Nilgün Öncül, Kader Tokatli, Zeliha Yildirim
The inhibitory effect of lactoccocin BZ against Escherichia coli on fresh beef
Die hemmende Wirkung von Lactoccocin BZ gegen Escherichia coli auf frischem Rindfleisch
During the processing of fresh meat, there is a risk of contamination with foodborne pathogenic E. coli. The use of bacteriocins is considered to ensure the safety of fresh meat. In this context, the impact of lactococcin BZ on the E. coli population in fresh beef has been investigated. The antibacterial activity of lactococcin BZ was observed in meat samples inoculated with E. coli both during and after attachment condition (103 and 106 CFU/mL), and over a 12 days storage period in refrigeration (4°C). Different amounts of lactococcin BZ (ranging from 400 to 3200 AU/mL) were applied to fresh meat for varying treatment durations (0–30 minutes). Following the application of lactococcin BZ at levels of 800, 1600, and 3200 AU/mL during attachment, E. coli counts were immediately reduced by 3.62 log units. At high inoculum dose, lactococcin BZ (3200 AU/mL) decreased the pathogen by approximately 6 log units in 5 minutes during attachment. E. coli exhibited sensitivity to lactococcin BZ (400, 1600, and 3200 AU/mL) both in low and high inoculum doses after attachment to fresh beef. Furthermore, the inhibitory effect of lactococcin BZ increased with its concentration over the 12 days of refrigerated. In conclusion, lactococcin BZ demonstrated inhibitory effect against E. coli in fresh beef, suggesting its potential use as a biopreservative in the meat industry.
Cemalettin Baltaci, Kubra Yilmaz, Seyda Ozturk, Omer Karpuz
Vinegar production from different cherry laurel fruits and investigation of some of their physicochemical properties
Essigherstellung aus verschiedenen Kirschlorbeerfrüchten und Untersuchung einiger ihrer physikochemischen Eigenschaften
In this study, seven types of traditional cherry laurel fruit vinegars (CLFV) belonging to three different species were produced. Vinegars, including those produced during the study and supplied from the market, were analyzed for total acidity, volatile acidity and non-volatile acidity, pH, ash, oxidation number, iodine number, ester, mineral substance, alcohol, total solids, and total sugar-free solids. Analyses and the ranges of the results that were found in vinegar samples were as: Acidity percentage (as acetic acid) 1.68–4.13 %, volatile acidity (as acetic acid) 5.70–18.27 g/L, non-volatile acidity (as tartaric acid) 3.31–36.10 g/L, alcohol percentage 0.01–0.48, pH 2.24–3.57, total sugar 6.54–283.56 g/L, total solids 22.01–486.56 g/L, total sugar-free solids 14.11–217.73 g/L, ash 0.22–3.84 g/L, ester 16.80–61.14, oxidation number 389.60–394.05 and iodine number 35.20–386.88. For the color analysis, the values were found to be between 10.91 and 25.79 for L*, 5.15 and 15.08 for a*, between –4.83 and 8.72 for b*, between 3.54 and 16.83 for ΔE*. Based on their physicochemical properties, the vinegars numbered N4, N5, N6, N8, and N9 are considered suitable for vinegar production compared to the samples numbered N7 and N10. The raw material contents of N7 and N10 vinegars differ from the others and inhibit the development of acidity. Additionally, it has been determined that the physicochemical properties of N4, N5, N6, N8, and N9 vinegar samples are superior to those of apple and grape vinegars. According to the study result, cherry laurel fruits (CLF) are suitable for natural vinegar production under optimum conditions, and further field studies should be carried out to apply these findings at the industrial level.
Irem Kılınç, Berna Kılınç, Yakut Gevrekçi, Çigdem Takma
Artificial neural networks models used for fishery products
Modelle künstlicher neuronaler Netze für die Fischereitechnik
The statistical methods have many benefits in acquiring results in a variety of areas such as the optimization the conditions of processing, estimating the shelf life of food products, predicting the bacteria growth on foods, and also predicting the risk formation of chemicals and pathogenic bacteria on food products. The Artificial Neural Network (ANN) model is just one of many mathematical models used today, and its applications in many areas of food and aquatic products will be enhanced in the future. In addition, in the near future, the most appropriate ANN model will be determined by combining the data produced with advanced processing technologies or obtained from food products and different models for each type of food product, and relible predictions can be made for the future. In the lights of the importance of the subject in this review; the meaning, importance, types, formulations of the ANN models were explained. Additionally, the usages of the ANN models not only in aquaculture and fisheries studies, but also the usages of the ANN models for evaluation of safety assessments of the processing technologies of fishery products were highlighted. Moreover, the usages of the ANN models for determining the freshness and shelf life of fishery products were reviewed. It is expected that future development of various mathematical models, as well as studies to adapt the use of these models together, will benefit the aquaculture, fisheries, food and seafood processing industries.