At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335-354 cm-1, 435-454 cm-1, 485-540 cm-1, 820-915 cm-1, 1155-1185 cm-1, 1300-1414 cm-1, and 1415-1520 cm-1 under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control.
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