Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B₁ in Peanuts by Olfactory Visualization Technique.

ABSTRACT: This study proposes a novel method for detection of aflatoxin B₁ (AFB₁) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB₁ in peanuts. In this process, the optimization performance of grid search (GS) algo

SUBMITTER: Zhu C 

PROVIDER: S-EPMC9573054 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature