In the light of recent food fraud cases, the issue of food authenticity is receiving increasing attention. New analytical methods and evaluation approaches are currently being proposed. In this framework, the evaluation of mass spectral profiles constitutes a promising avenue, e.g. for the determination of food origin. Relevant evaluation approaches include principal component analysis, artificial neural networks, random forests, support vector machines, etc. The aim is to derive decision rules for the assignation of unknown samples to different classes. These decision rules are derived on the basis of samples whose origin is known—the training set. Typically, a reliable evaluation requires that the number of samples should be considerably larger than the number of features. However, in the framework of multiparameter mass spectrometry methods, the required ratio between sample and parameter numbers is inverted, with, e.g. 100 samples versus 10 000 features. In this paper, two approaches for the establishment of reliable decision rules in spite of low sample numbers are discussed.

中文翻译：

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多参数方法的有效机器学习算法

鉴于近期的食品造假案件，食品真伪问题越来越受到关注。目前正在提出新的分析方法和评估方法。在这个框架中，质谱图的评估构成了一条有前途的途径，例如用于确定食物来源。相关的评估方法包括主成分分析、人工神经网络、随机森林、支持向量机等。目的是推导出将未知样本分配到不同类别的决策规则。这些决策规则是根据来源已知的样本——训练集——推导出来的。通常，可靠的评估要求样本数量应远大于特征数量。然而，在多参数质谱方法的框架内，样本数和参数数之间所需的比率是倒过来的，例如 100 个样本对 10 000 个特征。在本文中，讨论了在样本数量较少的情况下建立可靠决策规则的两种方法。