电子鼻技术对猪肉挥发性盐基氮的预测研究

Research on prediction of the total volatile basic nitrogen in pork by electronic nose technique

为预测不同肥瘦配比猪肉的新鲜度,对4℃恒温贮藏条件下的新鲜猪肉进行挥发性盐基总氮(Total Volatile Basic Nitrogen,TVB-N)检测和营养成分检测,同时利用电子鼻技术检测挥发性气味的信息。以传感器阵列特征值为自变量建立蛋白质、脂肪的回归预测模型,分别对不同肥瘦配比的猪肉样本建立不分类和分类2种TVB-N神经网络预测模型。结果表明:先分类再建立神经网络模型预测的效果更优,将样本进行二分类建立2个模型后,模型训练组的相关系数达0.994、0.985(p<0.01),预测组的相关系数达到0.984、0.979(p<0.01);模型的绝对误差小而且分布区间集中,训练组和预测组各有86%、62.6%的样本的绝对误差在0~1之间;训练组中没有绝对误差大于2.5的样本,预测组中仅有8.5%的样本绝对误差大于2.5。电子鼻传感器特征信号与TVB-N数据具有很强的相关性,电子鼻可以快速预测出不同肥瘦配比猪肉在贮藏期间TVB-N含量的变化,进而无损的评价猪肉的新鲜度。

In order to predict freshness of different proportion of fat and lean pork, TVB- N content and nutrient of fresh pork was detected under the condition of 4℃ .The volatile odor information of fresh pork was also detected by electronic nose technology.The regression prediction model of nutrient components was established with the characteristic values of sensor array.Two kinds of TVB-N neural network prediction models were established for classifying and not classifying the proportion of fat and lean.The results showed that the classification and establishment of neural network model to predict the effect better. After classifying the samples into two categories and establishing 2 models, correlation coefficient of the model training group was 0.994,0.985 （p 〈 0.01 ）, the correlation coefficient of prediction group reached 0.984,0.979 （p 〈 0.01 ）.The absolute error of the model was small and the distribution interval was concentrated. The 86% and 62.6% samples of the absolute error between 0-1 in the training group.There was no absolute error of more than 2.5 samples, only 8.5 % samples in the prediction group was greater than 2.5.There exists a good correlation between e-nose sensor signal and TVB-N content, the electronic nose detection technique could be used as a rapid way to predict TVB-N content and to evaluate pork freshness with non- destructive test.