scholarly journals Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5519
Author(s):  
Kenneth E. Schackart ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Limit Of Detection ◽  
Principal Component ◽  
Surface Enhanced Raman Spectroscopy ◽  
Support Vector ◽  
Data Repositories ◽  
Wide Range ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Artificial Neural Network Ann

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

scholarly journals Spatial Mapping of Pyocyanin in Pseudomonas Aeruginosa Bacterial Communities Using Surface Enhanced Raman Scattering

2016 ◽  
Vol 71 (2) ◽  
pp. 215-223 ◽  
Author(s):  
Sneha Polisetti ◽  
Nameera F. Baig ◽  
Nydia Morales-Soto ◽  
Joshua D. Shrout ◽  
Paul W. Bohn
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Limit Of Detection ◽  
Principal Component ◽  
Laboratory Strain ◽  
Surface Enhanced Raman Spectroscopy ◽  
Spatiotemporal Mapping ◽  
Surface Enhanced ◽  
Specific Strain ◽  
Surface Enhanced Raman ◽  
Sers Imaging

Surface enhanced Raman spectroscopy (SERS) imaging was used in conjunction with principal component analysis (PCA) for the in situ spatiotemporal mapping of the virulence factor pyocyanin in communities of the pathogenic bacterium Pseudomonas aeruginosa. The combination of SERS imaging and PCA analysis provides a robust method for the characterization of heterogeneous biological systems while circumventing issues associated with interference from sample autofluorescence and low reproducibility of SERS signals. The production of pyocyanin is found to depend both on the growth carbon source and on the specific strain of P. aeruginosa studied. A cystic fibrosis lung isolate strain of P. aeruginosa synthesizes and secretes pyocyanin when grown with glucose and glutamate, while the laboratory strain exhibits detectable production of pyocyanin only when grown with glutamate as the source of carbon. Pyocyanin production in the laboratory strain grown with glucose was below the limit of detection of SERS. In addition, the combination of SERS imaging and PCA can elucidate subtle differences in the molecular composition of biofilms. PCA loading plots from the clinical isolate exhibit features corresponding to vibrational bands of carbohydrates, which represent the mucoid biofilm matrix specific to that isolate, features that are not seen in the PCA loading plots of the laboratory strain.

scholarly journals Surface-enhanced Raman spectroscopy of cell lysates mixed with silver nanoparticles for tumor classification

2017 ◽  
Vol 8 ◽  
pp. 1183-1190 ◽  
Author(s):  
Mohamed Hassoun ◽  
Iwan W.Schie ◽  
Tatiana Tolstik ◽  
Sarmiza E Stanca ◽  
Christoph Krafft ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Silver Nanoparticles ◽  
Cell Lines ◽  
Principal Component ◽  
Surface Enhanced Raman Spectroscopy ◽  
Support Vector ◽  
Cell Identification ◽  
Cell Lysates ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

The throughput of spontaneous Raman spectroscopy for cell identification applications is limited to the range of one cell per second because of the relatively low sensitivity. Surface-enhanced Raman scattering (SERS) is a widespread way to amplify the intensity of Raman signals by several orders of magnitude and, consequently, to improve the sensitivity and throughput. SERS protocols using immuno-functionalized nanoparticles turned out to be challenging for cell identification because they require complex preparation procedures. Here, a new SERS strategy is presented for cell classification using non-functionalized silver nanoparticles and potassium chloride to induce aggregation. To demonstrate the principle, cell lysates were prepared by ultrasonication that disrupts the cell membrane and enables interaction of released cellular biomolecules to nanoparticles. This approach was applied to distinguish four cell lines – Capan-1, HepG2, Sk-Hep1 and MCF-7 – using SERS at 785 nm excitation. Six independent batches were prepared per cell line to check the reproducibility. Principal component analysis was applied for data reduction and assessment of spectral variations that were assigned to proteins, nucleotides and carbohydrates. Four principal components were selected as input for classification models based on support vector machines. Leave-three-batches-out cross validation recognized four cell lines with sensitivities, specificities and accuracies above 96%. We conclude that this reproducible and specific SERS approach offers prospects for cell identification using easily preparable silver nanoparticles.

