Raman Spectroscopy and Its Application in Fruit Quality Detection

Huang, Yong; Wang, Haoran; Huang, Huasheng; Tan, Zhiping; Hou, Chaojun; Zhuang, Jiajun; Tang, Yu

doi:10.3390/agriculture15020195

Open AccessReview

Raman Spectroscopy and Its Application in Fruit Quality Detection

by

Yong Huang

¹,

Haoran Wang

¹

,

Huasheng Huang

¹,

Zhiping Tan

¹,

Chaojun Hou

²,

Jiajun Zhuang

² and

Yu Tang

^1,*

¹

Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China

²

College of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

^*

Author to whom correspondence should be addressed.

Agriculture 2025, 15(2), 195; https://doi.org/10.3390/agriculture15020195

Submission received: 23 December 2024 / Revised: 9 January 2025 / Accepted: 12 January 2025 / Published: 17 January 2025

(This article belongs to the Section Agricultural Product Quality and Safety)

Download

Browse Figures

Review Reports Versions Notes

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

Share and Cite

MDPI and ACS Style

Huang, Y.; Wang, H.; Huang, H.; Tan, Z.; Hou, C.; Zhuang, J.; Tang, Y. Raman Spectroscopy and Its Application in Fruit Quality Detection. Agriculture 2025, 15, 195. https://doi.org/10.3390/agriculture15020195

AMA Style

Huang Y, Wang H, Huang H, Tan Z, Hou C, Zhuang J, Tang Y. Raman Spectroscopy and Its Application in Fruit Quality Detection. Agriculture. 2025; 15(2):195. https://doi.org/10.3390/agriculture15020195

Chicago/Turabian Style

Huang, Yong, Haoran Wang, Huasheng Huang, Zhiping Tan, Chaojun Hou, Jiajun Zhuang, and Yu Tang. 2025. "Raman Spectroscopy and Its Application in Fruit Quality Detection" Agriculture 15, no. 2: 195. https://doi.org/10.3390/agriculture15020195

APA Style

Huang, Y., Wang, H., Huang, H., Tan, Z., Hou, C., Zhuang, J., & Tang, Y. (2025). Raman Spectroscopy and Its Application in Fruit Quality Detection. Agriculture, 15(2), 195. https://doi.org/10.3390/agriculture15020195

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Raman Spectroscopy and Its Application in Fruit Quality Detection

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI