Terahertz radiation has many unique characteristics that make it useful for noninvasive mail inspection. While qualitative analysis of mail for suspicious objects is a relatively instantaneous process, quantitative analysis methods may be time-consuming. Multivariate analysis methods, including principal component analysis (PCA), partial least squares (PLS), and interval partial least squares (iPLS), were used for quantitative model building and to predict the content of substances in mail. The optimal spectral interval was selected by analyzing the influence of different spectral regions on the predicted results. A specific interval partial least squares (SiPLS) model was established to improve prediction accuracy and reduce the root mean square error (RMSE) by an order of magnitude. The content of dangerous substances was calculated using SiPLS, established by referencing spectral data of pure substances. Our methods demonstrated that establishing a multiple regression model based on spectral data of pure substances could predict the content of dangerous substances in mail.

太赫兹辐射具有许多独特的特性，使其可用于无创邮件检查。对邮件中的可疑对象进行定性分析是一个相对瞬时的过程，而定量分析方法可能会很耗时。多元分析方法，包括主成分分析（PCA），偏最小二乘（PLS）和区间偏最小二乘（iPLS），用于建立定量模型并预测邮件中的物质含量。通过分析不同光谱区域对预测结果的影响来选择最佳光谱间隔。建立了特定区间偏最小二乘（SiPLS）模型，以提高预测精度并将均方根误差（RMSE）降低一个数量级。使用SiPLS计算危险物质的含量，通过参考纯物质的光谱数据建立的。我们的方法表明，基于纯物质的光谱数据建立多元回归模型可以预测邮件中有害物质的含量。