Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms
The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/13/24/4182 |
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| Summary: | The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR). Independent component analysis (ICA), competitive adaptive reweighing sampling (CARS), and their dual combinations served to extract spectral features. In camellia oil adulteration with rice bran oil, both the ICA-BPNN and ICA-PLSR models are found to achieve satisfactory performances. For camellia oil adulteration with rice bran oil and corn oil, on the other hand, the performances of BPNN-based models are substantially deteriorated, and the best prediction accuracy is achieved by a PLSR model coupled with CARS-ICA. In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance. |
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| ISSN: | 2304-8158 |