Determination of latent tuberculosis infection from plasma samples via label-free SERS sensors and machine learning.

Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS). We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 × 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database. We demonstrated that SERS analysis with machine learning is a potential diagnostic tool for LTBI screening.

A novel portable Raman scattering platform for antibiotic screening in pig urine.

Background and Aim: Public health and food safety are gaining attention globally. Consumer health can be protected from chemical residues in meat by early detection or screening for antibiotic residues before selling the meat commercially. However, conventional practices are normally applied after slaughtering, which leads to massive business losses. This study aimed to use portable surface-enhanced Raman spectroscopy (SERS) equipped with multivariate curve resolution-alternation least squares (MCR-ALS) to determine the concentrations of enrofloxacin, oxytetracycline, and neomycin concentrations. This approach can overcome the problems of business loss, costs, and time-consumption, and limit of detection (LOD). Materials and Methods: Aqueous solutions of three standard antibiotics (enrofloxacin, oxytetracycline, and neomycin) with different concentrations were prepared, and the LOD for each antibiotic solution was determined using SERS. Extracted pig urine was spiked with enrofloxacin at concentrations of 10, 20, 50, 100, and 10,000 ppm. These solutions were investigated using SERS and MCR-ALS analysis. Urine samples from pigs at 1 and 7 days after enrofloxacin administration were collected and investigated using SERS and MCR-ALS to differentiate the urinary enrofloxacin concentrations. Results: The LOD of enrofloxacin, oxytetracycline, and neomycin in aqueous solutions were 0.5, 2.0, and 100 ppm, respectively. Analysis of enrofloxacin spiking in pig urine samples demonstrated the different concentrations of enrofloxacin at 10, 20, 50, 100, and 10,000 ppm. The LOD of spiking enrofloxacin was 10 ppm, which was 10 times lower than the regulated value. This technique was validated for the first time using urine collected on days 1 and 7 after enrofloxacin administration. The results revealed a higher concentration of enrofloxacin on day 7 than on day 1 due to consecutive administrations. The observed concentration of enrofloxacin was closely correlated with its circulation time and metabolism in pigs. Conclusion: A combination of SERS sensing platform and MCR-ALS is a promising technique for on-farming screening. This platform can increase the efficiency of antibiotic detection in pig urine at lower costs and time. Expansion and fine adjustments of the Raman dataset may be required for individual farms to achieve higher sensitivity.

Label free detection of multiple trace antibiotics with SERS substrates and independent components analysis.

Surface enhanced Raman spectroscopy (SERS) has been widely studied and recognized as a powerful label-free technique for trace chemical analysis. However, its drawback in simultaneously identifying several molecular species has greatly limited its real-world applications. In this work, we reported a combination between SERS and independent component analysis (ICA) to detect several trace antibiotics which are commonly used in aquacultures, including malachite green, furazolidone, furaltadone hydrochloride, nitrofurantoin, and nitrofurazone. The analysis results indicate that the ICA method is highly effective in decomposing the measured SERS spectra. The target antibiotics could be precisely identified when the number of components and the sign of each independent component loading were properly optimized. With SERS substrates, the optimized ICA can identify trace molecules in a mixture at a concentration of 10-6 M achieving the correlation values to the reference molecular spectra of 71-98%. Furthermore, measurement results obtained from a real-world sample demonstration could also be recognized as an important basis to suggest this method is promising for monitoring antibiotics in a real aquatic environment.

Discriminant Analysis PCA-LDA Assisted Surface-Enhanced Raman Spectroscopy for Direct Identification of Malaria-Infected Red Blood Cells.

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.

An efficient and simple SERS approach for trace analysis of tetrahydrocannabinol and cannabinol and multi-cannabinoid detection.

Many countries have legalized cannabis and its derived products for multiple purposes. Consequently, it has become necessary to develop a rapid, effective, and reliable tool for detecting delta-9-tetrahydrocannabinol (THC) and cannabinol (CBN), which are important biologically active compounds in cannabis. Herein, we have fabricated SERS chips by using glancing angle deposition and tuned dimensions of silver nanorods (AgNRs) for detecting THC and CBN at low concentrations. Experimental and computational results showed that the AgNR substrate with film thickness (or nanorod length) of 150 nm, corresponding to nanorod diameter of 79 nm and gap between nanorods of 23 nm, can effectively sense trace THC and CBN with good reproducibility and sensitivity. Due to limited spectral studies of the cannabinoids in previous reports, this work also explored towards identifying characteristic Raman lines of THC and CBN. This information is critical to further reliable data analysis and interpretation. Moreover, multianalyte detection of THC and CBN in a mixture was successfully demonstrated by applying an open-source independent component analysis (ICA) model. The overall method is fast, sensitive, and reliable for sensing trace THC and CBN. The SERS chip-based method and spectral results here are useful for a variety of cannabis testing applications, such as product screening and forensic investigation.

Dataset of serum proteomic spectra from tuberculosis patients detected by Raman spectroscopy and surface-enhanced Raman spectroscopy.

In this data article, we present Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) data obtained using an InVia Reflex confocal Raman microscope (Renishaw; Wotton-under-Edge, UK) and processed using WiRE™ 4.2 software. The data include RS and SERS spectra detected, after removal of albumin, from the serum proteome of tuberculosis (TB) patient categories and controls (active tuberculosis; ATB, latent tuberculosis; LTBI, TB-exposed persons with undetected infection; EC, healthy controls; HC) using 532 nm and 785 nm laser wavelengths for RS and 785 nm for SERS. The RS and SERS data had high reproducibility (SERS; R2 = 0.988, RS at 785 nm; R2 = 0.972, RS at 532 nm; R2 = 0.9150). This data can be used for analysis of proteomic spectra based on RS and SERS for TB diagnosis and can also be compared to other populations. The spectral dataset based on normal, healthy control groups might be used as the control data for analysis of other diseases using RS and SERS approaches.

