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Infrared (IR) Spectroscopy: Overview01:09

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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.
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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...
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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material,  molecules absorb light depending on the energy required for...
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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,...
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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
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Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
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Metabolomic Profile of <i>Vaccinium corymbosum</i> Leaves: Exploiting Diversity Among Ten Different Cultivars.

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Near-Infrared Spectroscopy Machine-Learning Spectral Analysis Tool for Blueberries (Vaccinium corymbosum) Cultivar

Pedro Ribeiro1, Maria Inês Barbosa1, Clara Sousa1

  • 1CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal.

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Summary

Near-infrared spectroscopy accurately distinguishes 19 Vaccinium corymbosum blueberry cultivars using leaf spectra. This method offers a precise alternative to berry examination for cultivar identification.

Keywords:
blueberriescultivarinfrared spectroscopymachine learningtaxonomyvaccinium

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Machine Learning

Background:

  • Vaccinium corymbosum (highbush blueberry) is a major commercial blueberry species with numerous cultivars.
  • Current cultivar discrimination relies on berry examination, which can be subjective and time-consuming.
  • Accurate cultivar identification is crucial for maintaining genetic purity and market quality.

Purpose of the Study:

  • To develop a novel method for discriminating 19 Vaccinium corymbosum cultivars using leaf near-infrared (NIR) spectra.
  • To evaluate the effectiveness of machine learning models combined with data dimensionality reduction (DDR) techniques for cultivar identification.

Main Methods:

  • Acquisition of NIR spectra from fresh blueberry leaves across two regions and three seasons.
  • Application of four DDR techniques (dictionary learning, factor analysis, fast ICA, PCA) to NIR spectral data.
  • Training and testing of 10 machine learning classifiers using cross-validation to identify optimal models.

Main Results:

  • Cultivar discrimination accuracy ranged from 52.27% to 100%.
  • Highest accuracy (100%) achieved using adaxial leaf spectra in fall and abaxial spectra in winter.
  • Fast Independent Component Analysis (ICA) was a key DDR technique in the best-performing models.

Conclusions:

  • Near-infrared spectroscopy, coupled with machine learning and DDR, is a highly accurate and suitable method for Vaccinium corymbosum cultivar discrimination.
  • This technique provides a non-destructive and efficient alternative to traditional berry examination for cultivar identification.