Selection method for subintervals of near infrared spectral characteristics based on simulated annealing-genetic algorithm

Abstract

The invention discloses a selection method for subintervals of near infrared spectral characteristics based on a simulated annealing-genetic algorithm. The method comprises the following steps: pretreating a near infrared spectrum; then dynamically dividing subintervals on the pretreated near infrared spectrum, introducing an Metropolis criterion in the simulated annealing algorithm to gene exchange and gene selection operators, and selecting an optimal character subinterval with the simulated annealing-genetic algorithm; and finally judging the best subinterval division method to be combined with the optimal character subinterval and building a PLS model for the selected optimal character subinterval. In the selection method, high-quality offspring individuals can be generated through improved variation and commutating operators, not only adaptability levels of overall populations are improved, but also enough power for population evolution is provided; and deficiency brought by the total number of the spectrum subintervals manually designated according to the experiences in the process of modeling can be avoided, and spectral models with high precision and strong prediction ability can be rapidly obtained.

Description

technical field [0001] The invention relates to a method for selecting a near-infrared spectrum characteristic subinterval for analyzing the quality of agricultural products and food, in particular to a method for selecting a near-infrared spectrum characteristic subinterval based on simulated annealing-genetic algorithm. Background technique [0002] Near-infrared spectroscopy is more and more widely used in the analysis of agricultural products and food quality due to its fast analysis speed and high efficiency. Therefore, how to effectively extract characteristic information from a large amount of near-infrared spectral data has become the focus of research in this field. [0003] The characteristic absorption of the sample in one or several bands of the near-infrared spectrum determines that the wavenumber points adjacent to the high-information wavenumber point have higher information content, that is, the near-infrared spectral data has a certain continuous correlation...

AI Technical Summary

Problems solved by technology

In recent years, some scholars have combined the genetic algorithm with the classic interval partial least squares algorithm to select the characteristic subintervals of the near-infrared spectrum, simulate the natural evolution process such as genetic variation in nature, and solve the optimal combination of the characteristic subintervals, but there are still Some deficiencies, such as dividing sub-intervals often rely on experience, which has a certain degree of subjectivity; genetic algorithms are prone to premature convergence and fall into local optimal solutions, and cannot ensure the global optimal approximate solution, etc.

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