多变量曲线分辨率,其次是部分最小方程差异分析,并与Vis-NIR超光谱成像相结合,用于大米认证
Maryam Dehbasteh1, Nima Naderi Tehrani1, Hadi Parastar1
1Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran.
Food research international (Ottawa, Ont.)
|January 29, 2026
概括
这项研究使用可见短波长高光谱成像 (Vis-SWNIR HSI) 和多变量曲线分辨率交替最小平方 (MCR-ALS) 来验证大米的真实性. 该方法准确地识别了大米的来源,品种,并检测了伪造,确保了食品的质量.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
背景情况:
- 米是全球的主要粮食,但改行为威胁到它的完整性.
- 验证大米的来源和品种对于消费者信任和行业标准至关重要.
- 目前用于验证大米真实性的方法可能耗时且具有侵入性.
研究的目的:
- 开发和验证一种使用高光谱成像来验证大米真实性的非侵入性方法.
- 根据原产地和品种来区分大米样本.
- 为了检测大米样本中的改.
主要方法:
- 使用可见短波长高光谱成像 (Vis-SWNIR HSI) 来扫描163个完整的米样.
- 使用多变量曲线分辨率交替最小方形 (MCR-ALS) 来提取光谱和空间配置文件.
- 化学分析,包括主要成分分析 (PCA) 和部分最小平方区分分析 (PLS-DA),用于分类.
主要成果:
- MCR-ALS算法成功地根据原产地 (94.4%的准确率) 和品种 (82.75%的准确率) 区分了大米样本.
- 伪造检测实现了100%的分类准确性.
- 这标志着MCR-ALS首次用于完整大米认证的已知应用.
结论:
- 与MCR-ALS相结合的Vis-SWNIR HSI是快速,非侵入性食品认证的实用和有效工具.
- 开发的方法提供了一种具有成本效益的解决方案,以确保大米的质量并防止欺诈行为.
- 这种方法对食品行业来说具有很大的潜力,可以确保产品的真实性.
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