Diagnosis of active tuberculosis and latent tuberculosis infection based on Raman spectroscopy and surface-enhanced Raman spectroscopy.

Current tools for screening LTBI are limited due to the long turnaround time required, cross-reactivity of tuberculin skin test to BCG vaccine and the high cost of interferon gamma release assay (IGRA) tests. We evaluated Raman spectroscopy (RS) for serum-protein fingerprinting from 26 active TB (ATB) cases, 20 LTBI cases, 34 early clearance (EC; TB-exposed persons with undetected infection) and 38 healthy controls (HC). RS at 532 nm using candidate peaks provided 92.31% sensitivity and 90.0% to distinguish ATB from LTBI, 84.62% sensitivity and 89.47% specificity to distinguish ATB from HC and 87.10% sensitivity and 85.0% specificity to distinguish LTBI from EC. RS at 532 nm with the random forest model provided 86.84% sensitivity and 65.0% specificity to distinguish LTBI from HC and 94.74% sensitivity and 87.10% specificity to distinguish EC from HC. Using preliminary sample sets (n = 5 for each TB-infection category), surface-enhanced Raman spectroscopy (SERS) showed high potential diagnostic performance, distinguishing very clearly among all TB-infection categories with 100% sensitivity and specificity. With lower cost, shorter turnaround time and performance comparable to that of IGRAs, our study demonstrated RS and SERS to have high potential for ATB and LTBI diagnosis.

Tuberculosis determination using SERS and chemometric methods.

Nanostructures have been multiplying the advantages of Raman spectroscopy and further amplify the advantages of Raman spectroscopy is a continuous effort focused on the appropriate design of nanostructures. Herein, we designed different shapes of plasmonic nanostructures such as Vertical, Zig Zag, Slant nanorods and Spherical nanoparticles employing the DC magnetron sputtering system as SERS-active substrates for ultrasensitive detection of target molecules. The fabricated plasmonic nanostructures sensitivity and uniformity were exploited by reference dye analyte. These nanostructures were utilized in the label free detection of infectious disease, Tuberculosis (TB). For the first time, TB detection from serum samples using SERS has been demonstrated. Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples. The results demonstrate the performance of the SERS spectra, chemometric methods and potential of the method in clinical diagnosis.

Highly-Sensitive Surface-Enhanced Raman Spectroscopy (SERS)-based Chemical Sensor using 3D Graphene Foam Decorated with Silver Nanoparticles as SERS substrate.

In this work, a novel platform for surface-enhanced Raman spectroscopy (SERS)-based chemical sensors utilizing three-dimensional microporous graphene foam (GF) decorated with silver nanoparticles (AgNPs) is developed and applied for methylene blue (MB) detection. The results demonstrate that silver nanoparticles significantly enhance cascaded amplification of SERS effect on multilayer graphene foam (GF). The enhancement factor of AgNPs/GF sensor is found to be four orders of magnitude larger than that of AgNPs/Si substrate. In addition, the sensitivity of the sensor could be tuned by controlling the size of silver nanoparticles. The highest SERS enhancement factor of ∼ 5 × 10(4) is achieved at the optimal nanoparticle size of 50 nm. Moreover, the sensor is capable of detecting MB over broad concentration ranges from 1 nM to 100 µM. Therefore, AgNPs/GF is a highly promising SERS substrate for detection of chemical substances with ultra-low concentrations.

Surface-enhanced Raman scattering using silver nanocluster on anodic aluminum oxide template sensor toward protein detection.

The affordable surface-enhanced Raman scattering (SERS) substrates, with a structure consisting of densely distributed round-shape silver nanoclusters on anodic aluminum oxide (AAO) template, is fabricated by magnetron sputtering and anodization processes. The physical investigations show that the silver nanoclusters with size distribution ranging from 10 to 30 nm uniformly distributed on the top and in the bottom of the AAO nanochannels. The SERS activities from adsorbed probe molecules, i.e., methylene blue, on the SERS substrate surface indicate a high Raman enhancement factor for trace organic analysis. The SERS substrate is successfully utilized in the detection of a trace amount of three different proteins, bovin serum albumin, immunoglobulin G, and cardiac troponin T, also adsorbed on the substrate surface. Several spectral bands containing important molecular structures of these proteins are clearly observed and identified. The obtained results indicated a step forward to label-free biomolecular detections in chip-based biosensors.

Cardiac troponin T detection using polymers coated quartz crystal microbalance as a cost-effective immunosensor.

Cardiac troponin T (cTnT) detection has been the focus of increased interest due to its role in myocardial infarction diagnosis. In this study, we report a relatively low coat technique to detect cTnT using a quartz crystal microbalance (QCM) sensor. A sensitive detection is achieved by introducing a QCM surface with a carboxylic polyvinyl chloride immobilization layer. The surface morphologies of this polymer film under varied deposition thickness have been investigated by field emission scanning electron microscopy and atomic force microscopy. A cTnT detection result from a modified QCM surface can be obtained within a short response time by a direct detection of the immunoreaction and a direct conversion of mass accumulation into a frequency shift, representing a measurable electrical signal. The relationship between the cTnT concentration and the response current from a QCM sensor shows detectability at the concentration of cTnT as low as 5 ng/ml.