Progress in machine learning-supported electronic nose and hyperspectral imaging technologies for food safety...

The growing concern over food safety, driven by threats such as food contaminations and adulterations has prompted the adoption of advanced technologies like electronic nose (e-nose) and hyperspectral imaging (HSI), which are increasingly enhanced by machine learning innovations. This paper aims to provide a comprehensive review on food safety, by combining insights from both e-nose and HSI technologies alongside machine learning algorithms. First, the basic principles of e-nose, HSI, and machine learning, with particular emphasis on artificial neural network (ANN) and deep learning (DL) are briefly discussed. The review then examines how machine learning enhances the performance of e-nose and HSI, followed by an exploration of recent applications in detecting food hazards, including drug residues, microbial contaminants, pesticide residues, toxins, and adulterants. Subsequently, key limitations encountered in the applications of machine learning, e-nose and HSI, along with future perspectives on the potential advancements of these technologies are highlighted. E-nose and HSI technologies have shown their great potential for applications in food safety assessment through machine learning assistance. Despite this, their use is primarily limited to laboratory environments, restricting their real-world applications. Additionally, the lack of standardized protocols hampers their acceptance and the reproducibility of tests in food safety assessments. Thus, further research is essential to address these limitations and enhance the effectiveness of e-nose and HSI technologies in practical applications. Ultimately, this paper offers a detailed understanding of both technologies, highlighting the pivotal role of machine learning and presenting insights into their innovative applications within food safety evaluation.