A Binary Logistic Regression Model as a Tool to Predict Craft Beer Susceptibility to Microbial Spoilage

Improving the microbial and physicochemical shelf life of yufka paste using Lactobacillus plantarum and calcium propionate

Considering the importance of reducing the use of chemical preservatives in food and the increasing attention of consumers to consume food products with minimal additives, the main goal of this research was to study the effect of use of chemical (calcium propionate) and biological (Lactobacillus plantarum) preservatives on increasing shelf life of yufka paste considering its physicochemical and microbial characteristics. For this purpose, three samples of yufka paste were prepared by adding concentrations of 103, 105, and 107 cfu/mL of L. plantarum individually and three samples of paste were prepared by adding the same amount of bacteria in combination with 0.3% calcium propionate and these samples were compared with the control sample (without preservative) and the sample only containing 0.3% calcium propionate. The obtained results showed that different treatments and time had a significant effect on physicochemical properties including pH, moisture, and protein of yufka paste (p < .05). The results of the survival of L. plantarum showed that with increasing time, the survival rate of bacteria increased (p < .05). The pH of the samples showed that the L. plantarum has a significant effect on controlling the chemical quality of yufka during storage. The count of mold and yeast in the combined use of L. plantarum and 0.3% propionate was lower than the single use of propionate chemical preservative, which indicated the very good effect of the green preservative in controlling the moldy spoilage of yufka. Low concentrations of bacteria showed better antimold results than treatments containing bacteria and propionate in a combined form, propionate or control treatment.

Predicting heterotrophic plate count exceedance in tap water: A binary classification model supervised by culture-independent data

Culture-independent data can be utilized to identify heterotrophic plate count (HPC) exceedances in drinking water. Although HPC represents less than 1% of the bacterial community and exhibits time lags of several days, HPC data are widely used to assess the microbiological quality of drinking water and are incorporated into drinking water standards. The present study confirmed the nonlinear relationships between HPC, intact cell count (ICC), and adenosine triphosphate (ATP) in tap water samples (stagnant and flushed). By using a combination of ICC, ATP, and free chlorine data as inputs, we show that HPC exceedance can be classified using a 2-layer feed-forward artificial neural network (ANN). Despite the nonlinearity of HPC, the best binary classification model showed accuracies of 95%, sensitivity of 91%, and specificity of 96%. ICC and chlorine concentrations were the most important features for classifiers. The main limitations, such as sample size and class imbalance, were also discussed. The present model provides the ability to convert data from emerging measurement techniques into established and well-understood measures, overcoming culture dependence and offering near real-time data to help ensure the biostability and safety of drinking water.