Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2027/r/gumushane/13587
  Efficacy of blue light-assisted ClO2 for Salmonella decontamination on basil leaves: inactivation kinetics, quality preservation, and microbiota dynamics

  Lian, Yi; Liu, Songlin; Ozturk, Samet; Wei, Xinyao

  Frequent Salmonella outbreaks associated with basil leaves highlight the need for effective postharvest decontamination. Conventional microbial sanitizers often fail to inactivate pathogens while preserving product quality, creating a demand for novel decontamination technologies suitable for basil. In this study, the microbial safety of basil under typical consumption scenarios was evaluated and a blue light-assisted ClO2 (COM) decontamination process for basil leaves was developed. Mathematical models were used to describe the microbial inactivation kinetics, and a systematic comparison was conducted against traditional single-chemical sanitizers (H2O2, HClO, ClO2, and 75% ethanol) in terms of microbial safety and final product quality. Results showed that simulated cooking scenarios, including pizza baking and hot soup immersion, were insufficient to reduce the Salmonella levels below the detection limit (<10 CFU/g) under a high initial load of 9.7 log CFU/g. Compared to the single-chemical treatments, 100 mg/L COM treatment achieved significantly higher decontamination (p < 0.05) by reducing Salmonella and background microbiota by 4.8 and 3.5 log CFU/g, respectively, with treatment time of 15 min. The inactivation kinetics of Salmonella showed a non-linear inactivation kinetics, which was captured by the Weibull model with a low RMSE value (RMSE = 0.16), although the AICc value (11.37) indicated limited model parsimony. Furthermore, the COM treatment significantly reduced bacterial richness (with the Chao1 index decreasing from 316 to 71) and effectively inactivated key spoilage organisms (Pseudomonas) as well as members of the Enterobacteriaceae, Actinomycetota, and Bacillota groups. Compared with the 100 mg/L ClO2 treatment alone (1.40 mg/kg), the 100 mg/L COM treatment resulted in a ClO2 residue concentration below the detection limit, without significantly altering visual appearance, color, chlorophyll content, total phenolics, or polyphenol oxidase activity (p > 0.05). Overall, blue light-assisted ClO2 treatment effectively reduced microbial contamination and reduced ClO2 residues while preserving the physical quality of basil, representing a promising decontamination approach for the fresh produce industry.

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• MakaleAçık Erişim2023/r/gumushane/11435
  Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements

  Bowler, Alexander; Ozturk, Samet; di Bari, Vincenzo; Glover, Zachary J.; Watson, Nicholas J.

  In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learning models offer the potential for non-invasive process monitoring. However, methods are required to ensure the models are robust to changing ultrasonic measurement distributions as a result of changing process conditions. As it is unknown when these changes in distribution will occur, domain adaptation methods are needed that can be applied to newly acquired data in real-time. In this work, yoghurt fermentation processes are monitored using non-invasive ultrasonic sensors. Furthermore, a transmission based method is compared to an industrially-relevant non-transmission method which does not require the sound wave to travel through the fermenting yoghurt. Three machine learning algorithms were investigated including fully-connected neural networks, fully-connected neural networks with long short-term memory layers, and convolutional neural networks with long short-term memory layers. Three real-time domain adaptation strategies were also evaluated, namely; feature alignment, prediction alignment, and feature removal. The most accurate method (mean squared error of 0.008 to predict pH during fermentation) was non-transmission based and used convolutional neural networks with long short-term memory layers, and a combination of all three domain adaption methods.

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• MakaleYalnız künye2021/r/gumushane/14760
  A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics

  Aykas, Didem Peren; Menevseoglu, Ahmed

  In this research, the determination of green pea and peanut adulteration in pistachio by portable Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopy was studied. A rapid method to detect adulteration in pistachio is a necessity in the food industry because of its high commodity value and being one of the most consumed tree nuts. Pistachio nut is a target for economically motivated adulteration, and green pea, spinach, and peanut are the most seen adulterants in pistachio. Portable FT-MIR and FT-NIR spectrometers are simple, non-invasive, rapid, easy to transport, and cheaper alternatives to traditional methods, including chromatography (GC, HPLC), mass-spectroscopy (GC-MS, LC-MS) for the detection of pistachio adulteration. Powdered pistachio samples were adulterated with powdered green pea and peanut samples at different concentrations (5–40%). Spectra were collected with a portable FT-MIR and FT-NIR spectrometers and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate a classification model to authenticate pure pistachio, and Partial Least Square Regression (PLSR) to predict the levels of adulterants in pistachio. SIMCA provided, in both units, very distinct clusters for pure and adulterated samples with inter-class distance (ICD) over 3. Both units also showed superior performance in predicting adulterant levels with rVal>0.99, standard error of prediction (SEP) < 2.5%. Based on SIMCA and PLSR models, portable FT-NIR spectrometer provided more advantages over portable FT-MIR unit because the prediction with the FT-NIR spectrometer was more precise than the FT-MIR unit. Both portable FT-MIR and FT-NIR units can be used as alternative methods to traditional methods and showed great potential for real-time surveillance to detect green pea and peanut adulteration in pistachio.

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• MakaleYalnız künye2021/r/gumushane/14673
  Effect of far infrared heating process on surface decontamination and quality attributes of whole yellow and white onions

  Coskun, Eda; Ozturk, Samet; Akpinar, Merve; Halkman, A. Kadir; Erdogdu, Ferruh

  This study aimed to develop an effective decontamination process for whole yellow and white onions with maintained quality attributes. For this purpose, a far infrared radiation (FIR) process was evaluated to determine its effects on microbial safety and quality of the onions. Prior to the FIR process, samples were inoculated with Salmonella culture to about 7–8 log10 CFU/cm2 and stored for 1–48 h at room temperature. Inoculated samples were then processed up to 120 s at 400 °C infrared source temperature. Temperature chance of the onions was recorded by using T-type thermocouples, and surface temperature distribution was obtained with an infrared thermal camera. Quality attributes (color changes, mass losses and textural changes) were also evaluated following the FIR process. The results indicated that 3.3 and 2.4 log10 CFU/cm2 reductions in the Salmonella counts were obtained for the whole yellow and white onions, stored for 1 h at room temperature after 120 s FIR processing. The background microbial population on the outer skin of the onions was under the detection limit (<1 log10 CFU/g). It was observed that the effectiveness of FIR treatment on Salmonella decontamination decreased as the storage time increased from 1 to 48 h at room temperature. FIR process did not significantly affect the quality attributes of the onions, and the results confirmed the potential use of FIR process for surface decontamination of the onions. Further studies were suggested to design an effective FIR system for industrial scale applications.

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