Advances in predicting human olfactory perception: from data acquisition to computational models

Abstract

Researchers endeavor to collect odor information to prepare for the exploration of odor prediction. Such researches will require machine learning algorithms with excellent data processing capabilities. Odor perception also requires advanced sensor performance, including sensitivity, selectivity, stability and minimum lower limits of detection. Gas sensing technologies play a key role in identifying gas mixtures by providing critical response signals and characterization data. Here, we explore the recent advances in gas sensing technologies to meet the pivotal needs of human olfactory perception. First, we summarize the databases of olfactory perception. Then, the fundamental sensing principles of gas chromatography-mass spectrometry and metal-oxide semiconductor, optical, and electrochemical sensors for molecular odor prediction are briefly introduced. Finally, we connect the odor-related sensing technology with suitable machine learning algorithms, encompassing areas like artificial neural networks (ANN), random forest (RF), K nearest neighbors (KNN), support vector machine (SVM), extreme learning machine (ELM), gradient boosting decision tree (GBDT), and decision tree (DT) approaches. In the future, machine learning is expected to help build an understanding of the link between odors and human olfactory sensory mechanisms, consequently making a significant contribution to olfactory research.