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Raman Spectroscopy and Its Application in Fruit Quality Detection

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Udgivet i:Agriculture vol. 15, no. 2 (2025), p. 195
Hovedforfatter: Huang, Yong
Andre forfattere: Wang, Haoran, Huang, Huasheng, Tan, Zhiping, Hou, Chaojun, Zhuang, Jiajun, Tang, Yu
Udgivet:
MDPI AG
Fag:
Pesticides
Phenols
Pollutants
Gold
Data processing
Food analysis
Organic compounds
Nanoparticles
Fruits
Molecular structure
Spectrum analysis
Food quality
Machine learning
Pesticide residues
Raman spectroscopy
Functional groups
Agribusiness
Disease detection
Chemical bonds
Radiation
Spectroscopy
Vibration
Learning algorithms
Spectral analysis
Lasers
Pigments
Residues
Quantitative analysis
Vibration analysis
Agricultural production
Food contamination
Light
Spectroscopic analysis
Environmental
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Resumen:Raman spectroscopy is a spectral analysis technique based on molecular vibration. It has gained widespread acceptance as a practical tool for the non-invasive and rapid characterization or identification of multiple analytes and compounds in recent years. In fruit quality detection, Raman spectroscopy is employed to detect organic compounds, such as pigments, phenols, and sugars, as well as to analyze the molecular structures of specific chemical bonds or functional groups, providing valuable insights into fruit disease detection, pesticide residue analysis, and origin identification. Consequently, Raman spectroscopy techniques have demonstrated significant potential in agri-food analysis across various domains. Notably, the frontier of Raman spectroscopy is experiencing a surge in machine learning applications to enhance the resolution and quality of the resulting spectra. This paper reviews the fundamental principles and recent advancements in Raman spectroscopy and explores data processing techniques that use machine learning in Raman spectroscopy, with a focus on its applications in detecting fruit diseases, analyzing pesticide residues, and identifying origins. Finally, it highlights the challenges and future prospects of Raman spectroscopy, offering an effective reference for fruit quality detection.
ISSN:2077-0472
DOI:10.3390/agriculture15020195
Fuente:Agriculture Science Database