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

•Preparing a color-sensitive gas sensor array including 14 porphyrins and one pH indicator.•Obtaining odor information of flour samples of different storage periods by the sensor array.•Optimizing color components combination by swarm intelligence optimization algorithms.•Developing BPNN models for...

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Vydáno v:Food chemistry Ročník 338; s. 127828
Hlavní autoři: Jiang, Hui, Liu, Tong, He, Peihuan, Ding, Yuhan, Chen, Quansheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 15.02.2021
Témata:
Algorithms
Color
Color-sensitive gas sensor array
fatty acid composition
Fatty Acids - analysis
Flour
Flour - analysis
food chemistry
Food Storage
Hydrogen-Ion Concentration
Neural Networks, Computer
Odorants - analysis
odors
Quantitative determination
Sensor feature optimization
Swarm intelligence optimization algorithms
system optimization
Triticum - chemistry
Sensor feature optimization
Flour
Swarm intelligence optimization algorithms
Color-sensitive gas sensor array
Quantitative determination
ISSN:0308-8146, 1873-7072, 1873-7072
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Shrnutí:•Preparing a color-sensitive gas sensor array including 14 porphyrins and one pH indicator.•Obtaining odor information of flour samples of different storage periods by the sensor array.•Optimizing color components combination by swarm intelligence optimization algorithms.•Developing BPNN models for quantitative determination of fatty acid values during flour storage. 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.
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ISSN:0308-8146
1873-7072
1873-7072
DOI:10.1016/j.foodchem.2020.127828