鸡蛋新鲜度检测十分重要，为实现无损检测鸡蛋新鲜度，该文利用电子鼻技术，通过挥发物的检测来尝试对20 ℃、70% RH贮藏条件下的鸡蛋新鲜度进行预测。并测量鸡蛋的理化指标（哈夫单位和蛋黄指数）作为新鲜度的衡量标准。通过线性判别分析对储藏不同天数的鸡蛋进行分类分析，发现线性判别分析能较好地区分不同储藏天数的鸡蛋，判别函数的总贡献率为75.70%；利用多元线性回归和BP神经网络分析法建立电子鼻响应信号和鸡蛋理化指标之间的关系模型，所建多元线性回归模型的相关系数达0.84以上，相对误差在8.00%左右；所建BP神经网络模型的相关系数达0.84以上，相对误差在9.00%左右。说明电子鼻技术对鸡蛋新鲜度具有一定的预测能力，该研究可为鸡蛋新鲜度的无损检测提供参考。

It is very important to detect the freshness of eggs. In order to achieve non-destructive detection of freshness, the electronic-nose technique was used in this study to predict the degrees of freshness of the eggs stored at a temperature of 20°C and a relative humidity (RH) of 70% by detecting volatiles. Meanwhile, as known indicators of the degree of freshness, the physical and chemical indices of eggs (Haugh unit and yolk index) were measured. The classification analysis of eggs stored for different numbers of days was conducted using linear discrimination analysis, and the result showed that these eggs could be distinguished effectively; the total contribution of the discriminant functions was 75.70%. Models of the relationship between the electronic-nose signal and the physical and chemical indices of eggs were established using multiple linear regression analysis and a back propagation (BP) neural network. The correlation coefficient and relative error of the multiple linear regression model were greater than 0.84 and around 8.00%, respectively. The correlation coefficient and the relative error of the BP neural network model were greater than 0.84 and around 9.00%, respectively. The results indicated that the electronic nose technique had a certain predictive capability of egg freshness, and this study can provide a reference for the non-destructive detection of the freshness of eggs.

国家自然科学基金项目（31071548）