Flour quality detection method based on hybrid simulated annealing and genetic algorithms

Abstract

The invention discloses a flour quality detection method based on hybrid simulated annealing and genetic algorithms. The flour quality detection method comprises main steps as follows: scanning flourby an infrared spectrometer to obtain spectral information, performing standard normal variable transformation on a flour spectrum to eliminate solid particles and surface scattering; globally searching the optimal spectral signal characteristic with the genetic algorithm, and searching the optimal individual in the genetic algorithm with the simulated annealing algorithm to realize combination ofglobal search and local search; preprocessing characteristic vectors, and constructing a classifier by a radial neural network to classify the processed characteristic vectors, so as to finish flourquality detection. The method has better robustness, by means of spectral preprocessing, spectral noise is removed, model complexity is reduced, and computation efficiency is improved; by combinationof the genetic algorithm and the simulated annealing algorithm, global and local optimization capabilities of a model are enhanced, accuracy of the flour quality detection is improved by the radial neural network, and nondestructive testing is realized.

Description

technical field [0001] The invention relates to the fields of food quality inspection, neural network and model optimization, in particular to a flour quality detection method based on mixed simulated annealing and genetic algorithm. Background technique [0002] Flour is one of the most important staple foods in our country. The precision of flour directly affects the color and taste of flour products, and its quality is directly related to the physical and mental health of consumers. The accuracy and robustness of the existing flour quality detection model are reduced due to too many interference factors in the spectral signal, and the model is complex, the amount of calculation is large, the flexibility of flour feature optimization is poor, the detection error is large, and the actual application effect Unsatisfactory, unable to meet the requirements of actual inspection work, affecting the economic benefits of enterprises and the physical and mental health of consumers....

AI Technical Summary

Problems solved by technology

The accuracy and robustness of the existing flour quality detection model are reduced due to too many interference factors in the spectral signal, and the model is complex, the amount of calculation is large, the flexibility of flour feature optimization is poor, the detection error is large, and the actual application effect Unsatisfactory, unable to meet the requirements of actual inspection work, affecting the economic benefits of enterprises and the physical and mental health of consumers

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