基于传感器阵列的小黄鱼新鲜度检测电子鼻设计

Design on electronic nose for detecting the freshness of Larimichthys polyactis based on sensor array

为设计一款可用于农产品新鲜度测定的电子鼻,以小黄鱼为研究对象,在恒定条件下开展腐败试验,以传感器响应值为自变量,小黄鱼挥发性盐基氮(Total volatile basic nitrogen,TVB-N)值为因变量,并分别通过多元线性回归、偏最小二乘法和BP神经网络,建立小黄鱼鱼肉品质等级的预测模型,并通过对比3种模型对TVB-N值预测的相关系数R和平均误差百分比RE-mean,分析模型性能优劣。结果表明,利用预测模型电子鼻能够分辨出小黄鱼的新鲜腐败样品,应用BP神经网络的预测算法对样品实现最好的预测,多元线性回归模型与最小二乘法预测模型性能较差。

To develop an electronic nose that can be used to determine the freshness of agricultural products,taking the Larimichthys polyactis as the research object,the corruption test was carried out under constant conditions,and the data were collected.The sensor response value was taken as the independent variable and the total volatile basic nitrogen(TVB-N)value of Larimichthys polyactis was taken as the dependent variable.The multiple linear regression,partial least squares and BPNN were used to establish a prediction model of Larimichthys polyactis meat quality grade,and the performance of the model was analyzed by comparing the correlation coeffi⁃cient R and the average error percentage RE-mean of the three models for TVB-N value prediction.The results showed that the elec⁃tronic nose could distinguish the fresh rotten samples of Larimichthys polyactis by using the prediction model.It could be seen that the prediction algorithm of BP neural network could achieve the best prediction for the samples,and the performance of the multiple linear regression model and the least square method was poor.