such as dibenzothiophene (DBT) and substituted DBTs. In this study, the identification and characterization of DBT desulfurizing bacteria (Chryseobacterium sp. IS, Gordonia sp. 4N, Mycolicibacterium sp. J2, and Rhodococcus sp. J16) based on their specific biochemical constituents were conducted using surface-enhanced Raman spectroscopy (SERS). By differentiating DBT desulfurizing bacteria, researchers can gain insights into their unique characteristics, thus leading to improved biodesulfurization strategies. SERS was used to differentiate all these species based on their biochemical differences and different SERS vibrational bands, thus emerging as a potential technique. Moreover, multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to differentiate these DBT desulfurizing bacteria on the basis of their characteristic SERS spectral signals. For all these isolates, the accuracy, sensitivity, and specificity are above 90%, and an AUC (area under the curve) value of close to 1 was achieved for all PLS-DA models.

The role of rcpA gene in regulating biofilm formation and virulence in Vibrio parahaemolyticus

Vibrio parahaemolyticus (V. parahaemolyticus) is a common seafood-borne pathogen that can colonize the intestine of host and cause gastroenteritis. Biofilm formation by V. parahaemolyticus enhances its persistence in various environments, which poses a series of threats to food safety. This work aims to investigate the function of rcpA gene in biofilm formation and virulence of V. parahaemolyticus. Deletion of rcpA significantly reduced motility, biofilm biomass, and extracellular polymeric substances, and inhibited biofilm formation on a variety of food and food contact surfaces. In mice infection model, mice infected with ∆rcpA strain exhibited a decreased rate of pathogen colonization, a lower level of inflammatory cytokines, and less tissue damage when compared to mice infected with wild type strain. RNA-seq analysis revealed that 374 genes were differentially expressed in the rcpA deletion mutant, which include genes related to quorum sensing, flagellar system, ribosome, type VI secretion system, biotin metabolism and transcriptional regulation. In conclusion, rcpA plays a role in determining biofilm formation and virulence of V. parahaemolyticus and further research is necessitated to fully understand its function in V. parahaemolyticus.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Recent advances on the formation, detection, resistance mechanism, and control technology of Listeria monocytogenes biofilm in food industry

Listeria monocytogenes is an important foodborne pathogen that causes listeriosis, a severe and fatal condition. Biofilms are communities of microorganisms nested within a self-secreted extracellular polymeric substance, and they protect L. monocytogenes from environmental stresses. Biofilms, once formed, can lead to the persistence of L. monocytogenes in processing equipment and are therefore considered to be a major concern for the food industry. This paper briefly introduces the recent advancements on biofilm formation characteristics and detection methods, and focuses on analysis of the mechanism of L. monocytogenes biofilm resistance; Moreover, this paper also summarizes and discusses the existing different techniques of L. monocytogenes biofilm control according to the physical, chemical, biological, and combined strategies, to provide a theoretical reference to aid the choice of effective control technology in the food industry.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

Surface-enhanced Raman spectroscopy for characterization of supernatant samples of biofilm forming bacterial strains

Staphylococcus epidermidis is considered major cause of nosocomial infections. Its pathogenicity is mainly due to the ability to form biofilms on different surfaces, particularly indwelling medical devices. This bacterium consists of different strains consisting of non, medium and strong biofilm forming ones. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that can be used to detect and analyze biochemical composition of the supernatant samples of different strains of bacteria including non, medium and strong biofilm forming bacterial strains. SERS is a powerful technique for the robust, reliable, rapid detection and discrimination of bacteria in the form of characteristic SERS spectral features which can be used for detection and classification. SERS is used to differentiate three classes of bacteria with respect to their biofilm forming ability. Silver nanoparticles (Ag NPs) are used as SERS substrate and synthesized with chemical reduction method. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are used to discriminate SERS spectral data sets of non, medium and strong biofilm forming bacteria. PLS-DA analysis is a multivariate statistical technique that can be used to analyze data from bacterial sets.

