通过一级分类OC-PLS在有机葡萄汁认证中应用的多块数据.
Carlos H Junges1, Celito C Guerra2, Adriano A Gomes1
1Laboratório de Quimiometria e Instrumentação Analítica (LAQIA), Instituto de Química, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre, Rio Grande do Sul (RS), CEP 91501-970, Brazil.
Food chemistry
|October 19, 2023
概括
一个新的多块回归策略有效地使用各种数据源来验证有机葡萄汁的真实性. 这种方法采用一类部分最小平方 (OC-PLS),在分类果汁真实性方面实现了高精度.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
背景情况:
- 确保有机葡萄汁的真实性对于消费者信任和监管合规性至关重要.
- 传统的分析方法可能难以整合各种数据源以进行全面的真实性评估.
- 需要强大的分类策略,能够处理食品分析中的多源分析数据.
研究的目的:
- 开发和评估一种新的多块回归策略,以提高有机葡萄汁的真实性评估.
- 建立一个强大的分类方法,整合来自各种分析来源的数据.
- 为了比较拟议的多块方法的性能与现有的方法,如DD-SIMCA.
主要方法:
- 实施多块回归技术,特别是一个类的部分最小平方 (OC-PLS) 分类器.
- 序列计算和与先前的回归得分相对正交的数据集成.
- 使用DD-SIMCA方法对可见数据进行比较分析.
主要成果:
- 提出的基于OC-PLS的多块方法在检测有针对性的有机葡萄汁样本方面表现出高的有效性.
- 最好的模型在测试组件上达到100%的灵敏度,89%的特异性和83%的准确性.
- 应用到可见数据时,多块方法的性能优于DD-SIMCA,突出了其在集成各种数据集方面的优势.
结论:
- 开发的多块回归策略为认证有机葡萄汁提供了高效和有效的解决方案.
- 该方法通过利用来自不同分析来源的数据来评估和分类有机葡萄汁.
- 在多块框架中的OC-PLS分类器为食品真实性分析提供了强大的工具.
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