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Selection of promising lactic acid bacteria as starter cultures for sourdough: using a step‐by‐step approach through quantitative analyses and statistics
Author(s) -
Corbo Maria Rosaria,
Bevilacqua Antonio,
Campaniello Daniela,
Speranza Barbara,
Sinigaglia Milena
Publication year - 2014
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.6490
Subject(s) - starter , lactobacillus plantarum , lactic acid , food science , bacteria , principal component analysis , microbiology and biotechnology , biology , chemistry , biological system , mathematics , statistics , genetics
BACKGROUND The main goal of this research was to show how to use a qualitative assessment of some technological properties of lactic acid bacteria ( LAB ), combined with the evaluation of the growth index ( GI ), to select promising starter cultures for sourdough . RESULTS Fifty‐four strains of LAB were isolated from a single factory, identified by molecular tools and studied for their growth as a function of NaCl (20, 40 and 65 g L −1 ), temperature (45, 15 and 10 °C), pH 9.2 and acidification in MRS broth. The growth was evaluated through absorbance and data were modelled as GI . GIs were used to build frequency histograms and to run a principal component analysis ( PCA ). In this way, six strains, identified as Lactobacillus plantarum and able to grow in a wide range of conditions (temperature, pH and salt) and/or able to decrease the pH by 1.77–2.0 units, were selected and tested in a model system (flour and water) to study the acidification after 24 h and their viability after 14 days . CONCLUSION The main result of this paper was to show how a simple step‐by‐step approach could be a useful tool to select promising starter cultures for sourdough. The method was based on (1) strain identification, (2) assessment of some traits through the GI , combined with simple statistical approaches (frequency histograms and PCA ), and (3) preliminary validation in model systems. © 2013 Society of Chemical Industry