追踪冷猪肉的营养和质量变化:12个月的研究使用7类肉类参数和VIS/NIR光谱学
Xi Tang1, Hui Xiao1, Ting Luo1
1National Key Laboratory for Swine Genetic Improvement and Germplasm Innovation, Jiangxi Agricultural University, Nanchang 330045, China.
Food chemistry
|March 31, 2025
概括
正确冷的猪肉可以保持1-2年质量. 视觉和近红外光谱 (VIS/NIRS) 与机器学习相结合,可以准确预测冷猪肉的储存时间,帮助食品安全和库存管理.
科学领域:
- 食品科学 食品科学 食品科学
- 肉类科学 肉科学
- 分析化学 分析化学
背景情况:
- 冷猪肉对于粮食安全和价格稳定至关重要.
- 在冷储存期间评估营养和物理化学变化是一项挑战.
- 了解随着时间的推移冷猪肉的质量对于有效的库存管理至关重要.
研究的目的:
- 量化12个月的冷猪肉在-20°C的肉质变化.
- 通过使用VIS/NIRS.开发冷猪肉储存时间的预测模型.
- 评估机器学习算法评估冷猪肉质量的潜力.
主要方法:
- 一个为期12个月的冷储存实验 (-20°C) 在50个猪肉长肌肉上进行.
- 在7个类别中对62个肉质成分进行量化.
- 使用VIS/NIRS和9个机器学习算法 (AdaBoost) 开发一个存储时间分类模型.
主要成果:
- 先知模型表明猪肉质量在1-2年内良好,在4年后恶化.
- 现型相关性揭示了脂肪酸 (阿拉基酸) 和氨基酸 (甲氨酸) 的相互作用和异常.
- 一个基于VIS/NIRS的存储时间分类模型使用SpecimIQ传输光谱和AdaBoost实现了85.8%的准确性.
结论:
- VIS/NIRS提供了一个实用的解决方案,用于精确识别冷猪肉的储存时间.
- 机器学习模型可以准确预测冷猪肉的质量和储存时间.
- 调查结果为管理冷猪肉储备和确保食品安全提供了宝贵的见解.
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