The role of smart optical biosensors and devices on predictive analytics for the future of aquaculture systems

Abstract

Recirculating aquaculture systems (RAS) have been rising quickly in the last decade, representing a new way to farm fish with sustainable aquaculture practices. This system is an environmentally and economically sustainable technology for farming aquatic organisms by reusing the water in production. RAS present some benefits compared with other aquaculture methods, for instance, allows the minimization of water usage and disease occurrence, the absence of antibiotics in these systems, shortens the production cycle, functions as a water treatment system, allows the improvement of the feed conversion, and a reduction in the alteration of coastal habitat, among others. However, this is a complex system with complex interactions between the number of fish and water quality parameters, which can compromise the fish welfare. Currently, there is a huge gap in the global aquaculture sector in terms of smart sensors for cortisol (stress hormone), bacteria, water pollutants, volatile organic compounds and micro/nano-plastics assessment. This sector does not measure such critical parameters which brings a weak understanding of the wellbeing of fish. Therefore, it is crucial to implement point of care (POC) sensors for those critical parameters' assessment via multiparameter solution and predictive analytic capabilities for data supply. This work presents an overall introduction about the impact of the RAS on fish production and its necessity as protein as well as the actual solutions for those problems. Additionally, it reviews the actual state of the art in terms of potential multiparameter POC sensors and predictive analytical approaches that have been investigated in recent years for future application in aquaculture with the aim to guide the researchers on the sector's needs. Additionally, future perspectives are also described in order to digitize the aquaculture sector with novel optical systems and biosensing elements.