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首页> 外文期刊>Applied Engineering in Agriculture >Prediction of egg's freshness using backward propagation neural network.
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Prediction of egg's freshness using backward propagation neural network.

机译：使用反向传播神经网络预测鸡蛋的新鲜度。

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摘要

Chemical, biological, and physical changes occur in eggs with the increase in the number of storage days and the storage condition. One hundred twenty eggs laid on the same day ranging in color from light brown to dark brown and of different size were bought from the local market. The samples were stored in the lab room ambient temperature and relative humidity. The spectral data from 300 to 1100 nm of 20 sample eggs on each alternative days were obtained until the 12th day of storage. Predictive model for Haugh Unit (HU) and thick albumen height was developed using backward propagation neural network (BPN) with Savitzky Golay (SG) third pass filter and Multiplicative Scatter Correction (MSC) pre-processing method. BPN predicted HU with correlation coefficient (R) and Root Mean Square Error of Calibration (RMSEC) of 0.82 and 8.11, respectively. Similarly the BPN predicted the thick albumen height with R and RMSEC of 0.83 and 0.88 mm. The device used in the research for spectral collection is simple and portable and the results obtained are satisfactory, this method can be used to evaluate the freshness of eggs in terms of HU and thick albumen height in the local market.
机译：随着储存天数和储存条件的增加，鸡蛋中会发生化学，生物和物理变化。当天从当地市场上购买了一百二十个鸡蛋，颜色从浅棕色到深棕色不等。将样品储存在实验室环境温度和相对湿度下。直到存储的第12天为止，每隔一天获取20个样本鸡蛋从300到1100 nm的光谱数据。利用反向传播神经网络（BPN）和Savitzky Golay（SG）三通滤波器和乘积散射校正（MSC）预处理方法，建立了霍夫单位（HU）和厚蛋白高度的预测模型。 BPN预测HU的相关系数（R）和校准均方根误差（RMSEC）分别为0.82和8.11。同样，BPN预测厚蛋白高度，R和RMSEC分别为0.83和0.88 mm。本研究中用于光谱采集的装置简单易行，取得的结果令人满意，该方法可用于评估本地市场上鸡蛋的新鲜度（HU）和厚蛋白高度。

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