Research on Predicting the Amount of Sodium Chloride Added in Brine Solution Using Electronic Tongue
Sodium chloride serves as the primary ingredient of salt.It is of great significance to develop an objective,accurate and rapid prediction method of sodium chloride addition in brine for intelligent control of brine salinity.Taking the brine used in the production of Fuliji roast chicken as the object,sodium chloride was added to the brine matrix solution according to the gradient concentration to prepare the electronic tongue test solution.Signals from the pulse voltammetric taste sensor are then collected for analysis.Competitive Adaptive Reweighted Sampling(CARS)method was used to screen key feature variables to form independent variable datasets.This data set is then used to construct and compare detection models employing techniques such as Partial Least Squares(PLS),Support Vector Machine(SVM)and Extreme Learning Machine(ELM).The results demonstrate that the CRAS method successfully identified 93 key feature variables.The electrodes are ranked in order of importance as follows:Tungsten(W)elec-trode>Palladium(Pd)electrode>Gold(Au)electrode>Titanium(Ti)electrode>Platinum(Pt)electrode.The constructed PLS,SVM and ELM models yielded test set correlation coefficients of 0.969,0.994,and 0.999 6 re-spectively.The root mean square errors in prediction were 0.035 6 g/L,0.089 8 g/L,and 1.67×10-5 g/L respec-tively,with the ELM model demonstrating the best performance.It can be concluded that the combination of a pulse voltammetric electronic tongue,based on bare inert metal electrodes,and the ELM regression algorithm can be effec-tively utilized for rapid prediction of sodium chloride addition in brine.