Optimizing pigment production from agricultural waste using metaheuristic-based algorithms

Due to the uncontrolled industrial applications of synthetic pigments that can cause a serious hazard to human health and the environment, the scientific community skewed towards natural colors. The simplest and efficient method to increase pigment production is by manipulating the medium. Among classical and statistical methods, one factor at a time and response surface methodology (RSM) is the most widely used in medium optimization. However, the main drawback of these methods is tedious wet experiments need to be conducted to predict the output for a new input data and prior to data processing and analytic for decision making. In the past few years, the rapid advances in the field of metaheuristic optimization algorithm have provided a solution in optimization problems. In this study, metaheuristic optimization scheme, together with the mathematical model which is regression analysis have been implemented to minimize time and cost of wet-lab experiments by increasing the pigment productions using the proposed compact experiments. Moreover, the predictive optimization performance and sensitivity analysis of metaheuristic algorithm have been evaluated to validate the results, and the authenticity has been proven by wet laboratory experiments. Analysis of the optimization showed that the percentage improvement for the proposed compact experiment which is particle swarm optimization (PSO) model improved from RSM model by 1.32%, while the percentage improvement for all compact experiments was better than multiple polynomial model (MPR) model with the highest PSO percentage of 2.0507%. Hence, the experimental findings revealed that, the metaheuristic-based approach successfully predicted the optimum fermentation parameters condition and concentration with better achievement on pigment production by using proposed compact experiment

Comparison of particle swarm optimization and response surface methodology in fermentation media optimization of flexirubin production

At present, response surface methodology (RSM) is the most preferred method for fermentation media optimization. However, in the last two decades, artificial intelligence algorithm has become one of the most efficient methods for empirical modelling and optimization. One of the popular developed approaches is Particle Swarm Optimization (PSO), which is used in optimizing a problem. This paper focuses on comparative studies between RSM and PSO in fermentation media optimization for the production of flexirubin production using Chryseobacterium artocarpi CECT 8497T. Two methodologies were compared for in terms of their modeling, sensitivity analysis, and optimization abilities. All experiments were performed accordingly to box-behnken design (BBD), and the generated data was analyzed using RSM and PSO. The sensitivity analysis performed by both methods has given comparative results. Based on the correlation coefficient, the model developed with PSO was found to be superior to the model developed with RSM. The result shows that PSO gives a better pigmentation yield with optimal fermentation concentration

Statistical and nature-inspired metaheuristics analysis on flexirubin production

Nowadays, demand for natural pigments has increased dramatically due to the awareness of the toxicity of some synthetic pigments. Because of the high cost of growth medium for natural pigment production, various studies have been carried out to explore medium which are less costly, such as agricultural waste. This study highlight on the application of firefly algorithm (FA) and bat algorithm (BA) in optimizing yellowish-orange pigment production (flexirubin) from the agricultural waste material. At present, response surface methodology (RSM) is the most preferred statistical method in optimizing pigment production. However, in the last two decades, nature-inspired metaheuristics approach has been used extensively in the fermentation process and have continually improve the efficiency in the optimization problem especially in pigment production. This study compared the analytics studies of RSM, FA and BA in the estimation of fermentation parameters (Lactose, Ltryptophan, and KH2PO4) in flexirubin production from Chryseobacterium artocarpi CECT8497T. All models provided similar quality predictions for the above three independent variables in term of flexirubin production with bat algorithm showing more accurate in estimation, with the coefficient value of 98.87% compare to RSM 98.20% and FA 98.38%