scholarly journals Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia-Wei Tang ◽  
Qing-Hua Liu ◽  
Xiao-Cong Yin ◽  
Ya-Cheng Pan ◽  
Peng-Bo Wen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (14) ◽  
pp. 4827-4835 ◽  
Author(s):  
Shizhuang Weng ◽  
Hecai Yuan ◽  
Xueyan Zhang ◽  
Pan Li ◽  
Ling Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Deep Learning ◽  
Surface Enhanced Raman Spectroscopy ◽  
Learning Networks ◽  
Biological Detection ◽  
Machine Learning Methods ◽  
Intelligent Analysis ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis.

scholarly journals Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Biomedical Applications ◽  
Data Augmentation ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Cancer Tissue ◽  
Deep Convolutional Neural Networks ◽  
Surface Enhanced Raman

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

scholarly journals Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Biomedical Applications ◽  
Data Augmentation ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Cancer Tissue ◽  
Deep Convolutional Neural Networks ◽  
Surface Enhanced Raman

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

scholarly journals Nanoparticle surface-enhanced Raman spectroscopy as a noninvasive, label-free tool to monitor hematological malignancy

Nanomedicine ◽  
2021 ◽  
Vol 16 (24) ◽  
pp. 2175-2188
Author(s):  
Stacy Grieve ◽  
Nagaprasad Puvvada ◽  
Angkoon Phinyomark ◽  
Kevin Russell ◽  
Alli Murugesan ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Hematological Malignancies ◽  
Residual Disease ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Clinical Tool ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
A Cell

Aim: Monitoring minimal residual disease remains a challenge to the effective medical management of hematological malignancies; yet surface-enhanced Raman spectroscopy (SERS) has emerged as a potential clinical tool to do so. Materials & methods: We developed a cell-free, label-free SERS approach using gold nanoparticles (nanoSERS) to classify hematological malignancies referenced against two control cohorts: healthy and noncancer cardiovascular disease. A predictive model was built using machine-learning algorithms to incorporate disease burden scores for patients under standard treatment upon. Results: Linear- and quadratic-discriminant analysis distinguished three cohorts with 69.8 and 71.4% accuracies, respectively. A predictive nanoSERS model correlated (MSE = 1.6) with established clinical parameters. Conclusion: This study offers a proof-of-concept for the noninvasive monitoring of disease progression, highlighting the potential to incorporate nanoSERS into translational medicine.

Advanced Machine Learning Methods for Prediction of Fracture Closure Pressure

2021 ◽  
Author(s):  
Mohamed Ibrahim Mohamed ◽  
Dinesh Mehta ◽  
Erdal Ozkan
Keyword(s):  
Machine Learning ◽  
Integrated Approach ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Pressure Derivative ◽  
Fracture Geometry ◽  
Machine Learning Methods ◽  
Personal Bias ◽  
Fracture Closure

Abstract Determining the closure pressure is crucial for optimal hydraulic fracturing design and successful execution of fracturing treatment. Historically, the use of diagnostic tests before the main fracturing treatment has significantly advanced to gain more information about the pattern of fracture propagation and fluid performance to optimize the designs. The goal is to inject a small volume of fracturing fluid to breakdown the formation and create small fracture geometry, then once pumping is stopped the pressure decline is analyzed to observe the fracture closure. Many analytical methods such as G-Function, square root of time, etc. have been developed to determine the fracture closure pressure. There are cases in which there is difficulty in determining the fracture closure pressure, as well as personal bias and field experiences make it challenging to interpret the changes in the pressure derivative slope and identify fracture closure. These conditions include: High permeability reservoirs where fracture closure occurs very fast due to the quick fluid leakoff.Extremely low permeability reservoir, which requires a long shut-in time for the fluid to leak off and determine the fracture closure pressure.The non-ideal fluid leak-off behavior under complex conditions. The objective of this study is to apply machine learning methods to implement a predesigned algorithm to execute the required tasks and predict the fracture closure pressure while minimizing the shortcomings in determining the closure pressure for non-ideal or subjective conditions. This paper demonstrates training different supervised machine learning algorithms to help predict fracture closure pressure. The workflow involves using the datasets to train and optimize the models, which subsequently are used to predict the closure pressure of testing data. The output results are then compared with actual results from more than 120 DFIT data points. We further propose an integrated approach to feature selection and dataset processing and study the effects of data processing on the success of the model prediction. The results from this study limit the subjectivity and the need for the experience of personal interpreting the data. We speculate that a linear regression and MLP neural network algorithms can yield high scores in the prediction of fracture closure pressure.

Micron Surface-Enhanced Raman Spectroscopy of Intact Biological Organisms and Model Systems

1994 ◽  
Vol 48 (5) ◽  
pp. 545-548 ◽  
Author(s):  
Elizabeth A. Todd ◽  
Michael D. Morris
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Biological Membranes ◽  
Surface Enhanced Raman Spectroscopy ◽  
Model Systems ◽  
Zebrafish Embryos ◽  
Filter Membrane ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Biological Organisms

Surface-enhanced Raman spectra have been obtained within intact zebrafish embryos and inside the 500-fL pores of a Nucleopore filter membrane with the use of coated microelectrodes with 1–3 μm active silver tip diameters. The spectra obtained demonstrate the microelectrode's ability to penetrate biological membranes as well as restricted volumes.

scholarly journals Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7714
Author(s):  
Ha Quang Man ◽  
Doan Huy Hien ◽  
Kieu Duy Thong ◽  
Bui Viet Dung ◽  
Nguyen Minh Hoa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Flow Unit ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Log Data ◽  
Hydraulic Flow ◽  
Core Data ◽  
Machine Learning Methods

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

The Analyst ◽  
2020 ◽  
Vol 145 (23) ◽  
pp. 7559-7570
Author(s):  
Fatma Uysal Ciloglu ◽  
Ayse Mine Saridag ◽  
Ibrahim Halil Kilic ◽  
Mahmut Tokmakci ◽  
Mehmet Kahraman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Staphylococcus Aureus ◽  
Raman Spectroscopy ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Techniques ◽  
Methicillin Resistant ◽  
Learning Techniques ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Herein, surface-enhanced Raman spectroscopy (SERS) combined with supervised and unsupervised machine learning techniques were used for the identification of methicillin-resistant and methicillin-sensitive Staphylococcus aureus.

scholarly journals A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Mengya Li ◽  
Haiyan He ◽  
Guorong Huang ◽  
Bo Lin ◽  
Huiyan Tian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Classification Model ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Unseen Data ◽  
Raman Spectral Data

Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.

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