Journal of Food Process Engineering最新文献

Pepper seed quality is determined using the mechanical and physical properties through artificial neural networks (ANNs) to enable accurate and timely agricultural planning. The objective of this study is to develop a model that provides simple, precise, rapid, and cost-effective predictions based on thousand-grain weight, porosity, and various classifications for pepper seeds. To achieve this, three different models—artificial neural networks (ANN), radial basis function (RBF), and multiple linear regression analysis (MLR) were employed to estimate thousand-grain weight and porosity. The best-selected model was then used to classify 12 different pepper seed varieties. This applied model's performance was evaluated using the determination coefficient (R²), the root mean square error (RMSE), the mean relative percentage absolute error (MRPE), and the mean square error (MSE). A comparison of the ANN model results indicated that the input parameters—width, length, thickness, and bulk density—provided the optimal prediction model concerning R², RMSE, MRPE, and MSE. For the testing dataset, the ANN model achieved values ranging from 0.88 to 0.92 for R², 0.276 to 0.016 for RMSE, 1.647 to 0.232 for MRPE, and 0.138–0.008 for MSE using 5-8-1 and 8-10-1 network structures, respectively. These selected models can be used as a neurocomputing-based approach to predict the mechanical and physical properties of pepper seeds, assisting in variety classification and genotype prediction.

This study first reports the microbial growth dynamics and shelf life of pasteurized milk at extreme temperature abuse (37°C and 45°C), that occurs frequently in tropical countries. We determined total plate count (TPC) in pasteurized milk: double-toned milk (DTM), toned milk (TM), and full-cream milk (FCM) with a fat content of 1.5%, 3%, and 6%, respectively, at 4°C, 10°C, 20°C, 37°C, and 45°C (±1°C). Milk was considered to be expired when TPC exceeded 4.47 log CFU/mL (Food Safety and Standards Authority of India). Our study revealed a biphasic microbial growth pattern (two sequential sigmoid curves) at 4°C and was explained by the biphasic Baranyi model. At higher storage temperatures (10°C–45°C), monophasic microbial growth was observed and was described by the Baranyi and Roberts model. The temperature dependency of the growth rate was described by the Huang square root model. Our analysis indicated that the milk's shelf life decreased exponentially with increasing storage temperatures. At 4°C, we found a maximum shelf life of 168, 350, and 120 h for DTM, TM, and FCM, respectively. The biochemical properties of milk, including pH, proteolysis, lipolysis, and titratable acidity, significantly (p < 0.05) correlated with the microbial load in pasteurized milk at different storage temperatures studied. The results and the developed models can be used for efficient cold chain management and estimating the shelf life of pasteurized milk subjected to temperature abuse.

In the context of pneumatic transport for bulk grains, pneumatic conveying is a critical technique that safeguards the integrity of the transported grains. The utilization of non-spherical bulk grain particles as the conveyed materials within the transport pipeline emphasizes the importance of analyzing their dynamic behavior within the conduit. A geometric reconstruction technique is applied to represent ellipsoidal bulk grain particles, and leveraging gas–solid coupling theory, a motion model for individual particles in the flow field is developed. By incorporating the Hertz contact model with the equations of motion, a contact dynamics framework for binary non-spherical particles is established, which is subsequently subjected to numerical simulations. A comparative analysis of the physical property parameters of four varieties of bulk grain particles—wheat, rice, soybeans, and corn—illustrates the interrelations among their minimum approach distances, normal and tangential forces, contact forces, total capacities, and the Stokes number. During the pneumatic conveying process, wheat exhibits relatively stable characteristics, thereby minimizing the risk of particle fragmentation, while soybeans and corn demonstrate a higher susceptibility to breakage.

