Moisture measurement in cheese analogue using stretched and multi-exponential models of the magnetic resonance T_2 relaxation curve

摘要

The dairy industry would benefit from rapid and non-destructive determination of moisture content of cheese products. The two components primarily responsible for the low-field magnetic resonance (MR) spin-spin relaxation (T_2) signal of cheese products are fat and the water bound to protein. If the moisture component of the signal can be distinguished from the fat component, it should be possible to measure moisture using an MR sensor. Therefore, a key aspect of the development of an MR moisture measurement method is examination of techniques for analysis of T_2 relaxation curves. One common method of T_2 analysis of complex foods, such as cheese, is to fit multi-term exponential models to the curves. An alternative approach is proposed which uses stretched exponential models. The single-term stretched exponential model has been used for porous rock systems and polymers, but not for foods. The T_2 relaxation curves were analysed using both models and the results were compared. The number of unknowns in the three-term exponential and two-term stretched exponential models was reduced by assuming the relaxation curve of the fat component was the same as the relaxation curve of pure fat. In each model, one of the exponential terms described the behaviour of the water in the cheese analogue, while the remaining term or terms described the behaviour of the fat. For each model the T_2 relaxation time associated with the water was well correlated with moisture content. Coefficients of determination of the relaxation time versus moisture from each of the two models were nearly identical. The advantages and disadvantages of the two models are discussed.