Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features

The fatty acid content of flour is an important indicator for determining the deterioration of flour. We propose a novel rapid measurement method for fatty acid content during flour storage based on a self-designed color-sensitive gas sensor array. First, a color-sensitive gas sensor array was prepared to capture the odor changes during flour storage. The sensor features were then optimized using genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO). Finally, back propagation neural network (BPNN) models were established to measure the fatty acid content during flour storage. Experimental results showed that the optimization effects of the three algorithms improved in the following order: GA < ACO < PSO, for the sensor features optimization. In the validation set, the determination coefficient of the best PSO-BPNN model was 0.9837. The overall results demonstrate that the models established on the optimized features can realize rapid measurements of fatty acid content during flour storage.

Automated summary

A novel rapid measurement method for fatty acid content during flour storage was proposed using a self-designed color-sensitive gas sensor array. The sensor features were optimized using GA, ACO, and PSO, and BPNN models were established for measurement. Experimental results showed that PSO was the most effective algorithm for sensor features optimization, achieving a determination coefficient of 0.9837 in the validation set. Overall, the optimized models allowed for rapid measurements of fatty acid content during flour storage.