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- MakaleYalnız künye2026Scopus SJRQ1Classification 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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İncele - MakaleAçık Erişim2024Scopus SJRQ1Thermal 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.
AtıfScopus9WoS8OpenAlex10İncele - MakaleAçık Erişim2020Scopus SJRQ1Effect of cellulose nanofiber-based coating with chitosan and trans-cinnamaldehyde on the microbiological safety and quality of cantaloupe rind and fresh-cut pulp. Part 1: Microbial safety
Zhang, Jin; Ozturk, Samet; Singh, Rakesh K.; Kong, Fanbin
This study aimed to evaluate the effect of cellulose nanofiber (CNF)-based coating containing chitosan (CHI) and trans-cinnamaldehyde (TC) on microbial growth of foodborne pathogens and spoilage microorganisms on cantaloupe rind and fresh-cut pulp. Coated/uncoated cantaloupe rind and pulp samples were inoculated with Salmonella enterica, Escherichia coli O157:H7 and Listeria monocytogenes and subsequently subjected to storage for 20 d at 4 °C or 12 d at 22 °C. Results showed that coating with only CNF (0.5%, dry-base) was effective in the elimination of S. enterica and E. coli O157:H7. Adding CHI (1%, w/w) and TC (1%, w/w) into CNF-based coating enhanced film formation on the surface and increased the antimicrobial activity on rind sample, but the reduction effect was less significant on pulp samples. The CNF (0.5%, dry-base) +CHI (1%, w/w) +TC (1%, w/w) coating resulted in 2.23, 2.43 and 3.22 log10 CFU/cm3 reduction of S. enterica, E. coli O157:H7 and L. monocytogenes, respectively, on pulp samples after 20 d at 4 °C. The formulation also significantly inhibited the growth of natural spoilage microorganisms, and no growth was observed up to 18 d at 4 °C. Results indicated that CNF-based coating significantly improved microbial safety of cantaloupe rind and fresh-cut pulp during storage by forming a physical barrier reducing activities of oxygen-dependent microorganisms.
AtıfScopus13WoS9OpenAlex10İncele