Optimization of the Viability of Probiotics in a New Fermented Milk Drink by the Genetic Algorithms for Response Surface Modeling

ABSTRACT: Calcium gluconate (0.0 to 0.5%), sodium gluconate (0.0 to 1.0%), and N-acetylglucosamine (0.0 to1.0%) were added to skim milk to retain the viability of Lactobacillus acidophilus and Bifidobacterium longum . To carry out response surface modeling, the regression method was performed on experimental results to buildmathematical models. The models were then formulated as an objective function in an optimization problemthat was consequently optimized using a genetic algorithm approach to obtain the maximum viability of theprobiotics. The genetic algorithms (GAs) were examined to search for the optimal value. The results indicatedthat GAs were very effective for optimizing the activity of probiotic cultures.Keywords: probiotics, optimization, genetic algorithms, response surface modeling Introduction L ACTOBACILLUS ACIDOPHILUS AND BIFIDOBACTERIA MUST RETAIN viability and activity in food carriers to meet the suggested“therapeutic minimum” at the time of consumption (Playne 1994). Several factors have been claimed to affect the viability of probioticcultures in fermented milk products. The culture conditions, chem-ical composition of the fermentation medium (for example, carbo-hydrate source), final acidity, growth promoters and inhibitors, in-cubation temperature, and fermentation time and storagetemperature have all been identified as having effects on the man-ufacturing and storage of yogurt (Young and Nelson 1978; Hammanand Marth 1983; Kneifel and others 1993; Lankaputhra and Shah1995). In food microbiology, during the past few years, much effort hasbeen directed to developing models describing the combined ef-fects of the factors for microbe growth. Response surface method-ology (RSM) is a collection of statistical and mathematical tech-niques useful for developing, improving, and optimizing processes.The main advantage of RSM is the reduced number of experimentaltrials needed to evaluate multiple parameters and their interactions(Porretta and others 1995; Lee and others 2000). It was successfullyused for application in finding the optimum producing conditionsof the dairy product Kou Woan Lao (Weng and others 2001). The ex-perimental data were used to build mathematical models using theregression method. Once an appropriate approximating model isobtained, this model may be analyzed to determine the optimumconditions for the process.Genetic algorithms (GAs) are search procedures that imitate thenatural evolution process and can be used for the computation ofthe global maximum or minimum of a function (Mitchell 1996). Genetic algorithms differ from other search techniques in that theysearch among a population of points and use probabilistic ratherthan deterministic transition rules. As a result, genetic algorithmssearch more globally (Wang 1997). More details regarding GAs willbe included in the next section.The purpose of this research was to study the addition of calci-um gluconate (0.0 to 0.5%), sodium gluconate (0.0 to 1.0%) and N-acetylglucosamine (0.0 to 1.0%) in milk to optimize the viability ofthe probiotics by RSM and genetic algorithms. The experimentaldata were used to build mathematical models using the regressionmethod. The mathematical models were formulated as an objectivefunction (as in an optimization problem) and were optimized us-ing the genetic algorithm approach to obtain the maximum viabilityfor the probiotics. The endpoint was to provide a new optimizationmethod to improve the growth of probiotics in dairy products.