通过自制电子鼻系统采集活体大闸蟹的气味信息,采用流行学习算法对大闸蟹样本的多维特征响应进行降维,提取样本的低维特征向量,再利用反向传播神经网络实现对大闸蟹新鲜度的识别,并与理化指标挥发性盐基氮进行比较.结果表明,基于该算法的大闸蟹新鲜度识别的准确度可达到98.1%,且依据电子鼻技术与依据理化指标判断结果基本一致,因此采用电子鼻技术的大闸蟹新鲜度无损识别方法是可行的.%An electronic nose was designed to collect odor data of live Chinese mitten crab (Eriocheir sinensis) using a sensor array consisting of 7 commercial tin oxide gas sensors. To obtain a better feature vector for identifying different crabs, a modified unsupervised discriminant projection coupled with sample label information was proposed which could maintain the local and global structure and take advantage of the important label information to achieve optimal linear geometric projection. Then back-propagation neural network was used for modeling the quality changes of crabs during storage. At the same time, the total volatile basic nitrogen (TVB-N) of crab meat was measured and used as an indicator of crab freshness. The results showed that a high degree of accuracy in nondestructive identification of crab freshness was achieved with electronic nose based on this algorithm.
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