Synthesis, Characterization, and Evaluation of Antifungal Activity of 1-Butyl-3-hexyl-1H-imidazol-2(3H)-selenone by Surface-Enhanced Raman Spectroscopy

In the present research work, a selenium N-heterocyclic carbene (Se-NHC) complex/adduct was synthesized and characterized by using different analytical methods including FT-IR, 1HNMR, and 13CNMR. The antifungal activity of the Se-NHC complex against Aspergillus flavus (A. flavus) fungus was investigated with disc diffusion assay. Moreover, the biochemical changes occurring in this fungus due to exposure of different concentrations of the in-house synthesized compound are characterized by surface-enhanced Raman spectroscopy (SERS) and are illustrated in the form of SERS spectral peaks. SERS analysis yields valuable information about the probable mechanisms responsible for the antifungal effects of the Se-NHC complex. As demonstrated by the SERS spectra, this Se-NHC complex caused denaturation and conformational changes in the proteins as well as decomposition of the fungal cell membrane. The SERS spectra were analyzed using two chemometric tools such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The fungal samples' SERS spectra were differentiated using PCA, while various groups of spectra were discriminated with ultrahigh sensitivity (98%), high specificity (99.7%), accuracy (100%), and area under the receiver operating characteristic curve (87%) using PLS-DA.

Anti-Biofilm Activity of Laurel Essential Oil against Vibrio parahaemolyticus

Vibrio parahaemolyticus is a primary seafood-associated pathogen that could cause gastroenteritis. It can attach to various surfaces and form a biofilm, which poses serious threats to food safety. Hence, an effective strategy is urgently needed to control the biofilm formation of V. parahaemolyticus. Laurel essential oil (LEO) is used in food, pharmaceutical and other industries, and is commonly used as a flavoring agent and valuable spice in food industries. The potential antibiofilm effects of LEO against V. parahaemolyticus were examined in this study. LEO obviously reduced biofilm biomass at subinhibitory concentrations (SICs). It decreased the metabolic activity and viability of biofilm cells. Microscopic images and Raman spectrum indicted that LEO interfered with the structure and biochemical compositions of biofilms. Moreover, it also impaired swimming motility, decreased hydrophobicity, inhibited auto-aggregation and reduced attachment to different food-contact surfaces. RT-qPCR revealed that LEO significantly downregulated transcription levels of biofilm-associated genes of V. parahaemolyticus. These findings demonstrate that LEO could be potentially developed as an antibiofilm strategy to control V. parahaemolyticus biofilms in food industries.

Recent advances of Raman spectroscopy for the analysis of bacteria

Rapid and sensitive bacteria detection and identification are becoming increasingly important for a wide range of areas including the control of food safety, the prevention of infectious diseases, and environmental monitoring. Raman spectroscopy is an emerging technology which provides comprehensive information for the analysis of bacteria in a short time and with high sensitivity. Raman spectroscopy offers many advantages including relatively simple operation, non-destructive analysis, and information on molecular differences between bacteria species and strains. A variety of biochemical properties can be measured in a single spectrum. This short review covers the recent advancements and applications of Raman spectroscopy for bacteria analysis with specific focuses on bacteria detection, bacteria identification and discrimination, as well as bacteria antibiotic susceptibility testing in 2022. The development of novel substrates, the combination with other techniques, and the utilization of advanced data processing tools for the improvement of Raman spectroscopy and future directions are discussed.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.

OBJECTIVES

The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).

METHODOLOGY

Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.

RESULTS

HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.

CONCLUSIONS

Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.

Surface-enhanced Raman spectroscopy for studying the interaction of N-propyl substituted imidazole compound with salmon sperm DNA

BACKGROUND

Surface Enhanced Raman Spectroscopy (SERS) is a very promising and fast technique for studying drugs and for detecting chemical nature of a molecule and DNA interaction. In the current study, SERS is employed to check the interaction of different concentrations of n-propyl imidazole derivative ligand with salmon sperm DNA using silver nanoparticles as SERS substrates.

OBJECTIVES

Multivariate data analysis technique like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) are employed for the detailed analysis of the SERS spectral features associated with the mode of action of the imidazole derivative ligand with DNA.

METHODOLOGY

Silver nanoparticles were used as a SERS substrate in DNA-drug interaction. Five different concentrations of ligands were interacted with DNA and mix with Ag-NPs as substrate. The SERS spectra of were acquired for all seven samples and processed using MATLAB. Additionally, PCA and PLS-DA were used to assessed the ability SERS to differentiate interaction of DNA-drug.

RESULTS

Differentiating SERS features having changes in their peak position and intensities are observed including 629, 655, 791, 807, 859, 1337, 1377 and 1456 cm^-1. These SERS features reveal that binding of ligand with DNA is electrostatic in nature, and have specificity to major groove where it forms GC-CG interstrand cross-linking with the DNA double helix.

CONCLUSIONS

SERS give significant information regarding to Drug-DNA interaction mechanism, SERS spectra inferred the mode of action of anticancer compound that are imidazole in nature.