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Applications of IR Spectroscopy: Overview01:11

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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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UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given...
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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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Related Experiment Video

Updated: Sep 16, 2025

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
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Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.

Zhichen Lun1, Xiaohong Wu1,2, Jiajun Dong1

  • 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.

Foods (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

Artificial intelligence (AI) and spectroscopic technologies enhance food quality inspection. Their synergy offers faster, more precise, and non-invasive methods for detecting food quality from production to consumption.

Keywords:
deep learningmultimodal integrationquality inspectionspectral-heterogeneous fusionspectroscopic technologies

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Data Science

Background:

  • Growing consumer demand for high-quality, nutritious, and safe food products.
  • Spectroscopic technologies are crucial for food quality inspection.
  • Artificial intelligence (AI) offers new opportunities for food quality detection.

Purpose of the Study:

  • To review cutting-edge nondestructive spectroscopic and imaging technologies for food quality inspection.
  • To explore the integration of deep learning with spectroscopic techniques.
  • To identify future research directions for advanced food quality inspection systems.

Main Methods:

  • Review of six spectroscopic and imaging technologies: near-infrared/mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, hyperspectral imaging, terahertz spectroscopy, and nuclear magnetic resonance (NMR).
  • Focus on deep learning integration, spectral fusion, and hybrid spectral-heterogeneous fusion methodologies.
  • Analysis of technological principles, merits, and applications in food quality detection.

Main Results:

  • The combination of spectroscopic technologies and deep learning demonstrates superior speed, precision, and non-invasiveness in food quality analysis.
  • Synergistic approaches enhance spectral data processing accuracy and enable real-time decision-making.
  • These integrated methods effectively address challenges posed by complex matrices and spectral noise.

Conclusions:

  • The synergy between spectroscopy and deep learning is highly effective for food quality inspection.
  • Future research should focus on multimodal spectroscopic integration, edge computing for portable devices, and AI-driven applications.
  • The goal is to establish a high-precision, sustainable food quality inspection system throughout the supply chain.