A probiotic based product using multi-strain Bacillus species and predictive models for shrimp growth following probiotic intervention

Abstract

Probiotic bacteria in aquaculture found to have significant effect on health of shrimp and growth by changing the microbial composition of water. The primary objective of the study is to develop a probiotic based product to improve the yield of shrimp by using multi-strain Bacillus species. Towards this, various Bacillus strains, namely, B. subtilis, Bacillus clausii, B. cereus, B. Megaterium, B. pumilus and Bacillus polymyxa were collected and confirmed the strains through biochemical tests in subculture. Further, it is found that all the six strains exhibited good antifungal, antibacterial, antagonistic and enzyme activities. Multi-strain probiotic product as feed for the growth of shrimp was prepared by using the solid state fermentation technique and quality check was done to make sure no contamination. After the product development, the efficacy of the product has been tested by conducting field studies. The parameters such as (i) temperature (ii) pH (iii) dissolved oxygen (iv) total ammonia nitrogen and (v) shrimp weight & (vi) length were investigated for about 120 days and measurements were taken at every 5-day interval. The average initial weight and length of shrimp is 4.2 ± 0.2 g and 0.8 ± 0.1 cm respectively. The average values obtained for 120 days include (i) Temperature (°C): Control = 28 ± 0.3; Treated = 28 ± 0.15 (ii) pH: Control = 8 ± 0.19; Treated = 8 ± 0.12 (iii) Dissolved oxygen (ppm): Control = 5 ± 0.23, Treated = 5 ± 0.21 (iv) Total ammonia nitrogen (ppm): Control = 2 ± 0.2; Treated = 0.8 ± 0.01 (v) Shrimp weight (gm): Control = 16 ± 0.21; Treated = 22 ± 0.14 (vi) Length (cm): Control = 6 ± 0.14; Treated = 8 ± 0.26. The average survival rate was found to be 88.67 ± 1.15% in probiotics ponds and 70.00 ± 3.0% in controlled ponds. From the overall study, it is observed that the weight and length of shrimp has been increased significantly up to sixty days compared to control environment. Two-way ANOVA has been performed to understand the interdependent relation between the input and output variables. Advanced statistical models were developed by using the concepts of Artificial neural network, Multivariate adaptive regression splines and Relevance vector machine to predict the shrimp weight and length. The predicted shrimp weight and shrimp length is found to be in close agreement with those of the corresponding experimental observations. The models are found to be robust, reliable and efficient.