Drift compensation of the electronic nose in the development of instruments for out-of-laboratory analysis

A technique for evaluating and compensating for the drift of eight mass-sensitive sensor array in an open detection cell was developed to take into account the influence of external factors (temperature, changes in the chemical composition of the background) for out-of-laboratory analysis of biosamples when long-term monitoring of health state humans and animals. The effective way to compensate the sensor signal drift when the sorption properties of the sensitive coatings change during their long-term intensive operation is the daily internal standardization of the system. Distilled water is proposed as a standard for the biosamples based on the water matrix (blood, exhaled breath condensate, urine, etc.). Internal standardization is based on daily calculating the specific sensor signals by dividing the sensor signals for biosample on the corresponding averaged values from 3-5 measurements of standard. The stability of the sensor array operation is estimated using the theory of statistical process control (multivariate exponentially weighted moving average control charts) based on the specific signal of the sensor array. The control limits for the statistic quantity of the central tendency for each sensor and the whole array, variations of the sensor signals were determined. The average time to signal and run lengths for statistically substantiated monitoring of the electronic nose stability were calculated. Based on the analysis of tendency and variations in sensor signals for six months of operation, a technique was drawn up to control the stability of the sensor array for the out-of-laboratory analysis of biosamples. This approach is successfully verified by the classification of the results of the analysis of blood and water samples obtained for four months. The proposed technique can be introduced into the software algorithm of the electronic nose, which will increase the correctness of decision-making during long-term monitoring of the health state of humans and animals.