Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/14197
  Classification and detection of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes using Fourier-transform near infrared spectroscopy coupled with machine learning

  Ozturk, Samet; Huang, Lihan; Hwang, Cheng-An; Sheen, Shiowshuh

  This study was conducted to investigate the potential use of Fourier-transform near-infrared (FT-NIR) spectroscopy combined with machine learning (ML) algorithms to accurately identify three foodborne pathogens, including Salmonella spp., Listeria monocytogenes, and Escherichia coli O157:H7. Each bacterial strain (two per pathogen) was individually cultured and purified through sequential washing using ethanol–deionized (DI) water solutions. Each purified culture was transferred into each well of a 96-well cell plate covered with custom-cut filter paper and then vacuum dried at 50 °C under 20 kPa for 1 h. The absorbance spectra of dehydrated bacterial cells were then acquired across the range of 1000–2400 nm using a diffuse reflectance probe connected to a FT-NIR process analyzer. To identify the optimal classification pipeline, the acquired spectra were analyzed using ten different pre-processing methods, three feature selection methods, and supervised algorithms including partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). Furthermore, the accuracy of the developed ML pipelines was further enhanced through under-sampling and boosting steps. The results demonstrated that SVM, RF, ANN and CNN outperformed PLS-DA, where the classification accuracies were above 90%. The findings demonstrate that using Savitzky-Golay first derivative (SG1) filtering to pre-process the full spectra followed by SVM classification yielded the highest accuracy, achieving 95.3% overall accuracy in ML pipeline. This study highlights the powerful capability of FT-NIR spectroscopy coupled with ML algorithms to detect and identify foodborne pathogens on dehydrated surfaces in process environments.

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• MakaleYalnız künye2026/r/gumushane/13810
  Quality deterioration kinetics of roasted peanut matrices under fixed-time and estimated-lethality at various water activities

  Islam, Shahriar; Li, Zhiwei; Wang, Xin; Tasci, Caner; Ozturk, Samet; Erdoǧdu, Ferruh; Zhang, Qing; Liu, Shuxiang

  Water activity and particle size are critical determinants of quality stability and safety in low-moisture, lipid-rich foods. However, their combined effects during thermal processing of roasted peanut matrices remain insufficiently quantified. This study evaluated quality deterioration in roasted peanut matrices with different structural forms: crushed, flour, powder, and butter. Samples were equilibrated to water activity values of 0.11–0.92 at 25 °C and heated at 80 °C for 120 min. Colour, acid value, sensory score, and changes in relative fatty acid composition were measured. Kinetic experiments were also conducted at aw,25°C 0.32 using D-value-based thermal schedules at 85, 90, and 95 °C to relate microbial lethality to quality loss. Water activity strongly affected quality stability, with the predicted quality-favourable values varying slightly among responses. Therefore, aw,25°C (0.32) was selected as a practical condition for subsequent kinetic evaluation. Matrix performance differed between the fixed-time and D-value-based treatments. These findings describe quality deterioration under fixed-time and D-value-based thermal schedules.

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• MakaleYalnız künye2026/r/gumushane/13764
  A dynamic predictive model for Bacillus cereus growth from spores under temperature abuse conditions

  Juneja, Vijay K.; Osoria, Marangeli; Gao, Juan; Kapoor, Harsimran Kaur; Ozturk, Samet; Purohit, Anuj; Mishra, Abhinav

  Bacillus cereus, a common foodborne pathogen, can cause emetic and diarrheal food poisoning. The associated outbreaks are commonly caused by temperature abuse during food cooling, storage, and transportation. This study developed a model to predict B. cereus growth from spores based on isothermal growth data collected at 10–54.4 °C in tryptic soy broth. A four-strain spore cocktail was heated at 80 °C for 10 min and inoculated at an initial level of ∼2 log CFU/mL. Growth curves at temperatures showing growth (15–50 °C) were fitted using the Baranyi model. The average h0 value was 3.04. The temperature dependence of the maximum specific growth rate was characterized using the modified Ratkowsky model. The estimated theoretical minimum and maximum growth temperatures were 8.94 °C and 54.11 °C, respectively. The lag phase duration was related to temperature using a hyperbolic function. All models showed good agreement with the observed data, with satisfactory fit statistics (R2 = 0.953–0.998 and RMSE = 0.073–0.904). The Baranyi differential equations were solved numerically using a fourth-order Runge–Kutta method to build the dynamic model. Model validation was conducted under two sinusoidal non-isothermal temperature profiles (10–30 °C for 30 h and 25–45 °C for 23 h). The accuracy factor (Af) and bias factor (Bf) values were 1.2117 and 1.2117 for the 10–30 °C profile, and 1.1359 and 1.1288 for the 25–45 °C profile, respectively. These results demonstrated that the dynamic model could precisely predict B. cereus growth and be used as a baseline reference for food system research and risk assessment.

