从RGB图像到超光谱图像的光谱重建:以牛肉中的谷氨酸指数检测为例
Fujia Dong1, Ying Xu2, Yingkun Shi3
1School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Food chemistry
|October 12, 2024
概括
频谱重建 (SR) 将RGB图像转换为超频谱图像 (HSI),用于实时应用. MST++模型在从RGB重建HSI以进行牛肉分析时实现了高准确度,使得可靠的谷氨酸指数预测成为可能.
科学领域:
- 食品科学 食品科学 食品科学
- 图像处理 图像处理
- 频谱学是一种光谱学.
背景情况:
- 超光谱成像 (HSI) 提供了丰富的光谱信息,但通常是昂贵和缓慢的.
- 频谱重建 (SR) 旨在从标准RGB图像中恢复HSI,从而实现低成本的实时HSI应用.
- 复杂的食物系统对准确的RGB到HSI重建提出了挑战.
研究的目的:
- 研究复杂食品矩阵中RGB转换为HSI的最新SR算法的可行性.
- 评估和确定最佳的SR模型,用于从RGB数据中重建HSI.
- 评估基于SR的HSI用于预测牛肉中的谷氨酸指数的性能.
主要方法:
- 在360个牛肉样本上评估了11个SR算法.
- 使用多变量相关性分析来确认RGB作为宽带信息的投影.
- 为模型选择提出了一个全面的质量属性指标 (PSNR-Params-FLOPS).
- 使用SSIM和t-SNE进行一致性评估.
- 使用轻量级变压器开发了用于Raw-HSI,RGB和SR-HSI的预测模型.
主要成果:
- 在MST++模型中,RMSE=0.015,PSNR=36.70和SSIM=0.9253.3的SR性能优越.
- SR-HSI预测了谷氨酸酸指数,其性能与原始HSI相似 (R2P=0.8422,RPD=2.46与R2P=0.8526,RPD=2.69).
- 多变量分析证实了RGB能够表示全面的光谱信息的能力.
结论:
- 在复杂的食品系统中,MST++是RGB转换为HSI的最佳SR模型.
- SR-HSI显示了实时,低成本应用的巨大潜力,如食品质量评估.
- 该研究为评估SR算法及其在食品分析中的应用提供了一个框架.
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