A Training Set Sample Selection Method Based on SIMPLISMA for Robust Calibration in Near-Infrared Spectral Analysis

摘要

In the field of quantitative analysis using near-infrared (NIR) spectroscopy, calibration is necessary. Training set sample selection is an important aspect of multivariate calibration, and it has attracted the attention of several researchers. The aim of this technique consists of the following two items: 1. Choosing the smallest set of training samples that can be used without significantly compromising the prediction performance of the model. It is often used to save computational work (1-8). In some situations, the representative samples used in multivariate calibration can benefit both the modeling efficiency and the prediction performance. 2. Training set sample selection is also an important part in the context of transfer of multivariate calibration models (9-11), in which a subset of samples must be measured in different spectrometers. The measurements are then used to standardize the instrumental response of one or more spectrometers (slaves) with respect to a single one (master). In this case, a small number of representative samples are chosen so that they will contain sufficient information about the variations of the samples because of different measurement conditions. This benefits the practical application.