Improving the algorithm for processing data from multisensor system in tasks of determining quality parameters in vegetable oils

The effective functioning of modern production systems is impossible without using of methods for processing and analyzing data continuously generated during operation. Limitations imposed on the speed and precision of determining the required indicators lead to the need of optimizing the algorithms used. Multisensor systems, as a rule, have an excessive number of cross-sensitive sensors, and their signals can be used to determine various indicators of a similar physical nature. The purpose of the study is to improve the algorithm for processing multidimensional data from multisensor systems. Principal component analysis was applied as part of the developed algorithm for the formation of informative features. Partial least squares regression was used to build regression models. The data set for approbation of proposed approach was obtained through potentiometric measurements using a digital mV-meter. An experiment is described using a multisensor system called “electronic tongue”, consisting of 12 cross-sensitive potentiometric sensors. In the experiment, real samples of vegetable oils acted as analyzed objects. Regression models were built to determine three quality indicators of vegetable oils: peroxide value, para-anisidine value and total tocopherol concentrations. The results of the study were compared with known scientific works. A comparative analysis allowed us to conclude that using of the most informative sources selected according to the proposed algorithm can significantly reduce the root mean square error of prediction. The results obtained can be used both in systems for identifying deviations in production processes in “Industry 4.0” enterprises, and for expressly identifying counterfeit products.