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• MakaleAçık Erişim2024/r/gumushane/14133
  Thermal resistance of Listeria monocytogenes, Escherichia coli O157:H7 and Salmonella in animal fat – Kinetic analysis and mathematical modeling

  Ozturk, Samet; Huang, Lihan; Hwang, Cheng-An; Sheen, Shiowshuh

  The objective of this study was to evaluate the effect of fat on thermal resistance of L. monocytogenes, E. coli O157:H7, and Salmonella spp. A 4-strain cocktail of each microorganism was inoculated to beef tallow and heated isothermally at temperatures between 55 and 80℃. All survival curves did not follow the 1st-order inactivation kinetics but conformed to a two-stage linear pattern. The first stage was markedly less heat-resistant than the second, as manifested by significantly lower D values. The z values of E. coli O157 H7 and Salmonella spp. were 11.8 °C and 12.3 °C in the first stage (z1) but increased to 23.7 °C and 20.8 °C in the second stage (z2), respectively. For L. monocytogenes, while the z values were similar for both stages (z1 = 19.6 °C and z2 = 18.5 °C), the second stage D values are 3.6–5.9 times of those in the first stage. One-step analysis was used to fit the nonlinear curves to the Weibull model, yielding < 1 exponents for the model (0.495, 0.362, and 0.282, respectively, for L. monocytogenes, E. coli O157:H7, and Salmonella spp.), suggesting gradually increased thermal resistance during heating. The experimental results showed that these microorganisms could resist heating for longer time and at higher temperatures in tallow than they do in regular meats containing lower levels of fat. The kinetic models can be used to develop thermal processes to properly inactivate pathogens contaminated in the fat portions of meat products or other high fat products.

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• MakaleYalnız künye2023/r/gumushane/12799
  Increased heat tolerance and transcriptome analysis of Salmonella enterica Enteritidis PT 30 heat-shocked at 42 ℃

  Qiu, Yan; Ozturk, Samet; Cui, Xinyao; Qin, Wen; Wu, Qingping; Liu, Shuxiang

  In this study, we compared the heat tolerance parameter (D65℃) values of Salmonella enterica serovar Enteritidis PT 30 (S. Enteritidis ) heat adapted at different degrees (at 42 ℃ for 20–180 min) and cultivated using two methods. The treated group with the highest D65℃ value (LP-42 ℃-60 min) and the untreated groups (Control-TSB and Control-TSA) were subjected to transcriptome analysis. Heat-adaptation increased the D65℃ values of S. Enteritidis by 24.5–60.8%. The D65℃ values of the LP-42 ℃-60 min group (1.85 ± 0.13 min, 7.7% higher) was comparable to that of the Control-TSA. A total of 483 up- and 443 downregulated genes of S. enteritidis were identified in the LP-42 ℃-60 min group (log2fold change > 1, adjusted p-value < 0.05). Among these genes, 5 co-expressed and 15 differentially expressed genes in the LP-42 ℃-60 min and Control-TSA grops possibly contributed to the high D65℃ values of S. Enteritidis. The Rpo regulon was involved in the heat adaptation of S. Enteritidis, as evidenced by the significant upregulation of rpoS, rpoN, and rpoE. KEGG enrichment pathways, such as biosynthesis of secondary metabolites, tricarboxylic acid, and ribosomes were identified and mapped to reveal the molecular mechanisms of S. enteritidis during heat adaptation. This study quantified the enhanced heat tolerance of S. Enteritidis heat adapted at different degrees of heat-adaptation. The results of this study may serve as a basis for elucidating the molecular mechanisms underlying the enhanced heat tolerance at the transcriptome level.

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