This study presents differentiation in milk samples of mother's feeding male and female infants using Raman spectroscopy combined with a support vector machine (SVM). Major differences have been observed in the Raman spectra of both types of milk based on their chemical compositions. Overall, it has been found that milk samples of mother's having a female infant are richer in fatty acids, phospholipids, and tryptophan. In contrast, milk samples of mother's having a male infant contain more carotenoids and saccharides. Principal component analysis and SVM further highlighted the differences between the two groups on the basis of differentiating features obtained from their Raman spectra. The SVM model with two different kernels, i.e. polynomial kernel function (order-2) and Gaussian radial basis function (RBF sigma-2), are used for gender based milk differentiations. The performance of the proposed model in terms of accuracy, precision, sensitivity, and specificity using the polynomial kernel function of order-2 have been found to be 86%, 88%, 85% and 88%, respectively.
This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220-1275, and 1524 cm, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.
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