The aim of the study was evaluation of growth and metabolic activity of Lactobacillus rhamnosus GG during fermentation of leguminous porridges (soybean fl our, soybean, chickpea fl our, chickpea, white bean, red bean, speckled bean, green lentil, husked lentil, yellow pea), and the evaluation of their stability during storage. A mixture of leguminous sample with water was inoculated a er sterilization with equal number of L. rhamnosus GG, to obtain 5 log cfu/g in the porridge. Fermentation was led at 37 °C during 10 hours and storage at 5 °C for 21 days. Monitoring of the lactobacilli counts, pH value, and concentration of organic acids during fermentation and storage was done. Calculation of growth and metabolic parameters during fermentation and storage period was performed by the mechanistic model of Baranyi and Roberts. L. rhamnosus GG was able to grow up to 6.8-7.9 log cfu/g during fermentation, cell density during storage period was stable, except whole soybean, yellow pea and red bean. Metabolic activity of L. rhamnosus GG during fermentation caused decrease of pH value to the fi nal 5.6-6.0, increase of lactic and acetic acid concentration to 89.3-341.7 mg/kg and 129.2-525.2 mg/kg, respectively. During storage period, metabolic activity of L. rhamnosus GG continued.

The aim of this study was to evaluate suitability of legumes as carriers for probiotic strain Lactobacillus rhamnosus GG, leading to the development of new probiotic foods for consumers who have to restrict or dislike dairy products. The growth and metabolic activity of the probiotic strain Lactobacillus rhamnosus GG during fermentation of waterbased leguminous porridges, prepared from soy bean, soy fl our, green lentil, husked lentil, white bean, speckled bean, red bean, yellow pea, chickpea, and chickpea fl our, were monitored. Viable cell counts, pH values, and contents of organic acids were analysed during static fermentation of autoclaved substrates at 37 °C for 48 h. Lactobacillus rhamnosus GG was able to grow up to the counts higher than 6 log CFU g -1 (measured values in the range of 7.8-8.9 log CFU g -1 ), which is legislative limit for labelling food as probiotic. pH values of fermented substrates varied between 4.0-6.0, concentration of lactic acid ranged from 99.9 to 687.7 mg kg -1 , and level of acetic acid varied from 266.1 to 1182.0 mg kg -1 .

Allergic reactions to foods represent severe actual problems for mankind having increased global character. Adverse food reactions are divided to food allergy, an immunological response to food, and food intolerance, a non-immune reaction with allergy-like symptoms. It is estimated that 6-8 % of children and 1-2 % of adults suffer from food allergy. The prevalence of food intolerance in adults is no more than 5-6 %, however in infants and young children, it is varied from 0.3 % to 20 %. Allergy is caused by different food allergens (milk proteins, cereals, nuts, fruits and vegetables), while there is frequent cross-allergy among them. Food intolerance is adverse reaction resulting from enzyme deficiencies, pharmacological reactions, and response to toxic or irritant components of food. Focusing on dairy products and cereals, the impact of fermentation in reducing food intolerance or allergenicity is reviewed in this paper.

New analytical techniques, the GreenLight™ system for rapid enumeration of total viable counts (TVC) were used to estimate the numbers of bacteria inoculated in different levels in broth nutrient media. The new detection methodology was compared with agar plating EN ISO 4833:2003 method showing excellent correlation. The following coefficients of determination R2 = 0.985 and 0.999 were calculated for aerobic Pseudomonas aeruginosa and facultative anaerobic E. coli, respectively. After calibration, the system based on the principle of quenching of luminescence intensity and lifetime of an oxygen-sensitive dye by sample O2 consumed during microbial growth enables to determine the number of microorganisms within less than 24 hours. The higher microbial load the shorter time for determination of viable count is needed. In case of simple food matrix for example, the results can be reached even within one shift of production.