Raman Spectroscopy and Its Application in Fruit Quality Detection
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 spectrosco...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/2/195 |
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| author | Yong Huang Haoran Wang Huasheng Huang Zhiping Tan Chaojun Hou Jiajun Zhuang Yu Tang |
| author_facet | Yong Huang Haoran Wang Huasheng Huang Zhiping Tan Chaojun Hou Jiajun Zhuang Yu Tang |
| author_sort | Yong Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a148b5a2412428bb6b6015c4d0039d4 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-3a148b5a2412428bb6b6015c4d0039d42025-01-24T13:16:05ZengMDPI AGAgriculture2077-04722025-01-0115219510.3390/agriculture15020195Raman Spectroscopy and Its Application in Fruit Quality DetectionYong Huang0Haoran Wang1Huasheng Huang2Zhiping Tan3Chaojun Hou4Jiajun Zhuang5Yu Tang6Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaAcademy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaAcademy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaAcademy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaCollege of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaCollege of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaAcademy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaRaman 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.https://www.mdpi.com/2077-0472/15/2/195Raman spectroscopymachine learningdetection of fruit diseasesdetection of fruit pesticide residuesidentification of fruit origin |
| spellingShingle | Yong Huang Haoran Wang Huasheng Huang Zhiping Tan Chaojun Hou Jiajun Zhuang Yu Tang Raman Spectroscopy and Its Application in Fruit Quality Detection Agriculture Raman spectroscopy machine learning detection of fruit diseases detection of fruit pesticide residues identification of fruit origin |
| title | Raman Spectroscopy and Its Application in Fruit Quality Detection |
| title_full | Raman Spectroscopy and Its Application in Fruit Quality Detection |
| title_fullStr | Raman Spectroscopy and Its Application in Fruit Quality Detection |
| title_full_unstemmed | Raman Spectroscopy and Its Application in Fruit Quality Detection |
| title_short | Raman Spectroscopy and Its Application in Fruit Quality Detection |
| title_sort | raman spectroscopy and its application in fruit quality detection |
| topic | Raman spectroscopy machine learning detection of fruit diseases detection of fruit pesticide residues identification of fruit origin |
| url | https://www.mdpi.com/2077-0472/15/2/195 |
| work_keys_str_mv | AT yonghuang ramanspectroscopyanditsapplicationinfruitqualitydetection AT haoranwang ramanspectroscopyanditsapplicationinfruitqualitydetection AT huashenghuang ramanspectroscopyanditsapplicationinfruitqualitydetection AT zhipingtan ramanspectroscopyanditsapplicationinfruitqualitydetection AT chaojunhou ramanspectroscopyanditsapplicationinfruitqualitydetection AT jiajunzhuang ramanspectroscopyanditsapplicationinfruitqualitydetection AT yutang ramanspectroscopyanditsapplicationinfruitqualitydetection |