Open AccessRaman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk
Author(s) -
Rahat Ullah,
Saranjam Khan,
Samina Javaid,
Hina Ali,
Muhammad Bilal,
Muhammad Saleem
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.000844
Subject(s) - support vector machine , raman spectroscopy , principal component analysis , kernel (algebra) , artificial intelligence , mathematics , pattern recognition (psychology) , biology , computer science , optics , physics , combinatorics
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.