Predicting body fat mass by IR thermographic measurement of skin temperature: a novel multivariate model. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Laffaye, G.; Epishev, V.V.; Tetin, I.A.; Korableva, Y.B.; Naumova, K.A.; Antonenko, E.V.; Vavilov, V.P.
• Is Part Of: Quantitative infrared thermography. Volume 17:Issue 3(2020)
• Page Start: 192
• Page End: 209
• Publication Date: 2020-07-02
• Subjects: Infrared thermography -- temperature distribution -- skin temperature -- body composition -- adipose tissue -- gender -- multivariate model; Infrared technology -- Periodicals; Thermography -- Periodicals; Infrared technology; Thermography; Periodicals; 621.362
• DOI: 10.1080/17686733.2019.1646449
• Languages: English
• ISSNs: 1768-6733

Abstract

ABSTRACT: The purpose of this study has been to develop a multivariate model for predicting body fat mass in women by using the technique of infrared (IR) thermography. Sixty-nine healthy women, aged from 16 to 29, were investigated by using a body composition analyser and IR thermographic temperature measurement. The correlation analysis was performed to reveal the problem of multicollinearity. The technique of principal component analysis (PCA) was applied in order to reduce the number of variables. Both the total fat mass and the fat mass in the torso were accepted as the dependent variables. The individual scores were used as independent variables on each component after applying the orthogonal rotation. Two datasets were analysed: the full dataset with anthropometric characteristics (age, body mass, body length) and without anthropometric characteristics. The stepwise model meeting the Akaike information criterion (AIC) was selected to estimate the relative quality of all models. The models obtained on the full dataset were able to explain 73.9% of the fat mass in the torso and 70.4% of the total fat mass. Respectively, the models based on the reduced dataset explained 52.5% of the fat mass in the torso and 51.5% of the total fat mass.