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基于介电光谱技术的Korla香梨的颜色检测方法

Hong Zhang¹, Jiean Liao², Yawen Xiao³

¹College of Water Resources and Architectural Engineering, Tarim University, Alaer, China.

Frontiers in plant science

|November 17, 2025

概括

通过介电光谱学,精确检测了Korla芳香梨的颜色. 像SPA和UVE这样的特征可变提取方法提高了预测准确性,SPA-PLSR和UVE-PLSR模型显示了不同颜色参数的最佳结果.

科学领域:

农业科学农业科学
食品科学食品科学食品科学
频谱学是一种光谱学.

背景情况:

科拉香梨的商业价值取决于精确的质量控制,特别是水果的颜色.
快速而精确的色彩检测对于提高这些梨的销售能力至关重要.

研究的目的:

为了研究介电性质和Korla香梨的颜色之间的关系.
使用介电光谱学数据开发和比较用于预测梨色的模型.
确定最有效的特征变量提取和回归方法,以准确预测颜色.

主要方法:

用向量网络分析仪和0.1-26.5 GHz的同轴探针测量了介电性质 (介电常数 ε'和介电损失因子 ε′′).
使用非信息变量消除 (UVE) 和连续投影算法 (SPA) 来从介电光谱数据中提取特征变量.
部分最小平方回归 (PLSR),支向量回归 (SVR) 和最小平方支向量回归 (LSSVR) 被用来构建颜色预测模型.

主要成果:

在单个频率的介电参数和颜色之间观察到弱线性相关性.
紫外线和SPA都显著提高了对梨色的预测准确度.
SPA-PLSR模型为L* (R2 = 0.83) 提供了最好的预测,而UVE-PLSR模型在预测a* (R2 = 0.85) 和b* (R2) = 0.73方面表现出色.

结论:

介电光谱学与特征变量提取相结合,为准确检测Korla芳香梨的质量提供了一个有希望的新方法.
该SPA-PLSR和UVE-PLSR模型显示高潜力在梨的非破坏性颜色评估.
优化特征选择对于提高基于介电光谱的质量评估模型的性能至关重要.

关键词:

科尔拉香梨的香气很好.颜色颜色颜色颜色介电光谱技术技术介电光谱技术机器学习是机器学习.非破坏性测试是指非破坏性测试.

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