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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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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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Atomic Absorption Spectroscopy: Instrumentation

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IR Spectroscopy: Molecular Vibration Overview

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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.
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Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis.

Aaron R Flanagan¹, Dhairya Dalal², Frank G Glavin¹

¹School of Computer Science, University of Galway, Galway City, Co. Galway H91 FYH2, Ireland.

Chemical Reviews

|June 23, 2025

Summary

This summary is machine-generated.

This review bridges chemometrics and generative artificial intelligence (AI), offering a practical guide to AI and data augmentation for spectroscopy researchers. It simplifies complex AI concepts for easier implementation in scientific research.

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Area of Science:

Chemometrics
Spectroscopy
Artificial Intelligence

Background:

Generative artificial intelligence (AI) methods are complex and challenging to implement.
Existing data augmentation techniques are simpler but may not match advanced AI performance.
A knowledge gap exists between chemometrics and generative AI for spectroscopy applications.

Purpose of the Study:

To bridge the knowledge gap between chemometrics and generative AI.
To provide a starting point for new researchers in spectroscopy.
To review state-of-the-art generative AI and data augmentation techniques in spectroscopy.

Main Methods:

Collected and categorized 104 peer-reviewed journals and proceedings.
Described popular preprocessing techniques in spectroscopy.
Reviewed state-of-the-art generative AI and data augmentation methods.

Main Results:

Provided intuitive explanations of AI and data augmentation methods.
Highlighted strengths and weaknesses of various techniques.
Included graphical and practical application examples.

Conclusions:

Generative AI and data augmentation are crucial for modern spectroscopy.
This review simplifies complex AI concepts for researchers.
The work serves as a foundational resource for implementing advanced AI in spectroscopy.