Liquid scintillation counters are common instruments used in the measurement of pure beta-emitting radionuclides, and while they represent a conventional radiometric technique, they are still competitive for their potential to measure multiple radionuclides simultaneously. In this work, we propose an algorithm based on an artificial neural network (ANN) for the simultaneous analysis of the beta-ray spectra of ³H and ¹⁴C in dual beta-labeled samples using a liquid scintillation counter. We achieved percentage deviations below 5.0% using the proposed algorithm in 16 out of 18 cases, with RMSDs below 1.5% in 17 out of 18 cases. The trained ANN also produced activity ratios with high accuracy even while having to deal with highly fluctuating spectra. Results demonstrate that the rapid predictions with a short measurement time from our proposed ANN method are compatible with the calculated ones from previous studies that were obtained with long measurement times.

液体闪烁计数器是用于测量发射纯β的放射性核素的常用仪器，尽管它们代表了常规的放射技术，但它们同时测量多种放射性核素的潜力仍然具有竞争力。在这项工作中，我们提出了一种基于人工神经网络（ANN）的算法，用于同时分析³ H和¹⁴的β射线光谱使用液体闪烁计数器在双β-标记的样品中添加C。我们使用提出的算法在18个案例中的16个中实现了低于5.0％的百分比偏差，在18个案例中的17个中，RMSD低于1.5％。训练有素的人工神经网络即使在处理波动剧烈的光谱时，也能产生高精度的活度比。结果表明，从我们提出的ANN方法获得的短时间测量快速预测结果与从以前的研究中获得的长测量时间的预测结果是一致的。