Neural networks are a class of biologically inspired machine learning models that are used for classification and regression problems. This work assesses the classification performance of neural networks on ground-truth fire debris samples using ions that are representative of several compound classes that are typically present in ignitable liquids. An optimal neural network model was selected from a subset of candidate models that were trained on in-silico mixed fire debris samples from the National Center for Forensic Science Substrate and Ignitable Liquid Reference Collection databases. An optimal decision threshold was determined using a defined ratio of misclassification costs. A cost ratio corresponding to a false positive classification having a cost that is 10 times greater than a false negative classification resulted in a decision threshold of log likelihood ratio of 0.966. This decision threshold resulted in a false positive rate of 0.07 and a true positive rate of 0.59 for the ground-truth validation data. This study demonstrates the selection of an optimal decision threshold using ROC analysis and exhibits the potential of neural network models for the evaluation of fire debris evidence.

神经网络是一类由生物学启发的机器学习模型，用于分类和回归问题。这项工作使用代表可燃液体中通常存在的几种化合物类别的离子，评估了地面火碎屑样品上神经网络的分类性能。从国家法医科学底物中心和可燃液体参考物收集数据库的硅内混合火屑样品中经过训练的候选模型子集中，选择了最佳的神经网络模型。使用错误分类成本的定义比率确定最佳决策阈值。与具有比假阴性分类大十倍的成本的假阳性分类的成本比导致对数似然比的判定阈值为0.966。该决策阈值对地面真实性验证数据产生0.07的误报率和0.59的正确率。这项研究演示了使用ROC分析选择最佳决策阈值，并展示了神经网络模型用于评估火屑证据的潜力。