Long-term robust identification potential of a wavelet packet decomposition based recursive drift correction of E-nose data for Chinese spirits

The drift of electronic nose (E-nose) is always yielded, and makes it does not possess long-term robust detection ability, so that the detection accuracy of the same samples tested in the subsequent period will be reduced. In order to enhance the long-term identification robustness of six kinds of Chinese spirits, a recursive identification model was established. Firstly, E-nose data were decomposed by a wavelet packet and generated decomposition coefficients. Then a relative deviation threshold function was constructed to handle these coefficients. And then, the E-nose data with little drift or not were obtained by reconstructing the corrected coefficients. Finally, a concept of "sample test time window" (SMTW) was introduced for building the recursive identification model. The six kinds of spirit samples were discontinuously tested for 16 months, and the SMTW was determined as 6 months. As SMTW moves forward for 2 months every time, the recursive identification model based on Fisher discriminant analysis (FDA) was also built and the correct identification rate was 96.5%, namely the tested samples in 2 months of following SMTW could be accurately identified. This illustrates that the proposed methods are very effective. (C) 2019 Elsevier Ltd. All rights reserved.