使用VIS/NIR高光谱成像与化学测量的组合模型来预测猪肉的新鲜度
Minwoo Choi1, Hye-Jin Kim1, Azfar Ismail1,2
1Department of Agricultural Biotechnology and Center for Food and Bioconvergence, Seoul National University, Seoul 08826, Korea.
Animal bioscience
|August 30, 2024
概括
这项研究开发了一种改进的模型,用于使用高光谱成像和化学分析来预测猪肉的新鲜度. 综合方法提高了猪背肉质量指标预测的准确性.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 准确预测肉的新鲜度对于食品安全和质量控制至关重要.
- 评估肉类新鲜度的传统方法可能耗时且具有破坏性.
- 超光谱成像 (HSI) 为分析食品质量提供了一种非破坏性的方法.
研究的目的:
- 开发一种针对猪肉新鲜度的增强预测模型.
- 为了提高准确性,将高光谱成像 (HSI) 与化学测量分析相结合.
- 将光谱数据与关键新鲜度指标 (如总细菌数 (TBC) 和挥发性基本 (VBN)) 相关联.
主要方法:
- 在储存了27天的猪腰样本上使用过光谱成像 (HSI).
- 用部分最小平方回归 (PLSR) 与蒙特卡洛数据增强用于模型开发.
- 使用核磁共振 (NMR) 量化代谢资料,并将其与TBC和VBN相关联.
主要成果:
- 确定了64种代谢物,其中一些与TBC和VBN有很高的相关性.
- 开发模型以使用HSI光谱数据预测新鲜度指标,实现TBC的R2p值为0.7220,VBN为0.8392.
- 结合HSI数据和预测代谢物的组合模型显示,预测系数得到改善 (TBC R2p = 0.7583,VBN R2p = 0.8441).
结论:
- 将HSI光谱数据与关键代谢物结合起来,可以更好地预测猪肉的新鲜度.
- 这种综合方法提供了一种更有效和潜在的非破坏性方法来评估猪背肉质量.
- 了解光谱-代谢物相关性可以阐明基于HSI的肉类新鲜度评估背后的机制.
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