The vacuum steam thawing (VST) is to thaw frozen food by condensation heat exchange in a vacuum environment. Recently, the VST has gained attention due to the excellent thawing effect, such as a rapid thawing rate, high water holding capacity, and great antimicrobial capacity. This paper reviews the mechanism and application of VST, ultrasound thawing (UT), microwave thawing (MT), and electric field thawing (EFT), and analyzes the competitiveness of VST in these four novel thawing methods. The localized overheating phenomenon is the common disadvantage of UT, MT, and EFT. Based on the condensation heat exchange mechanism and the low-temperature and low-oxygen vacuum environment, even thawing can be achieved by VST. In addition, compared to UT, MT, and EFT, the quality of frozen food thawed by VST is much better. Moreover, two kinds of optimization of VST are summarized, which are thawing combined technology and vacuum sublimation-rehydration thawing (VSRT). The thawing combined technology aims at the complementarity role among VST and other thawing technologies. The VSRT aims at improving the effect of condensation heat exchange. These two techniques improve the thawing effect of VST and effectively reduce the cost requirements, which can be the research direction of VST in the future. Further refinement of these two techniques will have a positive impact on the thawing process in the food industry.

Antarctic krills are rapidly frozen onboard into blocks, and therefore, developing technologies that can enable rapid and uniform semi-thawing and tempering to prevent enzymatic reactions is essential to enhance their quality and commercial value. We evaluate the application of ohmic tempering (OT) in this study. Experimental blocks (4 × 4 × 4 cm³) are processed under OT at 20 kHz and several constant and variable voltages from −30 C to −5°C. Electrical conductivities of the Antarctic krill block and the block's solution are determined in temperature and frequency ranges of −30 C–0°C and 50 Hz to 200 kHz. Better temperature uniformity and reduced OT time are obtained by fine-tuning the voltage in four steps from 400 to 50 V based on the electrical conductivity (EC) changes to avoid overheating. Compared to conventional tempering methods (air, running water, and low temperature (inside a refrigerator)), OT results in a faster and optimal method. A three-dimensional heat transfer model for OT is established using COMSOL Multiphysics to explore the OT uniformity of the Antarctic krill block. The simulated temperature profiles are successfully validated using the measured values, confirming good temperature uniformity. Moreover, the simulation revealed hot and cold spots at different positions. At the endpoint of tempering (545 s), the temperature difference between the two spots was 1.364°C using the fine-tuning method. These results are relevant to the design of novel OT systems.

Ohmic heating is a thermal method of preserving food that uses electric current to produce high-quality food products with minimum sensory, nutritional, and structural changes. In this study, we developed an affordable and portable ohmic heater by statistically modeling its volumetric heating profile. We evaluated the performance by blanching Moringa oleifera (Moringa) leaves, a beneficial tree with significant nutritional and medicinal properties in its various parts. The fabricated heater functioned with a 60 Hz frequency and an AC electric source for the voltage supply. Five digital thermometers recorded the uniform temperature at different positions of the ohmic chamber every 30 s for 180 s, and the temperature profile was modeled using two statistical models, namely, the response surface method in Design Expert 7.0 and an artificial neural network in MATLAB 15.0 software. Uniform volumetric heating was confirmed empirically at the combination of four voltage gradients (10, 15, 20, and 25 V/cm) with three sodium salt concentrations (0.5%, 1.0%, and 1.5%) as food probes. The predicted heating parameters from statistical models were then employed to blanch Moringa leaves for extended storage. The nutritional properties of ohmic-blanched Moringa leaves were compared with the fresh and steam-blanched samples. The ohmic-blanched leaves were found to retain nutrients substantially higher compared to the steam-blanched leaves. The carbohydrate, protein, ascorbic acid, carotenoid, phenol, flavonoid, and iron content in ohmic-blanched leaves were found to be 14.6 ± 1.67 g/100 g, 6.3 ± 0.2 g/100 g, 135.07 ± 1.35 mg/100 g, 95.43 ± 1.4 mg/g, 37 ± 1 mg GAE/g, 28.03 ± 1.64 mg QE/g, and 15.1 ± 0.26 mg/g, respectively. Further, the carbohydrate, flavonoid, and iron content were higher in the ohmic-blanched sample than in the fresh leaves. Overall, our findings indicate that small-scale industries and homemakers could use the ohmic heater we fabricated to effectively preserve Moringa leaves, particularly food materials, retaining their nutritional values.

