Optimizing a submerged Monascus cultivation for production of red pigment with bug damaged wheat using artificial neural networks

Abstract

The combined effect of temperature, agitation speed, and light on red pigment production by Monascus purpureus (M. purpureus) Went DSM 1604 using bug damaged wheat was studied using an artificial neural network (ANN). Information retrieved from the ANN was used to determine the optimal operating conditions for pigment production by M. purpureus using bug damaged wheat meal. The developed ANN had R 2 values for training, validation, and testing data sets of 0.993, 0.961, and 0.944, respectively. According to the model, the highest pigment production of 1.874 absorbance units at 510 nm (A510 nm) would be achieved at 29 o C and 150 rpm under light conditions. The mean value of the experimental results obtained under these optimum conditions was 1.787±0.072 A510 nm, corresponding to a pigment yield of 35.740 A510 nm/g. The study showed that bug damaged wheat can be used as a substrate for red pigment production by M. purpureus.