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Related Concept Videos

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

4.5K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.8K
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...
1.8K
IR Spectrum01:19

IR Spectrum

1.9K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
1.9K
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

1.1K
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
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IR Spectrometers01:25

IR Spectrometers

2.2K
There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
2.2K
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

1.4K
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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Updated: Jan 4, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Fast honey classification using infrared spectrum and machine learning.

Hung-Yu Chien1, An-Tong Shih1, Bo-Shuen Yang1

  • 1Department of Information Management, National ChiNan University, Taiwan, R.O.C.

Mathematical Biosciences and Engineering : MBE
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

Infrared spectroscopy combined with machine learning offers a rapid, non-destructive method to detect adulterated and fraudulent honey. This technology effectively classifies honey types, protecting consumers and honest producers.

Keywords:
adulterationfraudhoneyinfra-redmachine learningspectrum

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Data Science

Background:

  • Honey adulteration and fraud are prevalent due to market demand exceeding supply.
  • Common issues include mixing with fructose, mislabeling imported honey as domestic, and up-pricing cheaper honey types.
  • Consumers face challenges in verifying honey authenticity.

Purpose of the Study:

  • To develop an efficient and convenient technology for classifying honey authenticity.
  • To protect consumers and support honest honey producers by combating fraudulent practices.

Main Methods:

  • Analysis of infrared spectra from honey samples.
  • Application of machine learning algorithms for honey classification.

Main Results:

  • The developed technology effectively distinguishes between several main honey types found in Taiwan.
  • Experimental results confirm the accuracy and reliability of the classification method.

Conclusions:

  • Infrared spectroscopy and machine learning provide a non-destructive, immediate, and low-manpower tool for honey screening.
  • This technology can serve as an effective solution for fast screening of honey products to ensure authenticity.