Abstract
Reproducibility of E-nose is an important issue. An inherent problem of MOS sensors is that the signal is different when exposed to the same surroundings. This is caused by the discreteness of MOS sensors. Therefore, this chapter shows how to solve this challenge of discreteness issue in E-nose. In terms of the homogeneous linearity between multi-sensors systems, an on-line calibration transfer model based on global affine transformation (GAT) and Kennard–Stone sequential (KSS) algorithm is presented and evaluated in this chapter. GAT is achieved in terms of one single sensor by a robust weighted least square (RWLS) algorithm, and KSS is studied for representative transfer sample subset selection from a large sample space. This chapter consists of two aspects: calibration step (for responses of sensors) and prediction step (for gas concentration). Prediction is developed to evaluate the performance of calibration transfer. In prediction step, three artificial neural networks for concentration prediction of three analytes were trained based on back-propagation algorithm. Experimental results show that the reproducibility can be significantly improved by using GAT method, and the discreteness problem is solved.
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Zhang, L., Tian, F., Zhang, D. (2018). Affine Calibration Transfer Model. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_18
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DOI: https://doi.org/10.1007/978-981-13-2167-2_18
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