Learning to smell for wellness

TL;DR

This paper introduces a weakly supervised domain adaptation framework for electronic nose models, enabling reliable smell perception across different domains with limited target data, demonstrated on beef quality datasets.

Contribution

The work presents a novel domain adaptation method combining supervised and unsupervised learning for olfaction models with limited target domain data.

Findings

The proposed approach performs competitively against baseline methods.

Effective generalization from one domain to another with limited target data.

Validated on diverse beef quality datasets.

Abstract

Learning to automatically perceive smell is becoming increasingly important with applications in monitoring the quality of food and drinks for healthy living. In todays age of proliferation of internet of things devices, the deployment of electronic nose otherwise known as smell sensors is on the increase for a variety of olfaction applications with the aid of machine learning models. These models are trained to classify food and drink quality into several categories depending on the granularity of interest. However, models trained to smell in one domain rarely perform adequately when used in another domain. In this work, we consider a problem where only few samples are available in the target domain and we are faced with the task of leveraging knowledge from another domain with relatively abundant data to make reliable inference in the target domain. We propose a weakly supervised domain adaptation framework where we demonstrate that by building multiple models in a mixture of supervised and unsupervised framework, we can generalise effectively from one domain to another. We evaluate our approach on several datasets of beef cuts and quality collected across different conditions and environments. We empirically show via several experiments that our approach perform competitively compared to a variety of baselines.