Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning
The technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide r...
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MDPI AG
2025-01-01
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| Series: | Molecules |
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| author | Zheng-Yong Zhang Jian-Sheng Su Huan-Ming Xiong |
| author_facet | Zheng-Yong Zhang Jian-Sheng Su Huan-Ming Xiong |
| author_sort | Zheng-Yong Zhang |
| collection | DOAJ |
| description | The technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide rich compositional and characteristic information about the samples. In terms of spectral preprocessing, there are various methods, such as normalization, wavelet denoising, and feature extraction. A combination of these methods with appropriate quantitative techniques is beneficial to reveal the differences between samples or improve predictive performance. Quantitative evaluation can be divided into similarity measurement methods and machine learning algorithms. When evaluating small batch samples, similarity measurements can provide quantitative discrimination results. When the sample data are sufficient and matched with Raman spectroscopy parameters, machine learning algorithms suitable for intelligent discrimination can be trained and optimized. Finally, with the rise of deep learning algorithms and fusion strategies, some challenges in this field are proposed. |
| format | Article |
| id | doaj-art-82fbcb2b629e4e4a96a833c957e09d56 |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-82fbcb2b629e4e4a96a833c957e09d562025-01-24T13:43:13ZengMDPI AGMolecules1420-30492025-01-0130223910.3390/molecules30020239Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine LearningZheng-Yong Zhang0Jian-Sheng Su1Huan-Ming Xiong2School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, ChinaSchool of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, ChinaDepartment of Chemistry, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200438, ChinaThe technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide rich compositional and characteristic information about the samples. In terms of spectral preprocessing, there are various methods, such as normalization, wavelet denoising, and feature extraction. A combination of these methods with appropriate quantitative techniques is beneficial to reveal the differences between samples or improve predictive performance. Quantitative evaluation can be divided into similarity measurement methods and machine learning algorithms. When evaluating small batch samples, similarity measurements can provide quantitative discrimination results. When the sample data are sufficient and matched with Raman spectroscopy parameters, machine learning algorithms suitable for intelligent discrimination can be trained and optimized. Finally, with the rise of deep learning algorithms and fusion strategies, some challenges in this field are proposed.https://www.mdpi.com/1420-3049/30/2/239dairy productsRaman spectroscopyquantitative identificationchemometricsmachine learning |
| spellingShingle | Zheng-Yong Zhang Jian-Sheng Su Huan-Ming Xiong Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning Molecules dairy products Raman spectroscopy quantitative identification chemometrics machine learning |
| title | Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning |
| title_full | Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning |
| title_fullStr | Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning |
| title_full_unstemmed | Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning |
| title_short | Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning |
| title_sort | technology for the quantitative identification of dairy products based on raman spectroscopy chemometrics and machine learning |
| topic | dairy products Raman spectroscopy quantitative identification chemometrics machine learning |
| url | https://www.mdpi.com/1420-3049/30/2/239 |
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