scholarly journals Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Shizhuang Weng ◽  
Mengqing Qiu ◽  
Ronglu Dong ◽  
Fang Wang ◽  
Jinling Zhao ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Determination ◽  
Limit Of Detection ◽  
Extraction Procedure ◽  
Surface Enhanced Raman Spectroscopy ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Chlormequat Chloride ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25 μg/g, which was far below the maximum residual value in wheat of China. Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm−1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.

scholarly journals Differentiation of different antifungals with various mechanisms using dynamic surface-enhanced Raman spectroscopy combined with machine learning

2021 ◽  
pp. 2141002
Author(s):  
Hao Li ◽  
Yongbing Cao ◽  
Feng Lu
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Principal Component ◽  
Surface Enhanced Raman Spectroscopy ◽  
New Drugs ◽  
Dynamic Surface ◽  
Raman Spectrometer ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Portable Raman

With the increase in mortality caused by pathogens worldwide and the subsequent serious drug resistance owing to the abuse of antibiotics, there is an urgent need to develop versatile analytical techniques to address this public issue. Vibrational spectroscopy, such as infrared (IR) or Raman spectroscopy, is a rapid, noninvasive, nondestructive, real-time, low-cost, and user-friendly technique that has recently gained considerable attention. In particular, surface-enhanced Raman spectroscopy (SERS) can provide a highly sensitive readout for bio-detection with ultralow or even trace content. Nevertheless, extra attachment cost, nonaqueous acquisition, and low reproducibility require the conventional SERS (C-SERS) to further optimize the conditions. The emergence of dynamic SERS (D-SERS) sheds light on C-SERS because of the dispensable substrate design, superior enhancement and stability of Raman signals, and solvent protection. The powerful sensitivity enables D-SERS to perform only with a portable Raman spectrometer with moderate spatial resolution and precision. Moreover, the assistance of machine learning methods, such as principal component analysis (PCA), further broadens its research depth through data mining of the information within the spectra. Therefore, in this study, D-SERS, a portable Raman spectrometer, and PCA were used to determine the phenotypic variations of fungal cells Candida albicans (C. albicans) under the influence of different antifungals with various mechanisms, and unknown antifungals were predicted using the established PCA model. We hope that the proposed technique will become a promising candidate for finding and screening new drugs in the future.

Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer

2021 ◽  
pp. 000370282110329
Author(s):  
Ling Wang ◽  
Mario O. Vendrell-Dones ◽  
Chiara Deriu ◽  
Sevde Doğruer ◽  
Peter de B. Harrington ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Component ◽  
Surface Enhanced Raman Spectroscopy ◽  
Quantitative Detection ◽  
Least Squares Regression ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Sers Detection ◽  
Current Screening

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

scholarly journals Detection of Triphenylmethane Drugs in Fish Muscle by Surface-Enhanced Raman Spectroscopy Coupled with Au-Ag Core-Shell Nanoparticles

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Lu Pei ◽  
Yiqun Huang ◽  
Chunying Li ◽  
Yuanyuan Zhang ◽  
Barbara A. Rasco ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Malachite Green ◽  
Crystal Violet ◽  
Bimetallic Nanoparticles ◽  
Limit Of Detection ◽  
Fish Muscle ◽  
Surface Enhanced Raman Spectroscopy ◽  
Vis Spectroscopy ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Silver-coated gold bimetallic nanoparticles were synthesized and used as substrates for surface-enhanced Raman spectroscopy (SERS) in detecting prohibited triphenylmethane drugs (including crystal violet and malachite green) in fish muscle. The optical properties and physical properties of bimetallic nanospheres were characterized by UV-Vis spectroscopy and transmission electron microscopy. The optimal nanospheres selected had relatively uniform size (diameter: 33 ± 3 nm) with a silver layer coated on the surface of gold seed (diameter: 18 ± 2 nm). For both crystal violet and malachite green, characteristic SERS spectral features could be identified at concentration as low as 0.1 μg/L with these bimetallic nanospheres. Crystal violet and malachite green residues in fish muscle could also be detected at levels as low as 0.1 ng/g, which could meet the most restricted regulatory requirements for the limit of detection in terms of analytical methods for crystal violet or malachite green in fish muscle. This study provides a basis for applying SERS technology with bimetallic nanoparticles to the identification of trace amounts of prohibited substances in aquatic food products, and the methodology could be extended to analyses of other hazardous chemicals in complex food matrices like vegetables and meats.

