Few-Shot Time-Series Classification of Chemosensor Data
Authors: Mahesh Bhargavi , Hettenkofer Sebastian , Graunke Thorsten , Garbas Jens-Uwe 
Published: July 2021

Recent advances in machine olfaction have demonstrated deep learning algorithms' capabilities in mining patterns in chemosensor data [1, 2]. While these algorithms can perform effective automated feature extraction, they are invariably dependent on a large amount of data. However, there is a pressing need to develop olfactory systems that learn rapidly and adapt continuously in time-critical applications such as gas leak monitoring or fire detection. The primary objectives of this work include the demonstration of rapid-learning and generalization capabilities on chemical sensor data. A simple application is considered in this direction where a system's readiness for rapid classification of gas mixtures is tested. The system consists of a low-cost metal-oxide sensor array that responds to gas mixtures from a headspace. The application in focus is the binary classification of gas sensor data (beverage vs. air). The primary choice of model for algorithm development is a convolutional neural network due to its promising inferencing capabilities. Owing to these algorithms' data-hungry nature, a partial meta-learning approach, known as transfer learning, is adopted. A baseline convolutional neural network is trained on the sensor data to distinguish beverages from the air. This baseline model is fine-tuned on new beverages, referred to as novel classes, using a one-shot and a five-shot regime. Results show that the fine-tuned models successfully distinguish new beverages from a minimal amount of data, besides overcoming the challenges posed by the chemical sensors, such as short-term and long-term drifts in the measurements. The resulting models perform favourably with an average test accuracy of 0.9165 for one-shot learning and 0.9170 for five-shot learning, given that the average baseline model test accuracy is 0.9356. Despite fine-tuning on novel classes, the model preserves its generalizability and is immune to catastrophic forgetting, a shortcoming often faced due to iterative training of neural networks.

Method

The aroma of fruit juices results from a complex combination of several volatile organic compounds such as esters, aldehydes, alcohols, ketones, and hydrocarbons [3] and depends on ambient factors such as humidity, temperature, etc. While the composition of each fruit juice varies depending on the fruits used, a fruity odour characterizes any fruit beverage. Therefore, an overarching motivation for this study is to detect the presence of fruity odour with chemical sensors. 50 ml samples of commercial juices, namely, orange, apple, blackcurrant, and multivitamin, are used for data collection. The sensor setup consisted of eight AS-MLV metal-oxide (MOX) sensors, humidity, and temperature sensors. The MOX sensors are heated by supplying pulse-modulated voltages for 12 seconds. Voltages are recorded continuously from four MOX sensors placed in the juice headspace. The data collection is carried out over multiple days. Classification of time-series voltages is performed by a convolutional neural network composed of a feature extractor and a classifier (refer Fig. 1). The transfer learning approach, inspired by Sun et al. [4], is split into meta-training and meta-testing stages. In the meta-training stage, both the feature extractor and the classifier are trained with data corresponding to Air and Beverage (a selection of three juices) classes, resulting in a baseline model. A novel juice's data is considered in the meta-testing phase. Classifier weights are fine-tuned with a small support set, comprising one or five samples carrying maximum information. Four experiments are performed holding each juice in the meta-testing dataset. The models are tested for catastrophic forgetting effects after fine-tuning. Furthermore, zero-shot testing of the baseline model is done to justify the need for fine-tuning and to determine the performance improvement on the new composition of Beverage class.

Results

Meta-training and meta-testing results are presented in Fig. 2. The average validation accuracy during meta-training is 0.9356. Upon fine-tuning with a one-shot regime, the resulting average accuracy on the query set is 0.9165, whereas the five-shot regime resulted in a slightly higher performance of 0.9170. This concludes that a pretrained convolutional neural network can learn and generalize a novel juice from a small amount of data. On the other hand, the low zero-shot test results depict that, apart from apple juice, the network cannot generalize the novel juice samples to the Beverage class without requiring at least one sample of the novel juice. The fine-tuned network does not undergo significant catastrophic forgetting, likely due to the shallow network architecture. The current results suggest that a model pretrained with a combination of juices can detect a new juice upon fine-tuning with a significantly small number of samples. The collected data can be used in the future for performance optimization through compensation of the influence of dynamic conditions such as room air quality, temperature, and humidity and the juice temperature.

References

[1] P. Peng, X. Zhao, X. Pan, and W. Ye, “Gas Classification using Deep Convolutional Neural Networks,” Sensors, vol. 18, no. 2, p. 157, Jan. 2018, doi: 10.3390/s18010157.

[2] X. Hu, Q. Liu, H. Cai, and F. Li, “Gas Recognition under Sensor Drift by using Deep Learning,” in Advances in Intelligent Systems and Computing, Springer Berlin Heidelberg, 2014, pp. 23–33.

[3] M. Riu-Aumatell, M. Castellari, E. López-Tamames, S. Galassi, and S. Buxaderas, “Characterisation of Volatile Compounds of Fruit Juices and Nectars by HS/SPME and GC/MS,” Food Chemistry, vol. 87, no. 4, pp. 627–637, Oct. 2004, doi: 10.1016/j.foodchem.2003.12.033.

[4] Q. Sun, Y. Liu, T. Chua and B. Schiele, “Meta-Transfer Learning for Few-Shot Learning,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 403-412, doi: 10.1109/CVPR.2019.00049.

Figure 1