Sony AI, SONY Corporation, Tokyo, Japan.
PLoS One. 2023 Aug 11;18(8):e0289881. doi: 10.1371/journal.pone.0289881. eCollection 2023.
Recent research has attempted to predict our perception of odorants using Machine Learning models. The featurization of the olfactory stimuli usually represents the odorants using molecular structure parameters, molecular fingerprints, mass spectra, or e-nose signals. However, the impact of the choice of featurization on predictive performance remains poorly reported in direct comparative studies. This paper experiments with different sensory features for several olfactory perception tasks. We investigate the multilabel classification of aroma molecules in odor descriptors. We investigate single-label classification not only in fine-grained odor descriptors ('orange', 'waxy', etc.), but also in odor descriptor groups. We created a database of odor vectors for 114 aroma molecules to conduct our experiments using a QCM (Quartz Crystal Microbalance) type smell sensor module (Aroma Coder®V2 Set). We compare these smell features with different baseline features to evaluate the cluster composition, considering the frequencies of the top odor descriptors carried by the aroma molecules. Experimental results suggest a statistically significant better performance of the QCM type smell sensor module compared with other baseline features with F1 evaluation metric.
最近的研究试图使用机器学习模型来预测我们对气味的感知。嗅觉刺激的特征化通常使用分子结构参数、分子指纹、质谱或电子鼻信号来表示气味剂。然而，在直接比较研究中，特征化选择对预测性能的影响报告得很少。本文针对几种嗅觉感知任务进行了不同的感官特征实验。我们研究了气味描述符中香气分子的多标签分类。我们不仅在细粒度的气味描述符（如“橙色”、“蜡质”等）中进行单标签分类，还在气味描述符组中进行单标签分类。我们创建了一个包含 114 种香气分子的气味向量数据库，使用 QCM（石英晶体微天平）型气味传感器模块（Aroma Coder®V2 Set）进行实验。我们将这些气味特征与不同的基线特征进行比较，以评估簇的组成，同时考虑携带香气分子的顶级气味描述符的频率。实验结果表明，与其他基线特征相比，使用 F1 评估指标，QCM 型气味传感器模块的性能有显著提高。
