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The migration of chemical compounds from packaging polymers to food presents a multifaceted challenge with implications for food safety and public health. This review explores the interaction between packaging materials and food products, focusing on permeation, migration, and sorption processes. The different migration mechanisms of contact migration, gas phase migration, penetration migration, set-off migration, and condensation/distillation migration have been discussed comprehensively. The major migrating compounds are plasticizers, nanoparticles, antioxidants, light stabilizers, thermal stabilizers, monomers, oligomers, printing inks, and adhesives, posing potential health risks due to their association with endocrine disruption and carcinogenic effects. Advanced analytical methods help in the monitoring of migrated compounds, facilitating compliance with regulatory standards. Regulatory agencies enforce guidelines to limit migration, prompting the development of barrier coatings and safer packaging alternatives. Furthermore, there is a need to decipher the migration mechanism for mitigating it along with advancements in analytical techniques for monitoring the migration of compounds. [ABSTRACT FROM AUTHOR]

The rheological properties (viscosity) of marjoram fortified rice dough (MFRD) inside extruder are helpful for product development, process control, and product quality. This study aims to predict viscosity of MFRD through regression analysis of existing empirical models and machine learning (ML) techniques. At first, MFRD was subjected to steady shear analysis (SSA) at various temperatures (60–100°C) and shear rates (1–50 s−1). The SSA data was split into training and validation sets in 70:30 ratio. Rheological modeling was conducted using various empirical models. Hyperparameter tuning was performed using existing MATLAB functions to develop ML models. The SSA revealed that MFRD exhibited pseudoplastic behavior, with viscosity decreasing with increasing temperature up to 70°C and then increasing again, most probably due to starch gelatinization. The regression analysis indicated satisfactory results for Power Law, Carreau, Cross, Sisko, Carreau‐Yasuda model (R2: 0.9849–0.9992) was deemed the best based on Akaike information criterion. A dual variable model (DVM) was then developed using this model, and its coefficient was calculated more efficiently through prepareSurfaceData (R2: 0.8865) MATLAB function than lsqcurvefit (R2: 0.8280) function. In ML model development, Levenberg–Marquardt was the most effective for artificial neural networks (ANNs), grid search for support vector, and Bayesian optimization for decision tree, ensemble, and gaussian process regression (GPR). Only ANN and GPR models had R2(testing) of 1 during training, but GPR model (Nash‐Sutcliffe efficiency: 0.9995) outperformed others during validation. Therefore, GPR model can be used to accurately predict MFRD viscosity and DVM for simulation study. Practical applications: Shear profile and temperature within the extruder change frequently, affecting the viscosity of MFRD and the extrudate quality. The present study develops a dual variable viscosity prediction model using regression, which can be utilized in simulating extrusion process of MFRD. Using ML approach, the GPR model was found to be better than other models in accurately predicting MFRD viscosity at a range of shear and temperature conditions between 60–100°C and 3–50 s−1, respectively. [ABSTRACT FROM AUTHOR]

Food-to-food fortification is an emerging technique to enrich the micronutrients in foods. Pertaining to this technique, noodles could also be fortified with natural fortificants. In this study, marjoram leaf powder (MLP) at a level of 2–10% was used as a natural fortificant to produce fortified rice noodles (FRNs) through an extrusion process. The MLP addition caused a significant increase in the iron, calcium, protein, and fiber in the FRNs. The noodles had a lower whiteness index than unfortified noodles but had a similar water absorption index. The water solubility index increased significantly due to the higher water retention ability of MLP. A rheological study showed a minimal effect of fortification on the gelling strength of the FRNs at lower levels. The microstructural studies found incremental cracks, which facilitated a lower cooking time and hardness but had an insignificant effect on the cooked noodle texture. Fortification improved the total phenolic content, antioxidant capacity, and total flavonoid content. However, no significant changes in bonds were observed, but a reduction in the noodles' crystallinity could be seen. The sensory analysis of the noodles reflected a higher acceptability of the 2–4% MLP fortified samples compared to the others. Overall, the MLP addition improved the nutritional content, antioxidant activity, and the cooking time but slightly affected the rheological, textural, and color properties of the noodles. [ABSTRACT FROM AUTHOR]

Mycelium is the vegetative part of the fungus that grows and forms fruiting bodies, called mushrooms. The Pleurotus eryngii variety mycelium is highly nutritious but perishable due to its high moisture content, making its drying necessary for further application. Pressing as a novel pre‐treatment was applied to fresh mycelium from 3 to 5 kN force for 20–50 s, and its effect on drying time was observed. The pressed mycelium was further vacuum dried at different temperatures (40–60°C) and pressure (60–260 mmHg) conditions. The drying data was fitted with 10 thin layer drying models, and Akaike information criteria was applied for selecting the best model. The physicochemical properties like browning index (BI), water activity (aw), water absorption index (WAI), water solubility index, and flowability were calculated. The study found that pressing at 5 kN for 30 s reduced the moisture content from 89% to 52% wb and drying time by 48%–50%. The drying curves showed lower drying time at higher vacuum and temperature conditions. The drying modeling suggested the best fit for Page, Wang and Singh, Verma, and Midilli models (R2 >.9867, RMSE <.0514, SSE <.0112). The Akaike information criterion showed that the Midilli model is in good agreement with drying data (Akaike increment = 0) at seven conditions. Mycelium dried at 60°C and 60 mmHg showed less aw (0.215 ± 0.004) and BI (28.946 ± 0.066), and maximum WAI (5.365 ± 0.046 g/g). Flowability of the dried mycelium is not affected (p <.05) by vacuum drying conditions. Practical Applications: Pressing as a non‐thermal pre‐treatment for drying can be used to reduce the drying time thereby saving the energy. As a natural fortificant, vacuum‐dried mycelium has potential in improving the nutritional status of the food products. Furthermore, the drying modeling will aid in the creation of optimum drying conditions for mycelium and the design of a commercial‐scale continuous drying system. [ABSTRACT FROM AUTHOR]

The protein content of a plant-based ingredient is generally lower than its animal food counterpart, and research into novel alternative protein is required that can provide similar protein content, texture and appearance as meat. This work investigates a mycelium-based low moisture meat analogue (LMMA) approach, by incorporating 0 to 40% w/w mycelium (MY) into pea protein isolate (PPI) via extrusion using a twin-screw extruder at 140 °C die temperature, 40 rpm screw speed, and 10 rpm feeder speed (0.53–0.54 kg/h). Physicochemical, rehydration, and structural properties of LMMA were assessed. The MY incorporation led to a significant change in color attributes due to Maillard reaction during extrusion. Water solubility index and water absorption capacity increased significantly with MY addition, owing to its porous structure. Oil absorption capacity increased due to increased hydrophobic interactions post-extrusion. Protein solubility decreased initially (upto 20% w/w MY), and increased afterwards, while the water holding capacity (WHC) and volumetric expansion ratio (VER) of LMMA enhanced with MY addition upto 30% w/w. Conversely, WHC and VER decreased for 40% w/w which was verified with the microstructure and FTIR analysis. Overall, MY (30% w/w) in PPI produced a fibrous and porous LMMA, showing future potential with an increasingly plant-based product market and decreasing carbon footprint of food production activities. [ABSTRACT FROM AUTHOR]