Grape juice, a non-alcoholic and non-fermented beverage, is known for its health benefits primarily due to its diverse phenolic compounds. Effective preservation methods are crucial for preventing spoilage by microorganisms and enzymes, ensuring safety, and extending shelf life. A nonthermal technology called Moderate electric field (MEF) treatment presents a promising approach for preserving liquid food quality and safety with slight effects on its functional and sensory attributes. This study investigated the MEF effect on the physicochemical attributes and microbial reduction of grape juice. The MEF reactor, operating at a frequency of 50 Hz, treated raw grape juice at different voltages (125, 150, and 175 V) and times (5, 10, and 15 min) combinations. The results indicated that MEF treatments significantly influenced temperature, pH, total soluble solids (TSS), turbidity, conductivity, color, and microbial reduction in grape juice. Higher voltages and longer treatment times generally resulted in higher temperatures and more pronounced changes in the juice's properties. Specifically, treatment T₁₉ (175 V for 15 min) showed the most significant effects, including increased TSS, turbidity, conductivity, and total color difference, while substantially reducing the microbial count. A maximum log reduction of 3.43 was achieved at 175 V for 15 min. These findings suggest that MEF treatment can effectively enhance grape juice quality and safety while preserving its nutritional and sensory attributes, particularly under specific conditions.

Ohmic heating (OH) is a promising technology in the food industry, with electrical conductivity (EC) playing a crucial role in its effectiveness. This study aimed to correlate the evolution of EC during pound cake baking using OH with the degree of starch gelatinization (DSG) and assess the impact of two sodium acid pyrophosphate leavening acids—SAPP10 and SAPP40—on EC. Baking was conducted in a prototype OH cell, and EC was measured in two ways: during baking as a function of increasing temperature, and at specific baking times and temperatures at the cake center. The latter was measured using impedancemetry at room temperature and correlated with the DSG determined by differential scanning calorimetry. Key results showed a clear negative correlation between EC and the DSG in the center of the OH-baked cake. Specifically, as the DSG increased from 4% at 60°C to about 48% at 76°C, EC decreased from 390 μS/cm to approximately 89 μS/cm, respectively. Importantly, the addition of different baking powders did not significantly affect EC evolution. These findings suggest that the relationship between EC and starch gelatinization in cakes is influenced by moisture content and ion mobility during baking. This study highlights the potential of EC monitoring as a tool to track internal changes in cakes during the OH process, offering valuable insights into how baking conditions can influence final product characteristics like texture and quality.

The aim of this research is the comprehensive comparison of advanced modeling tools for predicting the quality parameters of IoT-enabled IR-assisted RW-dried papaya leather. The models used in the current research were “response surface methodology (RSM),” “artificial neural network (ANN),” “machine learning regression algorithms (MLRA),” and “adaptive neuro-fuzzy inference system (ANFIS).” This comprehensive compression could be differentiated based on the model's fitness and prediction capabilities. The fitness of the model was assessed using statistical metrics such as “root mean square error (RMSE),” “mean square error (MAE),” and “coefficient of determination (R²).” While the prediction capability was evaluated by using tactics like predicting error, predicting accuracy, chi-square, and associated p-values. The results indicated that both the RSM and MLRA models had substantial predictive capabilities and achieved a prediction accuracy of 98.992 and 97.169, respectively. This study concluded that the model's fitness was getting excellent in the Alpha ANN (R² = 0.9943) and ANFIS (R² = 0.9903) models. However, when evaluating the prediction capabilities, the RSM and MLRA models outperformed the others.