基于改进WOA-Elman神经网络的高光谱牛奶蛋白质快速无损检测

目的：解决现有牛奶蛋白质检测方法存在的精度低、效率低和人工依赖性强等问题。方法：基于高光谱成像系统，提出一种将改进的鲸鱼算法与Elman神经网络相结合用于牛奶蛋白质含量快速无损检测。通过混沌映射、自适应收敛因子、自适应权重优化鲸鱼算法，提高搜索精度，优化后对Elman神经网络参数（权重和阈值）进行寻优。通过试验分析所提无损检测方法的性能。结果：与常规检测方法相比，试验方法在牛奶蛋白质无损检测的多个性能指标方面均为最优，决定系数为0.997 3，均方根误差为0.000 3，检测时间为1.56 s。结论：试验方法具有较高的检测精度和检测效率。

Rapid and non-destructive detection of hyperspectral milk protein based on improved WOA-Elman neural network

Objective: To solve the problems of low accuracy, low efficiency, and strong manual dependence in existing milk protein detection methods. Methods: Based on hyperspectral imaging systems, proposed a combination of improved whale algorithm and Elman neural network for rapid and non-destructive detection of milk protein content. Optimized the whale algorithm through three aspects (chaotic mapping, adaptive convergence factor, and adaptive weight) to improve search accuracy, and optimized the Elman neural network parameters (weights and thresholds) after optimization. Analyzed the performance of the proposed non-destructive testing method through experimental analysis. Results: Compared with conventional detection methods, proposed method was optimal for multiple performance indicators in non-destructive testing of milk protein. The experimental method was optimal in multiple performance indicators for non-destructive testing of milk protein, with determination coefficient of 0.997 3, the root mean square error of 0.000 3, and the detection time of 1.56 seconds. Conclusion: The experimental method has high detection accuracy and efficiency.