scholarly journals Detection and Differentiation of Avian Mycoplasmas by Surface-Enhanced Raman Spectroscopy Based on a Silver Nanorod Array

2011 ◽  
Vol 78 (6) ◽  
pp. 1930-1935 ◽  
Author(s):  
Suzanne L. Hennigan ◽  
Jeremy D. Driskell ◽  
Naola Ferguson-Noel ◽  
Richard A. Dluhy ◽  
Yiping Zhao ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Sensitivity And Specificity ◽  
Limit Of Detection ◽  
Direct Detection ◽  
Nanorod Array ◽  
Surface Enhanced Raman Spectroscopy ◽  
Mycoplasma Species ◽  
Content Type ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

ABSTRACTMycoplasma gallisepticumis a bacterial pathogen of poultry that is estimated to cause annual losses exceeding $780 million. The National Poultry Improvement Plan guidelines recommend regular surveillance and intervention strategies to containM. gallisepticuminfections and ensure mycoplasma-free avian stocks, but several factors make detection ofM. gallisepticumand diagnosis ofM. gallisepticuminfection a major challenge. Current techniques are laborious, require special expertise, and are typically plagued by false results. In this study, we describe a novel detection strategy which uses silver nanorod array–surface-enhanced Raman spectroscopy (NA-SERS) for direct detection of avian mycoplasmas. As a proof of concept for use in avian diagnostics, we used NA-SERS to detect and differentiate multiple strains of avian mycoplasma species, includingAcholeplasma laidlawii,Mycoplasma gallinarum,Mycoplasma gallinaceum,Mycoplasma synoviae, andM. gallisepticum, including vaccine strains 6/85, F, and ts-11. Chemometric multivariate analysis of spectral data was used to classify these species rapidly and accurately, with >93% sensitivity and specificity. Furthermore, NA-SERS had a lower limit of detection that was 100-fold greater than that of standard PCR and comparable to that of real-time quantitative PCR. Detection ofM. gallisepticumin choanal cleft swabs from experimentally infected birds yielded good sensitivity and specificity, suggesting that NA-SERS is applicable for clinical detection.

scholarly journals Defect engineering in semiconductor-based SERS

2021 ◽  
Author(s):  
Ge Song ◽  
Shan Cong ◽  
Zhi-Gang Zhao
Keyword(s):  
Raman Spectroscopy ◽  
Surface Enhanced Raman Spectroscopy ◽  
Semiconductor Materials ◽  
Defect Engineering ◽  
Wide Range ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Semiconductor-based surface enhanced Raman spectroscopy (SERS) platforms take advantage of the multifaceted tunability of semiconductor materials to realize specialized sensing demands in a wide range of applications. However, until quite...

scholarly journals The multivariate detection limit for Mycoplasma pneumoniae as determined by nanorod array-surface enhanced Raman spectroscopy and comparison with limit of detection by qPCR

The Analyst ◽  
2014 ◽  
Vol 139 (24) ◽  
pp. 6426-6434 ◽  
Author(s):  
Kelley C. Henderson ◽  
Edward S. Sheppard ◽  
Omar E. Rivera-Betancourt ◽  
Joo-Young Choi ◽  
Richard A. Dluhy ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Detection Limit ◽  
Mycoplasma Pneumoniae ◽  
Limit Of Detection ◽  
Bacterial Pathogen ◽  
Detection Limits ◽  
Nanorod Array ◽  
Surface Enhanced Raman Spectroscopy ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

The detection limits by NA-SERS and qPCR for the bacterial pathogenMycoplasma pneumoniaewere compared.

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