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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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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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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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2D NMR: Homonuclear Correlation Spectroscopy (COSY)01:06

2D NMR: Homonuclear Correlation Spectroscopy (COSY)

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Homonuclear correlation spectroscopy, or COSY, is a 2-dimensional NMR technique that provides information about coupled protons. Typically, the geminal and vicinal coupling are observed. For example, consider the COSY spectrum of ethyl acetate, where its 1D proton NMR spectrum is plotted along the vertical and horizontal axes with their corresponding chemical shift scale. Three spots on the diagonal corresponding to the three peaks in the 1D proton spectrum are called diagonal peaks. The COSY...
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2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

324
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
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Overfitting One-Dimensional convolutional neural networks for Raman spectra identification.

M Hamed Mozaffari1, Li-Lin Tay1

  • 1Metrology Research Centre, National Research Council Canada, Ottawa, ON K1A0R6, Canada.

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|February 6, 2022
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Summary

This study introduces a new method using one-dimensional Convolutional Neural Networks (1DCNN) to identify unknown substances with handheld Raman spectrometers. This approach enhances accuracy and speed while reducing the need for large reference databases.

Keywords:
AI in ChemometricsCNNDeep learningHandheld spectrometerSpectral matching

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Handheld Raman spectrometers are crucial for in situ substance identification by first responders.
  • Current devices rely on extensive reference data, limiting miniaturization and affordability due to memory constraints.

Purpose of the Study:

  • To address the limitations of memory and computational power in handheld Raman spectrometers.
  • To develop a more efficient and accurate method for real-time spectral matching using machine learning.

Main Methods:

  • Utilizing one-dimensional Convolutional Neural Networks (1DCNN) trained on augmented Raman spectra.
  • Employing an overfitted 1DCNN model as a substitute for large reference databases.
  • Testing the 1DCNN model's performance in identifying pure unknown Raman instances.

Main Results:

  • The 1DCNN model significantly reduces the reliance on extensive onboard reference data.
  • Experimental results demonstrate high accuracy in identifying unknown Raman spectra from thousands of classes.
  • The proposed method alleviates memory size limitations and increases identification speed.

Conclusions:

  • 1DCNN offers a viable solution to enhance the capabilities of handheld Raman spectrometers.
  • This machine learning approach improves the speed, accuracy, and affordability of in situ substance identification.
  • The study paves the way for more advanced and portable spectroscopic analysis tools.