Enhanced artificial intelligence for electrochemical sensors in monitoring and removing of azo dyes and food colorant substances

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

“…The results showed that the LS-SVM model had more advantages in HP intelligent analysis. Wu et al 30 used SVM and a genetic algorithm (GA) to perform regression analysis on artificial azo colorants (Tartrazine and Patent Blue V). The analysis revealed that the SVM model was more accurate in predicting Tartrazine and Patent Blue V than CA with 90.3% and 81.1%, respectively.…”