基于可见近红外高光谱图像的的可溶性固体含量预测
Yanli Yao1, Junjun He1, Zhangyun Gao2
1Key Laboratory of Tropical Fruit Biology, Ministry of Agriculture (Zhanjiang), Key Laboratory of Hainan Province for Postharvest Physiology and Technology of Tropical Horticultural Products, South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, China.
Frontiers in plant science
|February 6, 2026
概括
超光谱成像可以准确地检测的可溶性固体含量 (SSC). 该方法提供了高效和精确的质量识别,有可能在水果行业广泛应用.
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:
- 溶性固体含量 (SSC) 是质量和消费者接受度的关键指标.
- 准确和高效的SSC检测对于市场化和销售至关重要.
研究的目的:
- 开发和验证一种高光谱成像 (HSI) 方法,以准确检测SSC.
- 为了比较不同的光谱预处理和回归模型的性能,用于SSC预测.
主要方法:
- 在400-1700纳米范围内收集了的超光谱图像.
- 应用的光谱预处理 (MSC,SG等) 并建造了PLSR,拉索和RR模型.
- 利用连续投影算法 (SPA) 进行特征带选择,并优化了模型.
主要成果:
- 最初的模型表现良好 (PLSR R2=0.9459).
- 优化的ddA-PLSR模型实现了极好的准确性 (R2=0.9869,RMSE=0.1250).
- 确定了用于SSC预测的四个关键波长 (673-676nm,711-715nm,971-990nm,1357-1367nm).
结论:
- 超光谱成像是一种非常有效的工具,用于非破坏性的SSC评估.
- 优化的HSI方法显示了实时质量识别的巨大潜力.
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