Isotopic correlation for Pu composition prediction: Multivariate regresssion approach.

Multivariate regression calibration using multiple linear regression (MLR), principle component regression (PCR) and partial least squares regression (PLSR) algorithm was performed on Pu, Pu, Pu and Pu atom% abundances to predict Pu isotopic abundance. The MLR algorithm was found to be the best among these three algorithms. The effect of Pu composition on the Pu abundance prediction was found to be small but significant especially for achieving high accuracy of <0.5%. PCR and PLSR generated nearly identical results and were inferior to the MLR results. A comparison of MLR results with those obtained by employing seven previously reported empirical methods revealed far superior prediction capability of MLR model. Among the seven empirical models, the best prediction capability was found for Bignan correlation containing Pu isotopic data. The study clearly demonstrates that the production of Pu and Pu has some small correlation and the use of Pu in isotopic correlation for Pu prediction is important to get accurate results.