Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk

Ullah, Rahat; Khan, Saranjam; Javaid, Samina; Ali, Hina; Bilal, Muhammad; Saleem, Muhammad

doi:10.1364/boe.9.000844

Cited by 29 publications

(17 citation statements)

References 29 publications

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“…For example, composition of breast milk can vary depending on whether a mother is feeding male or female infant. Fatty acids, phospholipids, and tryptophan are found in greater concentrations in mothers feeding female infants while carotenoids and saccharides are more pronounced in milk from mothers having a male infant [127]. One study shows that Raman spectroscopy in tandem with SVM with a second-order polynomial kernel function can distinguish between the two classes of milk with 86% accuracy, 58% sensitivity, and 88% specificity.…”

Section: Vegetablesmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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Section: Vegetablesmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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“…The primary target of SVM is to find a "maximum margin" partitioning hyperplane suitable for classification samples so that the classification results are the most robust and have strong generalization capabilities [46]. Due to the existence of the kernel function, the samples of the original space reach the linearly separable high-dimensional feature space through mapping [47].…”

Section: Svm Classifier Design and Implementationmentioning

confidence: 99%

Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Nie

Reheman

et al. 2020

IEEE Access

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Pancreatic cancer (PC) is a malignant tumor that seriously threatens the survival of patients. Artificial classification has practical difficulties, such as unstable classification accuracy, a heavy workload, and the classification results depend on the subjective judgment of the clinician during the diagnosis and staging of PC. In addition, accurate PC staging could better help clinicians deliver the optimal therapeutic schedule for PC patients of different stages. Therefore, this study proposes a comprehensive medical computer-aided method for preoperative diagnosis and staging of PC based on an ensemble learningsupport vector machine (EL-SVM) and computed tomography (CT) images. The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. The EL-SVM learner was used to classify 168 CT images of normal pancreas and different stages of PC. The experimental results demonstrated that the normal pancreas (normal)-pancreatic cancer early stage (early stage) classification accuracy was 86.61%, the normal-pancreatic cancer stage III (stage III) classification accuracy was 87.04%, the normal-pancreatic cancer stage IV (stage IV) classification accuracy was 91.63%, the normal-PC classification accuracy was 87.89%, the early stage-stage III classification accuracy was 75.03%, and the early stage-stage IV classification accuracy was 81.22%, and the stage III-stage IV classification accuracy was 82.48%. Our experimental results prove that our proposed method is feasible and promising for clinical applications for the preoperative diagnosis and staging of PC via CT images.

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“…, and z b = (2/3)(µ a + µ s ') −1 , under the conditions that r > 1/(µ s ' + µ a ) and µ s '>>µ a . In the wavelength region 450 -530 nm, riboflavin, beta-carotene and fat contribute to the absorption of human milk 14 . Following our approach in Bosschaart, et al [24] a more robust estimate of µ s ' and µ a can be obtained by a two-step fitting approach over the wavelength range with (450 -530 nm) and without (530 -650 nm) absorption, rather than a single fit of the model over the entire wavelength range.…”

Section: Reduced Scattering Coefficient (µ S ') and Absorption Coeffimentioning

confidence: 99%

“…In turn, these factors can be related to milk synthesis, milk removal from the breast and the development of breastfeeding problems, such as mastitis. Regarding human milk analysis, chemically highly specific optical technologies such as Raman spectroscopy have only been marginally explored for this purpose [11][12][13][14]. Human milk macronutrient analysis with NIRS is commercially available, but the technology needs to be improved for its main purpose of personalized human milk fortification in premature infants [15].…”

Section: Introductionmentioning

confidence: 99%

Optical properties of human milk

Veenstra¹,

Lenferink²,

Petersen³

et al. 2019

Biomed. Opt. Express

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With human milk being the most important source of infant nutrition, the protection and support of breastfeeding are essential from a global health perspective. Nevertheless, relatively few objective methods are available to investigate human milk composition and lactation physiology when a mother experiences breastfeeding problems. We argue that optics and photonics offer promising opportunities for this purpose. Any research activity within this new application field starts with a thorough understanding on how light interacts with human milk. Therefore, the aim of this study was to investigate the full set of optical properties for human milk and the biological variability therein. Using a novel approach that combines spatially resolved diffuse reflectance spectroscopy (SR-DRS) and spectroscopic optical coherence tomography (sOCT) between 450 and 650 nm, we quantified the absorption coefficient µ a , scattering coefficient µ s , reduced scattering coefficient µ s ', anisotropy g and backscattering coefficient µ b,NA of mature human milk from 14 participants released at different stages during a breastfeed (foremilk, bulk milk and hindmilk). Significant correlations were found between µ a , µ s , µ s ' and µ b,NA and the biochemically determined fat concentration per sample (R s = 0.38, R s = 0.77, R s = 0.80, R s = 0.44 respectively). We explained the observed variations in the optical properties of human milk using Mie theory and the biological variability in both the concentration and size distribution of milk fat globules. In conclusion, we have provided a full set of optical properties for human milk, which can hopefully serve as a starting point for future biophotonic studies on human milk and the milk containing lactating breast.

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Raman spectroscopy combined with a support vector machine for differentiating between feeding male and female infants mother’s milk

Cited by 29 publications

References 29 publications

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Optical properties of human milk

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