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Related Concept Videos

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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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.
Different compounds display unique properties due to their...
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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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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 Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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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Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

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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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IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

2.9K
When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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Related Experiment Video

Updated: Sep 13, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Classification of Apricot Varieties by Infrared Spectroscopy and Machine Learning.

Jaume Béjar-Grimalt1, David Pérez-Guaita1, Ángel Sánchez-Illana1

  • 1Department of Analytical Chemistry, University of Valencia, 46100 Burjassot, Spain.

ACS Agricultural Science & Technology
|July 31, 2025
PubMed
Summary
This summary is machine-generated.

Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with machine learning accurately identifies apricot varieties. This method offers a faster, lab-free alternative to traditional physicochemical analysis for fruit classification.

Keywords:
ATR–FTIRPLS-DAPrunus armeniaca LRFSVMregression and classification

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Determination of Self- and Inter-incompatibility Relationships in Apricot Combining Hand-Pollination, Microscopy and Genetic Analyses
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Determination of Self- and Inter-incompatibility Relationships in Apricot Combining Hand-Pollination, Microscopy and Genetic Analyses
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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Data Science

Background:

  • Traditional apricot variety identification relies on time-consuming physicochemical analyses.
  • Developing rapid and accurate methods for fruit classification is crucial for the agricultural industry.

Purpose of the Study:

  • To investigate the efficacy of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy coupled with machine learning for classifying eight apricot varieties.
  • To establish ATR-FTIR spectroscopy as a viable, efficient alternative to traditional methods for apricot authentication.

Main Methods:

  • Utilized ATR-FTIR spectra from 731 apricots, divided into calibration (512) and test (219) sets.
  • Applied three machine learning models: partial least-squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF).
  • Validated spectroscopic models against reference models built using physicochemical data.

Main Results:

  • Achieved 97% accuracy in predicting apricot varieties using ATR-FTIR spectroscopy and machine learning models.
  • Identified strong correlations between spectral data and biochemical composition (sugars, organic acids) via PLS-DA regression vectors.
  • Obtained comparable results between spectroscopic and physicochemical methods, confirming the validity of the ATR-FTIR approach.

Conclusions:

  • ATR-FTIR spectroscopy combined with machine learning provides a highly accurate and efficient method for apricot variety classification.
  • This spectroscopic technique offers a promising, non-destructive alternative for rapid authentication in the food industry.
  • The study validates the potential of spectral data analysis for understanding fruit biochemical profiles and ensuring varietal integrity.