An Integrated Handheld Electronic Nose for Identifying Liquid Volatile Chemicals Using Improved Gradient-Boosting Decision Tree Methods

Abstract

The main ingredients of various odorous products are liquid volatile chemicals (LVC). In human society, identifying the type of LVC is the inner logic of many applications, such as exposing counterfeit products, grading food quality, diagnosing interior environments, and so on. The electronic nose (EN) can serve as a cost-effective, time-efficient, and safe solution to LVC identification. In this paper, we present the design and evaluation of an integrated handheld EN, namely SMUENOSEv2, which employs the NVIDIA Jetson Nano module for running the LVC identification method. All components of SMUENOSEv2 are enclosed in a handheld case. This all-in-one structure makes it convenient to use SMUENOSEv2 for quick on-site LVC identification. To evaluate the performance of SMUENOSEv2, two common odorous products, i.e., perfumes and liquors, were used as the samples to be identified. After sampling data preprocessing and feature generation, two improved gradient-boosting decision tree (GBDT) methods were used for feature classification. Extensive experimental results show that SMUENOSEv2 is capable of identifying LVC with considerably high accuracies. With previously trained GBDT models, the time spent for identifying the LVC type is less than 1 s.