title: Near infrared spectroscopy for body fat sensing in neonates: quantitative analysis by GAMOS simulations.
abstract: BACKGROUND
  Under-nutrition in neonates is closely linked to low body fat percentage. Undernourished neonates are exposed to immediate mortality as well as unwanted health impacts in their later life including obesity and hypertension. One potential low cost approach for obtaining direct measurements of body fat is near-infrared (NIR) interactance. The aims of this study were to model the effect of varying volume fractions of melanin and water in skin over NIR spectra, and to define sensitivity of NIR reflection on changes of thickness of subcutaneous fat. GAMOS simulations were used to develop two single fat layer models and four complete skin models over a range of skin colour (only for four skin models) and hydration within a spectrum of 800-1100 nm. The thickness of the subcutaneous fat was set from 1 to 15 mm in 1 mm intervals in each model.
  
  
  RESULTS
  Varying volume fractions of water in skin resulted minimal changes of NIR intensity at ranges of wavelengths from 890 to 940 nm and from 1010 to 1100 nm. Variation of the melanin volume in skin meanwhile was found to strongly influence the NIR intensity and sensitivity. The NIR sensitivities and NIR intensity over thickness of fat decreased from the Caucasian skin to African skin throughout the range of wavelengths. For the relationship between the NIR reflection and the thickness of subcutaneous fat, logarithmic relationship was obtained.
  
  
  CONCLUSIONS
  The minimal changes of NIR intensity values at wavelengths within the ranges from 890 to 940 nm and from 1010 to 1100 nm to variation of volume fractions of water suggests that wavelengths within those two ranges are considered for use in measurement of body fat to solve the variation of hydration in neonates. The stronger influence of skin colour on NIR shows that the melanin effect needs to be corrected by an independent measurement or by a modeling approach. The logarithmic response obtained with higher sensitivity at the lower range of thickness of fat suggests that implementation of NIRS may be suited for detecting under-nutrition and monitoring nutritional interventions for malnutrition in neonates in resource-constrained communities.
authors.affiliation: School of Electrical and Information Engineering, Faculty of Engineering, University of Sydney, New South Wales, Australia. fatin.mustafa@sydney.edu.au.
authors.forename: Fatin Hamimi
authors.identifier: http://orcid.org/0000-0002-7996-4114
authors.initials: FH
authors.lastname: Mustafa
authors.affiliation: School of Electrical and Information Engineering, Faculty of Engineering, University of Sydney, New South Wales, Australia. peter.jones@sydney.edu.au.
authors.forename: Peter W
authors.initials: PW
authors.lastname: Jones
authors.affiliation: School of Electrical and Information Engineering, Faculty of Engineering, University of Sydney, New South Wales, Australia.
authors.forename: Alistair L
authors.initials: AL
authors.lastname: McEwan
mesh_terms: D000273:Adipose Tissue / Q000378:metabolism*; D006801:Humans; D007231:Infant, Newborn; D008543:Melanins / Q000378:metabolism; D009010:Monte Carlo Method; D012867:Skin / Q000378:metabolism; D012984:Software; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D014867:Water / Q000378:metabolism
chemical_list: D008543:Melanins; D014867:Water
keywords: Body fat sensing; Fat thickness; GAMOS simulation; Near-infrared spectroscopy; Neonates; Under-nutrition detection