Dependency of the optical scattering properties of human milk on casein content and common sample preparation methods.

SIGNIFICANCE: Quantifying human milk composition is important for daily nutritional management in neonatal intensive cares worldwide. Photonic solutions based on visible light can potentially aid in this analysis, as energy content of human milk depends largely on fat content, and the optical scattering properties of human milk predominantly depend on the size and concentration of fat globules. However, it is expected that human milk scattering changes upon homogenization, routinely done before analysis, which may affect fat globule size. AIM: The first aim of this study was to investigate how the most common homogenization methods (gently inverting by hand, vortexing, and sonication) affect the optical properties of human milk. The second aim was to estimate the scattering contribution of casein micelles, the second most dominant scatterers in human milk. APPROACH: We combined diffuse reflectance spectroscopy with spectroscopic optical coherence tomography to measure the scattering coefficient µs, reduced scattering coefficient µs', and anisotropy g between 450 and 600 nm. RESULTS: Sonication induced the strongest changes in µs, µs', and g compared to the gently inverted samples (203%, 202%, and 7%, respectively, at 550 nm), but also vortexing changed µs' with 20%. Although casein micelles only showed a modest contribution to µs and g at 550 nm (7% and 1%, respectively), their contribution to µs' was 29%. CONCLUSIONS: The scattering properties of human milk strongly depend on the homogenization method that is employed, and gentle inversion should be the preferred method. The contribution of casein micelles was relatively small for µs and g but considerably larger for µs'.

Classification of burn injury using Raman spectroscopy and optical coherence tomography: An ex-vivo study on porcine skin.

Accurate depth assessment of burn wounds is a critical task to provide the right treatment and care. Currently, laser Doppler imaging is able to provide better accuracy compared to the standard clinical evaluation. However, its clinical applicability is limited by factors like scanning distance, time, and cost. Precise diagnosis of burns requires adequate structural and functional details. In this work, we evaluated the combined potential of two non-invasive optical modalities, optical coherence tomography (OCT) and Raman spectroscopy (RS), to identify degrees of burn wounds (superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT)). OCT provides morphological information, whereas, RS provides biochemical aspects. OCT images and Raman spectra were obtained from burns created on ex-vivo porcine skin. Algorithms were developed to segment skin region and extract textural features from OCT images, and derive spectral wave features from RS. These computed features were fed into machine learning classifiers for categorization of burns. Histological results obtained from trichrome staining were used as ground-truth. The combined performance of RS-OCT reported an overall average accuracy of 85% and ROC-AUC=0.94, in distinguishing the burn wounds. The significant performance on ex vivo skin motivates to assess the feasibility of combined RS-OCT in in vivo models.