Processing optimization of probiotic yogurt containing glucose oxidase using response surface methodology

Abstract

Exposure to oxygen may induce a lack of functionality of probiotic dairy foods because the anaerobic metabolism of probiotic bacteria compromises during storage the maintenance of their viability to provide benefits to consumer health. Glucose oxidase can constitute a potential alternative to increase the survival of probiotic bacteria in yogurt because it consumes the oxygen permeating to the inside of the pot during storage, thus making it possible to avoid the use of chemical additives. This research aimed to optimize the processing of probiotic yogurt supplemented with glucose oxidase using response surface methodology and to determine the levels of glucose and glucose oxidase that minimize the concentration of dissolved oxygen and maximize the Bifidobacterium longum count by the desirability function. Response surface methodology mathematical models adequately described the process, with adjusted determination coefficients of 83% for the oxygen and 94% for the B. longum. Linear and quadratic effects of the glucose oxidase were reported for the oxygen model, whereas for the B. longum count model an influence of the glucose oxidase at the linear level was observed followed by the quadratic influence of glucose and quadratic effect of glucose oxidase. The desirability function indicated that 62.32 ppm of glucose oxidase and 4.35 ppm of glucose was the best combination of these components for optimization of probiotic yogurt processing. An additional validation experiment was performed and results showed acceptable error between the predicted and experimental results.

Assessing the use of different chemometric techniques to discriminate low-fat and full-fat yogurts

“…These parameters include the fat level obtained by skimming the milk, the amount of milk powder added to standardize the total solids, the heat treatment of the milk, the inoculum level of starter culture and the stabilizer used (Mortazavian, Ehsani, Mousavi, Sohrabvandi, & Reinheimer, 2006;Mortazavian, Khosrokhavar, Rastegar, & Mortazaei, 2010;Peng, Horne, & Lucey, 2009;Soukoullis, Panagiotidis, Kourell, & Tzia, 2007). In the case of probiotic yogurts, there are additional parameters to be considered, such as the compounds used to supplement the milk (Lucas, Sodini, Monnet, Jolivet, & Corrieu, 2004), interaction between probiotic and starter cultures (Vinderola, Mocchiutti, & Reinheimer, 2002), the inoculation rate of the probiotic culture and its moment of addition during the yogurt processing (Kaur, Mishra, & Kumar, 2009), the quantity of water available during processing (Oliveira & Damin, 2003), oxygen level (Cruz et al, 2010;Cruz et al, 2012a,b) and the supplementation with a prebiotic ingredient (Oliveira, Perego, Oliveira, & Converti, 2009Debon et al, 2012).…”

Developing a prebiotic yogurt: Rheological, physico-chemical and microbiological aspects and adequacy of survival analysis methodology

“…Therefore, a visual evaluation of the models in order to choose the most adequate model is very important. However, due to its increased use in the area of studies involving shelf life and food stability (Cruz et al, 2010a), the Weibull distribution showed an adequate fit and was therefore chosen for the subsequent calculations. The estimates of maximum likelihood of the parameters of the Weibull distribution correspond to: Intercept (l) = 1.8787, and scale factor (r) = 0.7462.…”

“…The prebiotic plain yogurt were processed according to Cruz et al (2010a) with some modifications. Oligofructose (Raftilose P95-BENEO, Orafti, Oreye, Bélgica) was added in the proportions 0%, 2%, 4%, 6% and 8% wt.…”

Effects of whole-wheat flour and bordeaux grape pomace (Vitis labrusca L.) on the sensory, physicochemical and functional properties of cookies

“…Although some researchers still use the 'one-factor -at-a-time' approach to develop new products, the use of experimental design in the development of fortified foods has increased because it facilitates the investigation of the interaction between the effects of variables through mathematical models and response surface methodology. This statistical technique has been used to study the effects of orange pomace in the production of gluten-free bread (O'Shea et al, 2015), in the acceptance of a prebiotic beverage made with cashew nut and passion fruit juice (Rebouças et al, 2014), in the development of cookies enriched with added guava peel (Bertagnolli et al, 2014), and in the optimization of probiotic yogurts (Cruz et al, 2010).…”