Design of a reference value-based sample-selection method and evaluation of its prediction capability

A calibration set comprises the multidimensional space that represents the samples for prediction. The representative ability of a calibration set is a major factor that affects the predictive performance of a multivariate regression. A new reference value (YR)-based sample-selection algorithm that assembles a dependent value (y-value) uniform distribution is presented to assure the representation. The existing typical sample-selection algorithm is used for comparison. A set of soy sauce data is used as a set of typical samples that have a complex solution. Comparing the prediction results, it is shown that YR sample-selection has similar prediction performance to that of sample set partitioning based on joint x-y distances (SPXY), but with a simpler algorithm. The calibration models of the y-reference-included sample sets (SPXY and YR) are more accurate than those of y-reference-excluded sample sets (RS and KS). After modeling with the selected representative samples, the performances of YR and SPXY are comparable to that of full sample modeling with fewer samples. (C) 2015 Elsevier B.V. All rights reserved.