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基于机器学习的非破坏性测定果布里克斯从近红外光谱

Deqiang Zhou¹, Zhenghan Li¹, Jiahao Zhu¹

¹School of Intelligent Manufacturing Engineering, Jiangnan University, Wuxi, China.

Journal of food science

|March 14, 2026

概括

一个新的模型使用光谱分析和基因算法优化的神经网络准确地预测果Brix (糖含量). 这种方法为果提供了快速,高精度的质量控制.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
人工智能的人工智能

背景情况:

果的质量评估依赖于布里克斯 (糖含量) 测量.
非破坏性布里克斯测定方法往往缺乏足够的准确性.

研究的目的:

开发一种高精度和高效的非破坏性方法来确定果Brix.
用先进的算法改进现有的布里克斯预测模型.

主要方法:

开发了一种混合模型,将部分最小平方 (PLS) 结合起来,用于缩小维度,并使用反向传播 (BP) 神经网络进行预测.
使用改进的遗传算法 (GA) 来优化BP网络的初始权重和值.
频谱分析用于非破坏性数据采集.

主要成果:

对GA优化的PLS-BP模型实现了0.9320的高确定系数 (R2).
该模型显示了0.2534.4的低根平均平方预测误差 (RMSEP).
该模型表现出仅为1.2ms的快速推断时间.

结论:

拟议的GA优化的PLS-BP模型提供了一个快速,准确和可靠的方法,用于非破坏性的果布里克斯测定.
这种技术为实际的果质量控制提供了有效的技术支持.
该研究强调了将光谱分析与优化的神经网络集成为农产品评估的潜力.

关键词:

果布里克斯检测检测器遗传算法是一种遗传算法.神经网络的神经网络的神经网络频谱分析是一种分析.

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