Journal of Food Process Engineering最新文献

The sucupira fruit (Pterodon pubescens) contains a valuable oil characterized by high levels of fatty acids and an abundance of bioactive compounds, such as flavonoids and terpenoids. This study aimed to evaluate the use of ultrasound-assisted extraction to obtain sucupira fruit oil through a complete 2² factorial rotational design. The effects of ultrasound intensity (%) and ethanol concentration (%) on lipid content, antioxidant capacity, color, and reducing capacity were assessed. The maximum lipid content was 26.9/100 g, which was significantly higher than that obtained by the Soxhlet method (23.4/100 g). The extract obtained at the optimized conditions (90% ultrasound intensity and 80% ethanol concentration) showed antimicrobial activity against Salmonella and Escherichia coli cocktails. Compounds such as β-caryophyllene, caryophyllene oxide, α-humulene, α-copaene, and spathulenol, responsible for biologically relevant activities, were identified in the extract through GC/GC–MS. HPLC–MS/MS identified compounds such as catechin, luteolin, quercetin, taxifolin, among others.

UV-A light exposure (365 nm, 4.6 ± 0.2 mW/cm²) was combined with low relative humidity (RH) air flow at room temperature to dehydrate sweet potatoes and reduce the population of inoculated bacteria. Control samples underwent dehydration with low RH air at room temperature and in the absence of UV-A light to assess the importance of UV-A light in the dehydration and microbial reduction processes. The UV-A light-dehydrated sweet potatoes resulted in the removal of approximately 97.2% ± 2% of the original mass of water, which was significantly higher than the control samples. Infrared spectroscopy analyses confirmed the preservation of the physical and chemical integrity of the UV-A light-dehydrated samples. Despite the absence of pretreatments for enzyme inactivation, the UV-A light-dehydrated sweet potato did not exhibit a decrease in luminosity or darkening of color often associated with dehydration. Additionally, the utilization of UV-A light for the dehydration of sweet potatoes inoculated with ~6 log(CFU/g_{Dry solids}) of Escherichia coli K12 resulted in a 99.9% ± 0.1% or 3.1 ± 0.5 log(CFU/g_{Dry solids}) reduction with only a 92.2% ± 0.1% or 1.3 ± 0.5 log(CFU/g_{Dry solids}) reduction resulting from the control samples dehydrated without UV-A exposure. In the case of samples inoculated with ~6 log(CFU/g_{Dry solids}) of Listeria innocua L2 there was a 99.2% ± 0.5% or 2.2 ± 0.3 log(CFU/g_{Dry solids}) reduction when UV-A light was utilized and only a 60.9% ± 10.3% or 0.4 ± 0.1 log(CFU/g_{Dry solids}) reduction when samples were dehydrated in its absence.

This study aims to investigate the effect of ultrasound conditions on the germination kinetics and drying characteristics of a germinated Bengal gram at different drying temperatures. Ultrasound treatment was given to the Bengal gram seeds at two different conditions, that is, before (US) and after soaking (SU), which was then followed by germination. This study also determines mass transfer parameters at drying temperatures of 45°C, 55°C, and 65°C and assesses the influence on the physicofunctional characteristics of a germinated Bengal gram. The germination rate behavior was effectively predicted using a zero-order kinetic model with the highest R² value of 0.7974–0.8857 in each nontreated (S) and treated (SU, US) Bengal gram seed, respectively. This study showed that the ultrasound treatment effectively enhanced the germination rate in both conditions, and the highest germination rate was found in pretreated ultrasound Bengal gram samples. The logarithmic thin layer drying model, with the highest average R² of 0.9954 and the lowest average RMSE value of 0.0160, is the best-fitted model to predict the changes in moisture ratio in both treated and nontreated conditions. The moisture diffusivity values at drying temperatures ranging from 45°C to 65°C were found in treated (US, SU) and nontreated (S) germinated samples ranging from 3.34 × 10⁻⁸ to 4.03 × 10⁻⁸ m²/s, 1.30 × 10⁻⁸ to 2.01 × 10⁻⁸ m²/s, and 6.6 × 10⁻⁹ to 8.06 × 10⁻⁹ m²/s, respectively. The protein content increased in the ultrasound-treated sample between 12.37% and 13.50%. The solubility ranged from 8% to 10.36% throughout the treated and nontreated germinated Bengal gram flour. This study provides a unified approach to utilizing ultrasound-treated germinated Bengal gram seeds as an alternative option to develop a functional product with better nutritional and functional properties.

Microfiltration is one of the most suitable processes for protein recovery from whey due to its low energy consumption and lack of use of heat and chemicals. However, membrane fouling is one of the limiting factors in the microfiltration process, preventing its commercial use. In this study, an artificial neural network (ANN) based model was employed to study the effects of different operating parameters on membrane fouling in whey concentration. Trans-membrane pressure, Reynolds number, and feed temperature were selected as the input parameters. Experimental data from the available studies were used to train the ANN. The ANN with 23 neurons gave a minimum mean squared error (MSE) for trans-membrane pressure and Reynolds number. The ANN with seven neurons gave the minimum MSE for feed temperature. The predicted values from both ANNs well fitted with the experimental results with R² < 0.99. Simulations showed that membrane fouling increased as flux reduction increased from 36.3% to 76.39% when trans-membrane pressure increased from 0.5 to 2 bar. In contrast, a 19.96% reduction in flux was observed by increasing the Reynolds number from 750 to 2500. An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. An ANN-based approach was the most accurate method to model membrane fouling for whey protein separation.

This study has two main objectives: (i) to determine the physicochemical and rheological properties of different flours and (ii) to estimate the alveograph parameters obtained as a result of experimental studies. In this context, physicochemical (protein, ash, falling number, wet gluten, gluten index, Zeleny, and delayed sedimentation) and alveograph parameters (P, L, G, W, P/L, and IE) of 150 different bread and pastry flours were determined. Multiple regression analysis (MRA) and artificial neural network (ANN) methods were then used to predict alveograph results from this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe (NSEC), and relative error (RE) performance statistics were used to evaluate the CS prediction capabilities of the methods. It was found that the flours were in the range of 11.01%–13.82% protein, 325–403 s falling number, and 30–61 mL Zeleny and delayed sedimentation values. The ANN method showed better predictive performance than the regression-based method. W was the best estimated parameter in the ANN model. This was followed by G, L, Ie, P/L, and P values. Considering the RMSE value of the W parameter, it was observed that the ANN method provided an improvement of 5.16, 1.76, and 2.15 times compared to the regression method for the training, validation, and test sets, respectively.