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Abstract：
An electronic nose based on an array of 6 metal oxide semiconductor sensors was used, jointly with artificial neural network (ANN) method, to classify Pecorino cheeses according to their ripening time and manufacturing techniques. For this purpose different pre-treatments of electronic nose signals have been tested. In particular, four different features extraction algorithms were compared with a principal component analysis (PCA) using to reduce the dimensionality of data set (data consisted of 900 data points per sensor). All the ANN models (with different pre-treatment data) have different capability to predict the Pecorino cheeses categories. In particular, PCA show better results (classification performance: 100%; RMSE: 0.024) in comparison with other pre-treatment systems.
摘要：
使用了基于6个金属氧化物半导体传感器阵列的电子鼻，结合人工神经网络(ANN)方法，根据成熟时间和制造技术对Pecorino奶酪进行分类。为此,已经测试了电子鼻信号的不同预处理。特别是,将四种不同的特征提取算法与主成分分析（PCA）进行比较，以降低数据集的维数（每个传感器由900个数据点组成的数据）。所有ANN模型（具有不同的预处理数据）都具有不同的预测Pecorino奶酪类别的能力。特别是,与其他预处理系统相比，PCA显示出更好的结果（分类性能：100％；RMSE：0.024）。
