Accuracy calibration transfer between multi-sensor systems based on metal-oxide gas sensors used for analysis of exhaled air

Abstract

The application of electronic nose system as an instrument for exhaled air analysis for diagnosing various diseases (lung cancer, etc.) is one of the most rapidly developing areas of predictive medicine. However, a significant disadvantage of such systems is difficulty in production of identical multi-sensor systems because the sensors of the same type are not identical, that degrades the predictive ability. To solve this problem calibration transfer methods are used.This paper analyzes currently applied calibration transfer methods. A new approach to evaluate the effectiveness of calibration transfer methods for two multi-sensor systems is proposed and implemented. One- and three-component gas mixtures are taken for the analysis. In this work, four methods are used to adjust the sensor responses: UDS, UDSwoi, DS-L1R and DS-PLS2. Applicability of one-factor standardization methods (UDS and UDSwoi) is shown on the classification tasks: multi-class classification task of one-component gas mixtures and binary classification task of three-component gas mixtures. Multi-class classification of model one-component gas mixtures for UDS, UDSwoi with 4 calibration transfer samples provided the classification accuracy of 0.985 and 0.990, respectively. The binary classification of two three-component mixtures with different ratios of three VOCs within the 20% discrepancy limits, UDS, UDSwoi and DS-PLS2 achieved an error-free accuracy of 1.000.