基于高光谱成像技术的不同度的西红中可溶性固体含量的确定
Yiyang Zhang1, Yao Zhang2, Yu Tian1
1School of Wine & Horticulture, Ningxia University, Yinchuan, China.
Journal of food science
|August 14, 2024
概括
超光谱成像和神经网络预测番茄溶性固体含量 (SSC). 较高的肥水平对SSC产生负面影响,但模型显示质量评估的预测准确度很好.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 频谱学是一种光谱学.
背景情况:
- 溶性固体含量 (SSC) 对于番茄的风味和营养价值至关重要.
- 的可用性显著影响番茄的生长,新陈代谢,最终,味道.
- 开发SSC评估的非破坏性方法对于质量控制至关重要.
研究的目的:
- 调查不同处理对番茄SSC的影响.
- 开发和优化SSC的基于高光谱成像 (HSI) 的预测模型.
- 评估使用先进神经网络进行非破坏性质量测试的可行性.
主要方法:
- 使用高光谱成像 (HSI) 技术收集了番茄的光谱数据.
- 使用竞争性适应性重权取样 (CARS) 和代保留信息变量 (IRIV) 进行特征波长提取.
- 包括平行CNN (PCNN) 在内的定制卷积神经网络 (CNN) 模型被构建并优化为SSC预测.
主要成果:
- 番茄SSC显示与肥度的增加有负相关性.
- 汽车-CNN和IRIV-PCNN模型实现了高预测准确度,剩余预测偏差 (RPD) 值分别为1.64和1.66.
- 与传统方法相比,开发的CNN模型表现出优越的性能.
结论:
- 与优化的CNN模型相结合,HSI提供了一种可靠的方法,用于在西红中进行非破坏性SSC预测.
- 通过了解它们对番茄SSC的影响,可以了解管理策略.
- 这项研究为番茄行业的在线,非破坏性质量评估系统提供了基础.
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