Abstract:
Abstract： To overcome the difficulty of visually distinguishing pure milk types and non-destructive testing of pure milk， an improved genetic algorithm-optimized wavelet neural network pure milk recognition algorithm is proposed， which can effectively ameliorate the accuracy and recognition efficiency of traditional wavelet neural network recognition algorithms. Firstly， a genetic algorithm is employed to the traditional wavelet neural network recognition algorithm， which is utilized to optimize the weight， threshold， and wavelet basis function translation and contraction factor parameters in the wavelet neural network to improve the accuracy of the recognition algorithm. In addition， a cyclic perturbation strategy has been adopted to the algorithm， greatly reducing the number of iterations required for optimal results， thereby improving the recognition efficiency of the algorithm. In the algorithm experiment section of this article， 200 sets of pure milk samples of the same brand but different types are selected as experimental samples， and near-infrared spectroscopy technology is used to obtain absorbance data of all milk samples in the wavelength range of 4000～10000 cm−1. Subsequently， to improve the training efficiency of milk data， principal component analysis algorithm is utilized to extract feature data with high cumulative contribution rate. The extracted principal component feature data are preliminarily trained and tested utilizing the proposed algorithm. The experimental results show that adding genetic algorithm could improve the accuracy from 97.5% to 100%. After adding the cyclic perturbation strategy， the number of training iterations could be greatly reduced， and the convergence speed of the algorithm could be greatly improved. Therefore， the pure milk recognition algorithm proposed in this article can effectively and non destructively distinguish pure milk.

Key words: Key words： near infrared spectroscopy, wavelet, neural network, genetic algorithm, classification recognition, pure milk