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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...

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A Rapid, Multiplex Dual Reporter IgG and IgM SARS-CoV-2 Neutralization Assay for a Multiplexed Bead-Based Flow Analysis System
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Communication regarding the article "An efficient primary screening COVID-19 by serum Raman spectroscopy".

Felipe Soares1, Gabrielli Yamashita1

  • 1Industrial Engineering Universidade Federal do Rio Grande do Sul Porto Alegre Brazil.

Journal of Raman Spectroscopy : JRS
|May 16, 2022
PubMed
Summary

This study raises concerns about potential bias in COVID-19 serum Raman spectroscopy research. Reproducing the experiment revealed that ensuring data independence significantly lowers reported results, highlighting the need for transparent methods in machine learning and computational modeling.

Keywords:
COVID‐19Raman spectroscopymachine learningscientific rigorscreening

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Area of Science:

  • Computational modeling
  • Machine learning
  • Biomedical data analysis

Background:

  • Concerns regarding potential bias in machine learning experiments are critical.
  • The article "An efficient primary screening COVID-19 by serum Raman spectroscopy" is scrutinized for methodological rigor.

Purpose of the Study:

  • To evaluate the potential bias in the training and testing data independence of a COVID-19 screening study.
  • To reproduce the experiment and assess the impact of data independence on reported results.

Main Methods:

  • Reproducing the computational experiment using the available dataset.
  • Analyzing the impact of ensuring complete independence between training and test sets.

Main Results:

  • The study demonstrates that ensuring full independence of the test set from training data leads to lower reported results.
  • This suggests potential inadvertent bias in the original study's methodology.

Conclusions:

  • Adherence to protocols minimizing bias is imperative in computational modeling and machine learning.
  • The authors request further information and code availability from the original study to ensure reproducibility and validity.