Berrueta Luis A, Alonso-Salces Rosa M, Héberger Károly
Departamento de Química Analítica, Facultad de Ciencia y Tecnología, Universidad del País Vasco/Euskal Herriko Unibertsitatea, P.O. Box 644, E-48080 Bilbao, Spain.
J Chromatogr A. 2007 Jul 27;1158(1-2):196-214. doi: 10.1016/j.chroma.2007.05.024. Epub 2007 May 13.
Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.
由于现代分析仪器提供了大量的分析信息，数据分析已成为分析化学中的一项基本任务。有监督模式识别旨在基于实验数据建立一个分类模型，以便根据未知样品的测量特征模式将其分配到先前定义的样品类别中。本文综述了食品分析中最常用的有监督模式识别技术的基础，特别强调了测量数据的实际要求，并讨论了可能出现的常见误解和错误。还综述了过去两年文献中出现的有监督模式识别在食品化学领域的应用。
