Abstract
A new means about olfactory visualization technique for the quantitative analysis of procymidone residues in rapeseed oil has been proposed. First, an olfactory visualization system was set up to collect volatile odor information from rapeseed oil samples containing different concentrations of procymidone residues. Then, we utilized four intelligent optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) and simulated annealing (SA), to optimize the characteristics of the sensors. Finally, support vector machine regression (SVR) models employing optimized features were constructed for the quantitative detection of procymidone residues in rapeseed oil. The study demonstrated that the SA-SVR model demonstrated superior prediction results, achieving a high determination coefficient of prediction (R2PRP2<math><mrow><msubsup><mi>R</mi><mrow><mi>P</mi></mrow><mn>2</mn></msubsup></mrow></math>) at 0.9894. As indicated by the results, it is possible to successfully conduct non-destructive detection of procymidone residues in edible oil by the olfactory visualization technology.