NMR-based quantification of fat in mouse samples

• Main Author: Sreedhar, Sagaana Poongothai.
• Other Authors: Vitali Zagorodnov
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/51960
• Institution: Nanyang Technological University

The purpose of this project was to use a non-invasive and efficient method to calculate the proportion of the components present in a sample of data. The type of data that was collected was the relaxation signal of the sample by performing the non-invasive and non-destructive NMR Relaxometry experiment. For the purposes of this project, the proportion of fat that is present in the signal obtained by performing the experiment on a Mouse was required to be calculated. A sample of mouse contains three components which are Water, Lean Muscle, and Fat, and out of these, the proportion of Fat was to be estimated. Many possible methods were explored such as exponential fitting, Inverse Laplace Transformation, Principal Component Analysis, and Partial Least Squares Regression techniques. Upon deeper examination, Exponential curve-fitting using the Levenberg-Marquardt algorithm was selected as the best possible algorithm for implementation. The search was narrowed down by the fact that the decay parameters were unavailable and had to be estimated. The success of the algorithm was measured by analyzing the residuals of the fit data against the actual data, meaning that the smaller the error, the better the fit. The signal obtained was not only multi-exponential but also piece-wise exponential, and this was handled in the implementation of the algorithm. The decay parameters of water was found to be approximately 2041ms, fat to be around 85ms, and lean muscle to be around 37ms using this algorithm. Upon using these values to calculate the proportions, it was found that the Mouse sample contained ~83% lean muscle, ~17% fat, and little to no water content. The applications of NMR Relaxometry are varied, and when the right algorithms are chosen for analysis, it can be an extremely useful tool for understanding the different components of any biological system.