Multi-pathlength method to improve the spectrometric analysis accuracy based on “M + N” theory

Abstract

The pathlength is a special factor in near-infrared (NIR) spectroscopy analysis. It is a physical variable that is similar to the sample concentration, and is a variable combined with the geometric dimension deviation of the sample pool, installation error of the light source and incident optical fiber. The decrease of accuracy caused by the pathlength changes is a difficult problem in quantitative spectrometric analysis. The proposed “M + N” theory is a measurement theory in which the quantity impacting the measurement accuracy is classified as M elements and N factors. The theoretical connotation and three application methods of N factors are proposed and analyzed based on the definitions of the theory. “M + N” theory provides a set of guidelines to find a variety of ways to solve the negative impact of N factors on the measurement accuracy. In order to verify the effectiveness of three application methods, an experiment was done to predict the concentration of intra-lipids by single pathlength and multi-pathlength modelling. The experimental results showed that the prediction accuracy of the multi-pathlength was higher than that of the single pathlength. The experimental results also showed that the distribution of the calibration set should be larger than that of the predication set. In this case, the predication accuracy will be improved significantly. The connotation provides a basis for the measurement, and three methods of N factors can be used to improve the accuracy of quantitative spectrometric analysis. Using the multi-pathlength method to build a model, the errors caused by the replacement of the sample pool and instrument installation are decreased.