# Process Optimization for Antarctic Krill (_Euphausia superba_)
## 1 Introduction  
Antarctic krill (_Euphausia superba_) are shrimp-like planktonic crustaceans normally found in the Antarctic Ocean, and it is the primary producer in the Antarctic food chain system [1, 2]. According to the latest statistics, Antarctic krill capture is more than twice the total fisheries production worldwide [3]. The vast potential resources of Antarctic krill, as well as its unique status in the Antarctic, are a growing concern. As a result, the development and usage of Antarctic krill resources is an unavoidable trend, since the amount of fishing resources worldwide continues to decrease.  
Antarctic krill are protein-rich and have a distinctive flavor. At present, Antarctic krill are only developed and used to make krill meal paste, krill powder, krill oil, and other products [4, 5]. Recently, bioactive substances derived from Antarctic krill have been reported [6, 7, 8]. The functional and chelating peptides isolated from Antarctic krill exhibit substantial biological activity and functionality [7, 8]. However, large-scale manufacturing of bioactive compounds from raw Antarctic krill requires more extensive approaches and a greater amount of raw Antarctic krill. The development of high-value Antarctic krill products is therefore critical for their optimal usage. Antarctic krill sauce (A&S) has been developed to meet Chinese tastes while also filling a gap in high-value Antarctic krill processing [2].  
Process optimization is highly important for improving product quality. Traditional aquatic product processing relies heavily on sensory evaluation and experiences from humans, which lack scientific value and cannot objectively reflect the quality of the products, potentially affecting consumer acceptability. The Back propagation (BP) neural network is one of the most widely used artificial networks [9]. It determines the ideal weights and thresholds via the gradient descent approach and repeated learning training, resulting in lower error value prediction for sensory evaluation. When combined with the fact that the genetic algorithm (GA) can effectively generate multi-level and multi-factor global optimization design algorithms, it can effectively mitigate the impact of local optimal solutions [10]. [PERSON] et al. [11] effectively used the response surface method (RSM) and the GA-BP models to extract taxanes from _Taxus cuspidata_. [PERSON] and [PERSON] [12] employed the RSM and artificial neural network coupled with GA models to optimize the process of the synergistic digestion of potato waste and aquatic weeds. The improved approach resolved the acid extraction issue and increased the conversion rate by 6%. As a result, using a GA-BP neural network may successfully minimize the human subjective impact on the sensory evaluation of AkS, allowing for the optimization of the AkS production process.  
In this study, we designed an AkS and optimized its production process using salt addition, rock sugar addition, the addition of an oil-to-material ratio as a single factor, and a sensory evaluation scores as response values. The optimized process from the RSM was then submitted to BP neural network for training before being integrated with a GA to globally optimize the AkS production process. The experimental results provide the optimal production process for AkS and basic application data for the development and utilization of high-value Antarctic krill products.