Optimizing West Lake Longjing tea tree species detection method on basis of genetic algorithm

Abstract

An optimizing West Lake Longjing tea tree species detection method on the basis of genetic algorithm is characterized in that: an electronic nose is used to collect finger prints from odors of different species of collected West lake Longjing tea; on the basis of analysis of average finger print and the score plot of the main component, by adopting three key technologies of genetic algorithm, namely the roulette wheel selection, single-point crossover, and simple mutation, and taking the overall recognition rate as the adaptability function, sensors that are contributed to the modeling of tree species are chosen to carry out optimizing combination, thus the required number of sensors for modeling is reduced, at the same time a matrix analysis is carried out for the sensor spectrum responding value through the F value of variance analysis and the main component load.

Description

technical field [0001] The present application relates to a method for optimizing the detection of West Lake Longjing tea tree species based on a genetic algorithm. Background technique [0002] For a long time, sensory evaluation has been an important method to evaluate the quality of tea, but this method requires rich knowledge of tea science and evaluation experience. Unless they are professional tea reviewers, distributors or manufacturers, it is difficult for ordinary tea buyers to distinguish the quality of tea from good or bad. Without considerable experience, it is difficult to obtain reliable results. And training a tea judge not only needs to be carefully selected and invested a lot of money, but also the training period is relatively long. Moreover, even for professional tea tasters, the sensitivity of their sensory organs is easily changed by the interference of external factors, thus affecting the accuracy, objectivity and consistency of the evaluation results....

AI Technical Summary

Problems solved by technology

[0013] (4) Signal drift and denoising: Due to changes in instrument measurement parameters, measurement methods, measurement environment, sample sources and other factors, it is easy to cause the sensor response curve to drift, causing errors in intelligent sensory detection, making it unable to adapt to industrialization for a long time Continuous operation, so it is necessary to strengthen research on reducing response signal drift and signal noise analysis and processing technology

[0014] (5) Robustness of the model: Some studies did not discuss the model in detail when establishing the discriminant model, nor did they use independent prediction samples to test the robustness of the model

Although the current optimal combination method uses the concept of combination to a certain extent, this combination is based on the preliminary elimination and combines the grouped sensor arrays, which does not achieve the effect of global optimal combination.

Although the Loading value method avoids the addition of redundant sensors, it does not analyze the response performance of the selected sensor, that is, the repeatability of the response of the same sensor to the same sample and the discrimination of the response to different samples.

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