Raman Spectroscopy and Its Application in Fruit Quality Detection
Gorde:
| Argitaratua izan da: | Agriculture vol. 15, no. 2 (2025), p. 195 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , , , |
| Argitaratua: |
MDPI AG
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2077-0472 | ||
| 024 | 7 | |a 10.3390/agriculture15020195 |2 doi | |
| 035 | |a 3159158330 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231331 |2 nlm | ||
| 100 | 1 | |a Huang, Yong |u Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China; <email>gsdhuangy@gpnu.edu.cn</email> (Y.H.); <email>15537470396@163.com</email> (H.W.); <email>huanghsheng@gpnu.edu.cn</email> (H.H.); <email>tanzp@gpnu.edu.cn</email> (Z.T.) | |
| 245 | 1 | |a Raman Spectroscopy and Its Application in Fruit Quality Detection | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Pesticides | ||
| 653 | |a Phenols | ||
| 653 | |a Pollutants | ||
| 653 | |a Gold | ||
| 653 | |a Data processing | ||
| 653 | |a Food analysis | ||
| 653 | |a Organic compounds | ||
| 653 | |a Nanoparticles | ||
| 653 | |a Fruits | ||
| 653 | |a Molecular structure | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Food quality | ||
| 653 | |a Machine learning | ||
| 653 | |a Pesticide residues | ||
| 653 | |a Raman spectroscopy | ||
| 653 | |a Functional groups | ||
| 653 | |a Agribusiness | ||
| 653 | |a Disease detection | ||
| 653 | |a Chemical bonds | ||
| 653 | |a Radiation | ||
| 653 | |a Spectroscopy | ||
| 653 | |a Vibration | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Spectral analysis | ||
| 653 | |a Lasers | ||
| 653 | |a Pigments | ||
| 653 | |a Residues | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Vibration analysis | ||
| 653 | |a Agricultural production | ||
| 653 | |a Food contamination | ||
| 653 | |a Light | ||
| 653 | |a Spectroscopic analysis | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wang, Haoran |u Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China; <email>gsdhuangy@gpnu.edu.cn</email> (Y.H.); <email>15537470396@163.com</email> (H.W.); <email>huanghsheng@gpnu.edu.cn</email> (H.H.); <email>tanzp@gpnu.edu.cn</email> (Z.T.) | |
| 700 | 1 | |a Huang, Huasheng |u Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China; <email>gsdhuangy@gpnu.edu.cn</email> (Y.H.); <email>15537470396@163.com</email> (H.W.); <email>huanghsheng@gpnu.edu.cn</email> (H.H.); <email>tanzp@gpnu.edu.cn</email> (Z.T.) | |
| 700 | 1 | |a Tan, Zhiping |u Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China; <email>gsdhuangy@gpnu.edu.cn</email> (Y.H.); <email>15537470396@163.com</email> (H.W.); <email>huanghsheng@gpnu.edu.cn</email> (H.H.); <email>tanzp@gpnu.edu.cn</email> (Z.T.) | |
| 700 | 1 | |a Hou, Chaojun |u College of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; <email>houchaojun@zhku.edu.cn</email> (C.H.); <email>zhuangjiajun@zhku.edu.cn</email> (J.Z.) | |
| 700 | 1 | |a Zhuang, Jiajun |u College of Mathematics and Data Science, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China; <email>houchaojun@zhku.edu.cn</email> (C.H.); <email>zhuangjiajun@zhku.edu.cn</email> (J.Z.) | |
| 700 | 1 | |a Tang, Yu |u Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China; <email>gsdhuangy@gpnu.edu.cn</email> (Y.H.); <email>15537470396@163.com</email> (H.W.); <email>huanghsheng@gpnu.edu.cn</email> (H.H.); <email>tanzp@gpnu.edu.cn</email> (Z.T.) | |
| 773 | 0 | |t Agriculture |g vol. 15, no. 2 (2025), p. 195 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159158330/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159158330/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |