建模基于功率的变量选择,以对SIMCA进行严格的一类分类
Mateus Pires Schneider1, Cristina Malegori2, Paolo Oliveri2
1Institute of Chemistry, UFRGSAv. Bento Gonçalves, 9500, Porto Alegre, RS, CEP 91591 - 970, Brazil.
Analytica chimica acta
|September 29, 2025
概括
一个新的算法在一类分类 (OCC) 中选择软独立类类模拟 (SIMCA) 的关键变量. 这种方法提高了模型的解释性和节性,而不会牺牲食品认证中的分类性能.
科学领域:
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
- 分析化学 分析化学
背景情况:
- 一级分类 (OCC) 对于食品认证至关重要,因为只有目标样本可用.
- 传统软独立类类类模拟建模 (SIMCA) 通常使用全光谱或色谱数据,从而产生复杂的模型.
- 模型的节性和可解释性是开发强大的分类模型的关键挑战.
研究的目的:
- 开发和验证SIMCA的变量选择算法.
- 提高OCC.中的模型节性和可解释性.
- 评估拟议的算法的性能与传统的SIMCA相比.
主要方法:
- 一个新的变量选择算法,模拟功率选择器与SIMCA (MPS-SIMCA),是开发的.
- 该算法整合了三个标准:MP-紧度相关性,MP非增长率和最小MP值.
- 该算法在食用油,绿茶和橄油的UV-Vis,NIR和HPLC-CAD数据集上进行了测试.
主要成果:
- 与全频谱SIMCA相比,MPS-SIMCA实现了同等或更好的分类性能.
- 选择的变量在化学上具有意义,并与已知的组成标志物保持一致.
- MPS-SIMCA展示了优越的模型紧性和可解释性.
结论:
- 基于内部类结构的变量选择对SIMCA来说是可行的.
- MPS-SIMCA算法为食品认证提供了一个强大的和可解释的方法.
- 这种方法增强了SIMCA在现实场景中的实际应用,样本可用性有限.
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