AIMC Topic: Spectrum Analysis, Raman

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Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra.

Environmental science & technology
Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously use...

Point-of-care diagnosis of tissue fibrosis: a review of advances in vibrational spectroscopy with machine learning.

Pathology
Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by the need for additional stains, time, personnel and resources. Vibrational spectroscopy is a novel technique that offers an alternative atraumatic appr...

Nanoisland SERS-Substrates for Specific Detection and Quantification of Influenza A Virus.

Biosensors
Surface-enhanced Raman spectroscopy (SERS)-based aptasensors for virus determination have attracted a lot of interest recently. This approach provides both specificity due to an aptamer component and a low limit of detection due to signal enhancement...

Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte.

The Analyst
There is increasing interest in the application of Raman spectroscopy in a medical setting, ranging from supporting real-time clinical decisions surgical margins to assisting pathologists with disease classification. However, there remain a number o...

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early can...

Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology.

Journal of advanced research
INTRODUCTION: Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immun...

Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis.

International journal of molecular sciences
Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic ...

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach.

Scientific reports
Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it i...

Raman spectroscopy-based prediction of ofloxacin concentration in solution using a novel loss function and an improved GA-CNN model.

BMC bioinformatics
BACKGROUND: A Raman spectroscopy method can quickly and accurately measure the concentration of ofloxacin in solution. This method has the advantages of accuracy and rapidity over traditional detection methods. However, the manual analysis methods fo...

A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data.

Journal of chemical information and modeling
Raman spectra are examples of high dimensional data that can often be limited in the number of samples. This is a primary concern when Deep Learning frameworks are developed for tasks such as chemical species identification, quantification, and diagn...