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通过融合二维域和频率特征来检测葡萄和桃番茄的SSC和pH的光谱检测
Yuzhen Wei1, Siyi Yao1, Wenjun Hu1
1School of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000, China. doctorwhy08@163.com.
Analytical methods : advancing methods and applications
|November 4, 2025
概括
本研究引入了先进的深度学习模型,用于准确预测水果中的可溶性固体含量 (SSC) 和pH值. 双通道卷积神经网络 (双CNN) 在水果质量的光谱检测中取得了高精度.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 准确的可溶性固体含量 (SSC) 和pH预测对于水果和蔬菜的质量评估至关重要.
- 传统的方法往往是耗时和劳动密集的.
研究的目的:
- 开发先进的光谱建模技术,以快速准确地检测葡萄和桃番茄的SSC和pH值.
- 与现有方法相比,提高预测准确度和概括能力.
主要方法:
- 发展回归模型,包括部分最小平方回归 (PLSR),反向传播神经网络 (BPNN),辐射基函数神经网络 (RBFNN) 和卷积神经网络 (CNN).
- 将1D光谱转换为2D特征矩阵,用于2D-CNN分析.
- 使用波形变换提取频率特征矩阵.
- 构建一个双通道CNN (双CNN),集成2D光谱和频率特征.
主要成果:
- 1D-CNN模型表现出比传统模型更高的性能.
- 2D-CNN模型表现出更高的准确性和概括性.
- 双CNN模型实现了最高的预测准确性,稳定性和概括性.
- 确定系数 (RP2) 在葡萄中达到0.958的SSC和0.929的pH,在桃番茄中达到0.936的SSC和0.925的pH.
- 果实之间的模型转移验证显示了强大的性能 (RP20.892-0.915).
结论:
- 先进的深度学习模型,特别是双CNN,显著提高了水果质量属性的光谱检测的准确性和可靠性.
- 提出的方法为农业行业的非破坏性,快速质量评估提供了一个有希望的方法.
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