Real-time Monitoring and Evaluation of Indoor Odor with Portable Gas Chromatograph Combined with Deep Learning

Abstract: Objective The aim of this study is to develop a portable gas chromatograph, combined with machine learning, to achieve on-site VOC collection and rapid odor evaluation.
Methods We used a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) to establish an odor intensity prediction model. Due to the small amount of data collected, we used a Generative Adversarial Network (GAN) to generate VOC data for each odor intensity category to enhance model training.
Results After generating the data, we used CNN-LSTM to establish the model again and compared it with Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gradient Boosting Decision Trees (XG-Boost). The results showed that the test accuracy after using GAN to generate data was better than the original data.
Limitations Future work will focus on further optimizing the model and expanding the dataset to improve the accuracy and stability of the prediction.
Conclusion This study shows that by using deep learning and generative adversarial networks, we can effectively predict the odor intensity inside the car, thereby improving the air quality inside the car. In addition, we will explore the application of this method to air quality prediction under other environmental conditions. This provides new possibilities for future air quality monitoring and improvement. As our equipment is portable and the model structure is small enough to be directly embedded into the device, it can achieve on-site VOC collection and rapid odor evaluation. This provides new possibilities for future air quality monitoring and improvement.