Recent research works on multi-way chromatographic data for analytical calibration, classification and discrimination are reviewed. Focus is directed towards measured data arrays in the form of matrices, three- and four-dimensional mathematical objects, depending on the number of elution time and instrumental detection modes. Chemometric models typically used to process these data and to obtain the maximum amount of information on the studied systems are discussed. The advantages in processing such data for complex samples are highlighted, both for quantitative and qualitative purposes. In the former case, the achievement of the second-order advantage which permits analyte quantitation in the presence of uncalibrated constituents is perhaps the most relevant contribution to this field. For classification and discrimination, the processing of multi-way chromatographic data provides highly compressed information which can then be submitted to appropriate algorithms. This represents an additional advantage, because individual analytes do not need to be fully resolved and quantitated.

综述了多路色谱数据用于分析校准，分类和鉴别的最新研究成果。根据洗脱时间和仪器检测模式的数量，重点针对矩阵，三维和三维数学对象形式的测量数据阵列。讨论了通常用于处理这些数据并获得有关所研究系统的最大信息量的化学计量模型。无论是定量还是定性的目的，都强调了处理此类复杂样品数据的优势。在前一种情况下，实现二阶优势（允许在未校准的成分存在下进行分析物定量）可能是对该领域最相关的贡献。对于分类和歧视，多路色谱数据的处理提供了高度压缩的信息，然后可以将其提交给适当的算法。这代表了另一项优势，因为不需要单独解析和定量分析物。