Recent advances and challenges in electronic nose technology for agricultural commodities and food quality assessment

Abstract

Traditional methods for analyzing agricultural produce and food quality, such as chromatography and sensory evaluation, are often expensive, time consuming, and require skilled professionals, making them hard to implement for rapid and large-scale testing. The advent of Electronic nose (E-Nose) technology presents a promising alternative by emulating the human olfactory system through sensor arrays that identify volatile compounds and produce distinctive response patterns. This review examines the principles, evolution, and applications of E-nose systems for assessing the quality of food and agricultural products across diverse categories, including fish, meat, dairy products, tea, coffee, and edible oils. These systems provide non-destructive and smart analytical solutions for modern food-processing industries. Their role in identifying contamination, freshness assessment, authenticity, and monitoring fermentation plays a crucial role in modern food processing. Recent advancements integrating artificial intelligence (AI) and machine learning (ML) methodologies have significantly enhanced the efficacy of E-nose systems, enabling superior pattern recognition, data interpretation, and precise prediction capabilities. This enables objective and accurate quality assessment. Despite these advantages, the widespread adoption of this technology is hindered due to sensor drift, low sensitivity, high initial costs, and the difficulty of processing such complex data. Future research should focus on enhancing sensor stability, formulating resilient data analytics models, and decreasing system costs to facilitate scalability. Overall, E-nose technology has potential to transform food-quality assessment by providing the food industry with rapid, non-destructive, and intelligent analytical methods.