Journal of Food Processing and Preservation最新文献

Ohmic-assisted drying (OAD) is a novel drying system that combines ohmic heating and convection drying simultaneously. The present study is aimed at evaluating the mechanism of OAD system behaviours against the combined impact of operational and model uncertainties. Moreover, the dynamic (time-dependent), as well as static (end-of-drying) spatial homogeneity of the model predictions, was quantitatively described for the first time in the literature using Buzas and Gibson’s evenness value (an α-diversity index). The Monte Carlo simulation approach was used to propagate the uncertainty of randomly selected input variables using the Halton sequence sampling method. Mechanistic models include input uncertainties that lead to deviations in model estimations. Both time-dependent and independent global sensitivity of moisture, sample temperature, sample’s internal pressure, their spatial homogeneity, and drying time were assessed using Lasso regression (a variable selection method that penalises the coefficients using l₁ norm). The stochastic results of the mechanistic investigation showed that the effects of the input variables are almost identical both as static and time-dependent variables. Lasso regression results indicated that operational and model uncertainties cause varying changes in the magnitude and direction of the stochastic model predictions throughout the process. By contrast, the homogeneity properties of the dry product are not caused by these variations and heterogeneous distribution of the electric field. Additionally, electrical conductivity, oven temperature, applied voltage, and initial moisture were found to be the variables which have the most significant effect on all the variables which were examined in terms of operational and model uncertainties. Practical Applications. The present study investigates the stochastic behaviour of the OAD system through the mechanistic model and using a probabilistic modelling approach. To the best of our knowledge, in addition to being the first study to probabilistically evaluate the OAD system, we are introducing Buzas and Gibson’s evenness value for the first time in the food science/technology literature. This measure serves as a numerical indicator of the spatial distribution homogeneity of the physical properties of the sample. The study’s findings and proposed methodologies will have further applications not only for researchers but also for manufacturers, particularly for those involved in the design and analysis of new drying and food systems in general. Moreover, the presented methods and novel homogeneity measures are generic tools; they can be easily adapted to other process improvement practices involving input/output uncertainty/variability.

Background. Foodborne outbreaks and epidemic diseases are common in resource-limited countries. Many people in developing countries are increasing their habit of eating fast foods prepared on the street due to their low cost and accessibility, while most of them get sick due to poor food hygiene practices and safety measures among the street food vendors. Therefore, this research is aimed at assessing food hygiene knowledge, practices, and associated factors among street food vendors. Methods. A community-based cross-sectional study design was conducted among 494 street food vendors in Addis Ababa, Ethiopia. The data were collected using a face-to-face interview with a pretested questionnaire. All precautions for prevention of COVID-19 were followed since the study was conducted during the COVID-19 era. The data were entered into a computer and cleaned using EpiData 3.4 before being exported to SPSS version 23 for analysis. A multivariable logistic regression with a 95% confidence interval was used to identify the factors that were significantly associated with poor hygiene practice. Results. In this study, 78% of street food vendors had poor hygienic practices. Monthly income less than 2000 birr (AOR = 1.95, 95% CI (1.36, 11.26), p = 0.014), age groups less than 25 years of age (AOR = 3.17, 95% CI (1.12, 8.97), p = 0.03), food vendors who prepare food while not healthy (AOR = 2.07, 95% CI (1.25, 5.93), p = 0.025), and lack of toilet facilities (AOR = 2.84, 95% CI (1.58, 9.68), p ≤ 0.001) were significantly associated with the poor hygiene practices of street food vendors at a significance level of p ≤ 0.05. Conclusions. The general food hygiene practices and safety measures were poor. Age groups younger than 25 years of age, monthly income less than 2000 birr, preparing food while sick, and lack of access to toilet facilities were significantly associated with poor food hygiene and safety practices. To promote food safety and community health, street food vendors should receive health education and training about proper food hygiene practices through health extension workers and mass media. Basic infrastructure and services like clean water supply and waste disposal facilities should be provided for street vendors to improve food hygiene and safety practices.

Nowadays, the market is full of lists of products produced by way of vivid processing, blended formulation, and other novel formulation practices. In a similar vein, ohmic heating (OH) has proven to be a successful replacement for traditional procedures. Although the microbial burden is substantially reduced, the aforementioned method also controls the sensory and functional qualities of meals. OH is regarded as a thermal treatment option that is less damaging to food constituents in comparison to traditional thermal methods. The said processing method retains vivid food constituents that are widely acknowledged under the significant nutritional and functional category, hence addressing healthy longevity. Furthermore, OH is the least severe category of thermal operation in competition with the most technologically advanced food processing operations. The current study emphasizes the crucial relevance of knowing the effects of OH on food ingredients, which will serve as a basis for optimizing this novel approach for a wide range of food products. The effects of OH on quality characteristics such as color, flavor, and texture are thoroughly investigated, offering vital insights into OH’s capacity to maintain and enhance the sensory aspects of food items. Furthermore, the study looks at the effect of OH on nutritional contents, emphasizing the need to strike a careful balance between ensuring microbiological safety and retaining the nutritional integrity of treated foods. This literature survey closely examines the impact of OH on microorganisms, enzymes, color, and rheological and textural attributes of food, along with the deterioration of diverse bioactive substances, including phenolics and carotenoids, as well as vitamins. Besides, the paper explains the effect of the inactivation mechanism of microorganisms. The findings serve as a platform for future research and development, as well as a road map for the effective deployment and commercialization of OH technology in a variety of food industries.

The growing popularity of soy proteins among vegans and vegetarians, owing to their high protein content and widespread availability, has led to scientific studies on its various extraction methods mainly on ultrafiltration. This research employed artificial neural network (ANN) and Box-Behnken design (BBD) methodologies to predict the process parameters of ultrafiltration for the preparation of soy protein. Using BBD, the optimum process parameters of ultrafiltration were identified via the desirability function approach. The optimized permeate flux was 11.13 litres per hour (LPH) and 85.52% protein content in retentate. The identified ideal process parameters for ultrafiltration to achieve maximal protein retention encompassed a 10 kDa membrane module, a transmembrane pressure of 117 kPa (17 PSI), a volume concentration ratio of 3.5, diafiltration set at 1, and a flow rate of 65% of the pump capacity, exhibiting an absolute percent error value of 2.81. Employing these refined process parameters, the predicted value for protein retentate stood at 80.49%. The predictive accuracy of the model achieved an impressive 99.61% for protein retention. The ANN model effectively predicted the optimal ultrafiltration conditions, resulting in maximal protein retention and a protein content accuracy of 96.41% and 99.61%, respectively.