使用激光诱导分解光谱学对烤咖啡豆产品的分类:用于多类建模的新型变量选择方法
Yujin Oh1, Heesu Chae1, Hyemin Jung1
1Department of Chemistry, Mokpo National University, Jeonnam 58554, Republic of Korea. shnam@mokpo.ac.kr.
Analytical methods : advancing methods and applications
|March 4, 2025
概括
激光诱导分解光谱 (LIBS) 与k-最近邻居 (k-NN) 结合,有效地对烤咖啡豆进行分类. 这种以Li,Na和Rb为重点的元素分析方法,为咖啡认证提供了一个强大的方法,不受烤的影响.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 咖啡豆的认证对于行业的真实性和一致性至关重要.
- 现有的方法,如光谱学和色谱学有局限性,包括复杂的样本准备或分子数据在烤过程中的不稳定性.
- 激光诱导分解光谱 (LIBS) 提供快速,最少侵入性的基本分析,适合食品认证.
研究的目的:
- 评估将LIBS与分类烤咖啡豆产品的k-最近邻居 (k-NN) 算法相结合的可行性.
- 为准确的咖啡分类模型确定关键的基本歧视变量.
- 在咖啡烤过程中评估所选分析标记物的稳定性.
主要方法:
- 收集了来自韩国市场的12种烤咖啡豆产品的LIBS光谱.
- 对元素发射峰值 (Li,Na,K,Rb,Mg,Ca,C,H,O) 和分子波段 (CN,C2) 的分析光谱.
- 采用了k-NN算法,并使用了新的"类间变化比"来根据元素强度分类豆子,特别是Li,Na和Rb.
主要成果:
- 在烤咖啡豆中,LIBS成功检测到关键元素和分子带.
- 确定了Li,Na和Rb的排放强度是最重要的歧视变量.
- 该k-NN模型实现了高分类精度:96.0%的k=1和98.5%的k=3在缩放后.
- 元素组成 (Li,Na,Rb) 证明是稳定的,并且不受烤过程的影响.
结论:
- 与k-NN相结合的LIBS是验证烤咖啡产品真实性的实用和高效工具.
- 该方法利用对金属元素的高度敏感性进行快速准确的分类.
- 这种方法提供了一种稳定的替代方法,依赖于受烤影响的不稳定分子信息的方法.
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