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通过高光谱成像技术改进谷物干燥方法:视觉分析

Sicheng Yang1, Yang Cao2, Chuanjie Li3

  • 1Huanggang Public Testing Center, No.128 Huangzhou Avenue, Huanggang City, Hubei Province, China.

Current research in food science
|February 16, 2024
PubMed
概括
此摘要是机器生成的。

超光谱成像技术 (HSI) 与多变量分析相结合,有效地识别了谷物干燥方法. 使用融合的光谱和纹理特征的反向传播神经网络 (BPNN) 模型取得了最好的识别结果.

关键词:
谷物干燥 谷物干燥超光谱成像技术的使用.部分最小平方模型模型.视觉化的可视化

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科学领域:

  • 农业工程 农业工程
  • 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:

  • 谷物干燥对于储存和质量保存至关重要.
  • 不同的干燥方法会影响谷物特性.
  • 需要使用非破坏性方法来评估干燥效应.

研究的目的:

  • 开发和比较用于识别不同谷物干燥方法的模型.
  • 为此目的,利用高光谱成像技术 (HSI) 和多变量分析.
  • 为了评估光谱和纹理特征融合的有效性.

主要方法:

  • 采集了通过旋转通风,机械和自然方法干燥的谷物的高光谱图像 (388-1065nm).
  • 使用主要成分分析 (PCA) 提取的光谱特征.
  • 使用二次概率统计过提取的纹理特征.
  • 开发了部分最小平方回归 (PLSR) 和反向传播神经网络 (BPNN) 模型.
  • 融合的光谱和纹理特征用于增强识别.

主要成果:

  • 纹理分析显示,与其他方法相比,机械干燥具有独特的特性.
  • 使用融合光谱纹理特征的BPNN模型在区分干燥模式方面表现出卓越的性能.
  • 伪色彩可视化有效地代表了基于融合特征的干燥方法的差异.

结论:

  • 超光谱成像与光谱纹理特征融合相结合,提供了一种有效的非破坏性方法,用于识别谷物干燥技术.
  • BPNN模型提供了一种可靠的方法,可以快速准确地评估干燥后的谷物质量.
  • 这项研究为优化谷物干燥过程和质量控制提供了宝贵的见解.