Concepts from data science, machine learning, deep learning and artificial neural networks are spreading in many disciplines. The general idea is to exploit the power of statistical tools to interpret complex and, in many cases, non-linear data. Specifically in analytical chemistry, many chemometrics tools are being developed. However, they tend to get more complex without necessarily improving the prediction ability, which conspires against parsimony. In this report, we show how non-linear analytical data sets can be solved with equal or better efficiency by easily interpretable modified linear models, based on the concept of local sample selection before model building. The latter activity is conducted by choosing a sub-set of samples located in the neighborhood of each unknown sample in the space spanned by the latent variables. Two experimental examples related to the use of near infrared spectroscopy for the analysis of target properties in food samples are examined. The comparison with seemingly more complex chemometric models reveals that local regression is able to achieve similar analytical performance, with considerably less computational burden.

数据科学、机器学习、深度学习和人工神经网络的概念正在许多学科中传播。总的想法是利用统计工具的力量来解释复杂的，在许多情况下是非线性数据。特别是在分析化学中，正在开发许多化学计量学工具。然而，它们往往会变得更复杂，但不一定会提高预测能力，这与简约主义相悖。在本报告中，我们展示了如何基于模型构建之前的局部样本选择概念，通过易于解释的修改线性模型以同等或更好的效率解决非线性分析数据集。后一种活动是通过选择位于潜在变量跨越的空间中每个未知样本附近的样本子集来进行的。研究了两个与使用近红外光谱分析食品样品中的目标特性相关的实验示例。与看似更复杂的化学计量模型的比较表明，局部回归能够实现类似的分析性能，而计算负担要小得多。