Lopez-Molinero A, Castro A, Pino J, Perez-Arantegui J, Castillo J R
Department of Analytical Chemistry, University of Zaragoza, Spain.
Fresenius J Anal Chem. 2000 Jul;367(6):586-9. doi: 10.1007/s002160000433.
Artificial neural networks with unsupervised learning strategy known as Self-Organizing Maps were applied to classify ancient Roman glazed ceramics. Their clay ceramic bodies were analyzed by Inductively Coupled Plasma-Atomic Emission Spectroscopy and the chemical composition obtained was processed by this neural algorithm. The results obtained provide two types of information: firstly, classification of ceramic samples with identification of several groups and secondly, differentiation between the elemental chemical information. It was found that there are certain chemical elements which can be considered as principal and which can serve to differentiate between ceramics, whereas other elements give redundant information and do not contribute to sample differentiation. Seven chemical elements were considered principal and provide the necessary information. Two types of element were identified: 1- a group formed by common elements, such as: Ca, Fe, Mg, Mn and 2- another formed by optional elements: K or Na and Ba or Sr and Al or Ti.
采用具有无监督学习策略的人工神经网络，即自组织映射，对古罗马釉面陶瓷进行分类。通过电感耦合等离子体原子发射光谱法对其粘土陶瓷体进行分析，并将获得的化学成分用这种神经算法进行处理。获得的结果提供了两类信息：第一，对陶瓷样品进行分类并识别出几个组；第二，区分元素化学信息。研究发现，有某些化学元素可被视为主要元素，可用于区分陶瓷，而其他元素则提供冗余信息，对样品区分没有帮助。七种化学元素被视为主要元素并提供了必要信息。识别出两种类型的元素：1 - 由常见元素组成的一组，如：钙、铁、镁、锰；2 - 由可选元素组成的另一组：钾或钠以及钡或锶以及铝或钛。
