Smart Sensing and AI for Monitoring Rancidity in PUFA-Rich Agricultural Products

Matrices containing polyunsaturated fatty acids (PUFA), including nuts, edible oils, seeds, and fish oils, are highly susceptible to oxidative rancidity, which negatively impacts quality, safety, and consumer acceptance. This chapter examines smart farming strategies for monitoring rancidity in two representative PUFA-rich food systems: cashew nuts and edible oils. In both case studies, the detection of oxidative degradation products responsible for rancidity utilizes advanced volatile organic compound (VOC) analysis through gas chromatography (GC) and electronic nose (e-nose) technologies. The application of machine learning (ML) models to VOC data has identified key markers in cashews, such as propanal and hexanal, that indicate quality grades. For edible oils, real-time rancidity monitoring on storage containers employs chemoreceptor-based colorimetric sensors, supported by novel algorithms that track changes in color indexes associated with rancidity. This chapter further explores the development and application of chemometric sensor platforms, integrating multi-sensor data fusion and AI-based pattern recognition to enable accurate, automated quality assessment in agriculture. These sensor systems may be integrated with cloud-based Internet of Things (IoT) platforms and blockchain-enabled traceability, facilitating a convergence of sensing, computation, and data transparency essential to smart agricultural ecosystems. Critical considerations such as sensor calibration, interoperability, cost constraints, and field deployment are addressed, along with recommendations for scalable implementation. Such integrated smart agricultural systems have the potential to transform quality monitoring of PUFA-rich foods by linking advanced sensor technologies to practical applications, thereby enhancing supply chain reliability, reducing post-harvest losses, and increasing consumer confidence.