Raman Spectroscopy and Its Application in Fruit Quality Detection

Yong Huang, Haoran Wang, Huasheng Huang, Zhiping Tan, Chaojun Hou, Jiajun Zhuang and Yu Tang ()
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Yong Huang: Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Haoran Wang: Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Huasheng Huang: Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Zhiping Tan: Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
Chaojun Hou: College of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Jiajun Zhuang: College of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Yu Tang: Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Agriculture, 2025, vol. 15, issue 2, 1-34

Abstract: 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.

Keywords: Raman spectroscopy; machine learning; detection of fruit diseases; detection of fruit pesticide residues; identification of fruit origin (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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