Lysine and methionine production from agro-waste submerged fermentation using locally isolated lactic acid bacteria

Animal diets are essential to be supplemented with essential amino acids that are imperative for health and good growth. Intensive animal system and environmental constraints required new feeding strategies for the industry to be viable and sustainable. Improving animal productivity can be obtained through the maximum expression of the genetic potential of animals by nutritional approaches. Essential amino acid especially lysine and methionine are widely used as a feed additive in animal’s diet to meet the requirements. However,limited sources and food grade of the essential amino acid supplied in the animal feed formulation has drawn the interest of many researchers to search lactic acid bacteria producing amino acids from the cheap substrate. Lysine and methionine play a major role in improving the efficiency of animal protein production during the early stage of growth. The production of lysine and methionine is possible through a judicious selection of microbial species, and the strains of lactic acid bacteria (LAB) are the potential lysine-methionine producers. Therefore, this study concerns on identifying high-potential isolate from LAB and also focusing on formulating the medium from agro-waste by using various optimization approaches. In this study, isolation of indigenous LAB from different food sources was conducted to isolate superior lysine-methionine producer. A total of 18 isolates from 40 isolates were successfully identified then compared for their lysine and methionine productions. The superior LAB isolate was known as Pediococcus pentosaceus RF-1 which was identified fundamentally using 16S rRNA and scanning electron microscopy. Productions of lysine and methionine by P.pentosaceus RF-1 was further investigated using unstructured kinetic models (Logistic and Luedeking-Piret) by comparing two substrates (MRS and agrowastes used. The models were found suited to describe lysine-methionine productions as a growth-associated process where the values of the nongrowth-associated rate constant (β) for lysine and methionine productions were shown as zero (0). For the subsequent study of optimization, five environmental factors (molasses, nitrogen source, fish meal, glutamic acid and initial medium pH) were investigated in the shake-flask experiment. It showed that the molasses (5 g/L), fish meal (5 g/L), glutamic acid (0.5 g/L) and initial medium pH 7 gave significant effects on the growth of P. pentosaceus RF-1,and lysine methionine productions. Comparisons on the optimization study were conducted between the predictive RSM and ANN models. The RSM using central composite design (CCD) demonstrated 30 experiments of four factors. The RSM suggested that molasses (9.86 g/L), fish meal (10.06 g/L),glutamic acid (0.91 g/L) and initial medium pH 5.3 could enhance the productions of lysine and methionine. Data gathered from the RSM model were then applied in ANN study. The optimal configuration of the ANN model was found to be 4-5-2 with the explanation of incremental back propagation (IBP) algorithm in a combination of a sigmoidal transfer function (output) and linear hidden layer. Prediction of ANN models indicated that using molasses (10.02 g/L), fish meal (18 g/L), glutamic acid (1.17 g/L) and initial medium pH (4.26) was the greatest combination.The cultivation of P. pentosaceus RF-1 for lysine-methionine productions was carried out in 2 L stirred-tank bioreactor using batch and continuous mode of operations. The maximum specific growth rate (μmax) of 0.306 h-1 was obtained during the batch cultivation process. The effects of dilution rates (D) ranging from 0.2 to 0.4 h-1 were performed in continuous operation. The cultivation of P. pentosaceus RF-1 in continuous operation was prolonged to 40 h to attain a steady-state condition. This result implied that the optimum dilution rate was at 0.30 h-1 for the lysine and methionine productivity of 2.09 g/L/h and 0.879 g/L/h, respectively.

Preview Text FBSB 2016 40 - IR.pdf Download (2MB) | Preview

Actions (login required)

View Item