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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

332
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...
332
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...
373
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

587
The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
587
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

875
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
875
IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

5.5K
Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the...
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Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
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Learning algorithms for identification of whisky using portable Raman spectroscopy.

Kwang Jun Lee1,2,3, Alexander C Trowbridge1,2,3, Graham D Bruce4

  • 1Centre of Light for Life (CLL) and Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, Adelaide, 5005, SA, Australia.

Current Research in Food Science
|April 10, 2024
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Summary

A new framework uses a portable Raman device and machine learning for rapid whisky identification and quality control. This non-destructive method achieves over 99% accuracy, even through the bottle, aiding in counterfeit detection.

Keywords:
Brand identificationMachine learningRaman spectroscopyWhisky

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Brand substitution and quality control are significant challenges in the high-value spirits industry.
  • Accurate and efficient identification methods are crucial for consumer trust and industry integrity.

Purpose of the Study:

  • To develop a novel, automated framework for the direct analysis of whisky using spectral data.
  • To achieve high accuracy in identifying whisky brands and quantifying key chemical components.
  • To demonstrate the practical application of the technique in real-world scenarios, including through-bottle analysis.

Main Methods:

  • Integration of a portable Raman spectroscopy device with machine learning models.
  • Direct analysis of raw spectral data without human intervention.
  • Development of a unique beam geometry for through-bottle spectral measurements.

Main Results:

  • Machine learning models achieved over 99% accuracy in identifying whisky brands across twenty-eight commercial samples.
  • The framework successfully quantified ethanol concentrations and measured methanol levels.
  • Through-bottle spectral measurements demonstrated the technique's real-world applicability and practicality.

Conclusions:

  • The developed framework offers a rapid, non-destructive, and highly accurate method for whisky analysis.
  • The technique enhances practicality by enabling through-bottle measurements, reducing the need for sample preparation.
  • This approach has significant potential for detecting counterfeit or adulterated spirits and other high-value liquids.