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首页> 外文期刊>International Journal of Agricultural and Biological Engineering >Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method
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Prediction and fusion algorithm for meat moisture content measurement based on loss-on-drying method

机译:基于丧失干燥方法的肉类水分含量测量预测与融合算法

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摘要

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.
机译:干燥干燥方法已被广泛用作测量高湿度材料的水分含量的标准方法,如固体和半固体食品。丧失干燥方法提供可靠的结果,而通常劳动密集型和耗时。本文提出了一种基于损失干燥方法预测肉类水分含量的新算法。该拟议的方法基于Fick的第二律制定了一种基于Fick的第二法的肉类的干燥动力学模型,并使用最小二乘法设计了一种肉类水分含量的预测算法。将预测结果与官方分析化学家(AOAC)协会推荐的官方方法进行了比较。当肉类样品(牛肉和猪肉)的水分含量为69.46%至74.21%时,通过所提出的算法计算的肉水分含量(MMC)的相对误差为0.0017-0.0117,绝对误差小于1% 。测试时间约为40.18%-56.87%小于标准检测程序。

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