Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method

Abstract

The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods. Loss-on-drying method provides reliable results, whilst usually labor-intensive and time-consuming. This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method. The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method. The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists (AOAC). When the moisture content of meat samples (beef and pork) was varied from 69.46% to 74.21%, the relative error of the meat moisture content (MMC) calculated by the proposed algorithm was 0.0017-0.0117, the absolute errors were less than 1%. The testing time was about 40.18%-56.87% less than the standard detection procedure.