一个手持的物联网视觉/NIR光谱系统来评估葡萄中的可溶性固体含量
Xu Zhang1, Ziquan Qin1, Ruijie Zhao1
1College of Enology, Northwest A&F University, Xianyang 712100, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
一个新的低成本的手持物联网设备使用Vis/NIR光谱学准确预测葡萄中的可溶性固体含量. 数据优化技术显著改善了在现场进行葡萄质量评估的模型性能.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 葡萄酒质量与葡萄的质量直接相关,需要准确的成熟度评估.
- 溶性固体含量 (SSC) 是葡萄成熟度的一个关键指标.
- 传统的Vis/NIR光谱仪是昂贵的,并且不适合现场使用.
研究的目的:
- 开发一种低成本的手持式物联网多光谱设备,用于葡萄中非破坏性SSC检测.
- 评估数据预处理和特征选择方法的有效性,以提高SSC预测准确度.
- 为现场葡萄质量监测提供实用解决方案.
主要方法:
- 将AS7265X传感器集成到手持物联网设备中,用于光谱数据收集 (410-940 nm).
- 蒙特卡洛 (MC) 和主要组件分析 (PCA) 的应用,以消除异常值.
- 八种光谱预处理技术的比较,包括移动平均平滑 (MAS) 和萨维茨基-戈莱平滑 (SGS).
- 使用统一和高效 (UVE) 和连续投影算法 (SPA) 进行特征波长选择.
主要成果:
- 最初的SSC预测模型实现了0.697.7的RV2.
- 在MAS和SGS预处理中,RV2分别提高到0.756和0.766.
- 使用UVE和SPA增强RV2的特征选择分别为0.809和0.795.
- 优化的光谱数据显著改善了SSC预测模型的性能.
结论:
- 数据优化方法对于提高SSC预测模型的准确性至关重要.
- 开发的物联网Vis/NIR光谱系统为葡萄葡萄中SSC实时监测提供了具有成本效益和便携性的解决方案.
- 这项技术有助于改进葡萄收获决策,并有助于提高葡萄酒的质量.
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