Application of principal component analysis and artificial neural networks to recognize the individual VOCs of methanol/2-propanol in a binary mixture by SAW multi-sensor array

Abstract This work reports on the performance of a volatile organic compounds (VOCs) identification system based on a surface acoustic wave (SAW) multi-sensor array with four acoustic sensing elements, developed in dual configuration as multiplexed two-port resonator 433.92 MHz oscillators and a reference SAW element, in order to recognize the different individual components in a binary mixture of VOCs such as methanol (CH3OH) and 2-propanol (C3H7OH), in the range 20–140 and 5–70 ppm, respectively. The SAW sensors, operating at room temperature, have been specifically coated by sensing thin films belonging to various chemical classes such as arachidic acid (fatty acids), carbowax (stationary phases), triethanolamine (amines), acrylated polysiloxane (polysiloxanes) to ensure cross-sensitivity towards VOCs under test. By using the relative frequency change as the output signal of the SAW multi-sensor array with an artificial neural network (ANN), a recognition system has been realized for the identification of tested VOCs. The features extraction from output signals of the SAW multi-sensor array, exposed to the binary component mixture of methanol and 2-propanol, has been also performed by pattern recognition techniques such as principal component analysis (PCA). The feedforward multi-layer neural network with a hidden layer and trained by a back-propagation learning algorithm has been implemented in order to classify and identify the tested VOCs patterns. Both the normalized responses of four SAW sensors array and the selected principal components (PCs) scores have been used as inputs to the multi-layer perceptron ANN by resulting in a 100% success recognition rate with the four SAW sensors normalized responses and with the first two principal components scores of the original data of the primary matrix. The different strategies used to recognize the VOCs patterns by the ANNs such as the ‘Leave-one-out’ method and ‘Training-and-Test’ method are discussed. Our experimental results have evidenced that the proposed binary vapor mixture classifier based on the electronic nose system, developed by inexpensive and poorly selective chemical SAW sensors, is highly effective in the identification of tested VOCs of methanol and 2-propanol. Moreover, the combination of PCA, as data pre-processing technique, and ANN, as patterns classification technique, provides a rapid and accurate recognition method of the individual components in the tested binary mixture of methanol and 2-propanol.