The ability to characterize used nuclear fuel (UNF) is important for nuclear nonproliferation safeguards, criticality safety, and fuel storage. Multiple efforts have been made to estimate the burnup (BU), initial enrichment (IE), and cooling time (CT) based on multivariate models of isotopic concentrations and radiation signatures of the fuel. This work provides a comparison of multivariate modeling techniques and extends previous work by quantifying the uncertainty of the best model to predict each characteristic. Model inputs used are simulated gamma and neutron emissions from UNF of varying BU, IE, and CT. Modeling techniques explored include Ordinary Least Squares Regression (OLS), Principal Component Regression (PCR), and Partial Least Squares Regression (PLS). Multiple PCR and PLS models were built based on different variable selection methods, such as cross validation and Akaike Information Criteria. The OLS model predictions have a root mean square percent error (RMSPE) of less than 10%, but the models are very unstable. The PCR models exhibit a trade-off between accurate and stable predictions. The best performing PCR and PLS models have similar predictions errors, but the PLS models are favored due to their stability. The best model for each characteristic is a single output PLS model based on cross validation. The uncertainty of each of these models, based on their prediction variance and biases, is 0.220 GWd/MTU, 0.051% U-235, and 0.694 years for the BU, IE, and CT models, respectively. By building a 95% prediction interval based on the corresponding uncertainty of each characteristic, 1.97% of the BU predictions, 23.03% of the IE predictions, and 100% of the CT predictions lack 95% confidence that they are within the prescribed accuracy requirement for the characteristic.

表征废旧核燃料（UNF）的能力对于核不扩散保障，临界安全性和燃料存储很重要。基于同位素浓度和燃料的辐射特征的多变量模型，已经做出了多种努力来估计燃耗（BU），初始富集（IE）和冷却时间（CT）。这项工作提供了多变量建模技术的比较，并通过量化最佳模型的不确定性来预测每个特征来扩展先前的工作。使用的模型输入是来自BU，IE和CT不同的UNF的模拟伽马和中子发射。探索的建模技术包括普通最小二乘回归（OLS），主成分回归（PCR）和偏最小二乘回归（PLS）。基于不同的变量选择方法（例如交叉验证和Akaike信息标准）构建了多个PCR和PLS模型。OLS模型预测的均方根误差均方根（RMSPE）小于10％，但模型非常不稳定。PCR模型在准确和稳定的预测之间进行权衡。表现最佳的PCR和PLS模型具有相似的预测误差，但是PLS模型由于其稳定性而受到青睐。针对每个特性的最佳模型是基于交叉验证的单个输出PLS模型。根据它们的预测方差和偏差，每个模型的不确定性分别为BU，IE和CT模型，分别为0.220 GWd / MTU，0.051％U-235和0.694年。通过基于每个特性的相应不确定性建立95％的预测间隔1。