Optimization of process variables on physicochemical properties of milk during an innovative refractance window concentration

Refractance window drying of food and biological materials: Status on mechanisms, diffusion modelling and hybrid drying approach

Refractance window (RW) dryer has an immense advantage in terms of final product quality (textural and color attributes, nutrient retention), energy consumption, and drying time over other conventional dryers. RW is a thin film drying system and a technologically evolving drying process. RW drying is an energy-efficient (re-circulation of water) short drying process as the drying of food materials occurs due to a combined mode of heat transfer conduction, radiation, and convection (hot air circulates over film). The high-quality dried product is obtained because the product temperature remains below 80 °C. RW dryer application is not only limited to drying food products, but it can also be further used for improving the gelling and emulsion properties, formation of leather and edible film, and can be used for handling high protein products, drying leafy vegetables or marine foods as this process does not change any functional properties. Due to these advantages over other drying techniques, RW drying has gained academic and industrial interest in recent years. The industrial application of this technology at large scale is becoming difficult due because of large surface area requirement for mass production. Researchers are trying to scale-up by combing this technology with others technology (Infrared, ultrasound, solar energy, and osmotic dehydration). RW dryer is now extending from the food sector to other sectors like pharmaceutical, cosmetic, pigment, edible film formation, and encapsulation. Majority of the reviews on RW drying focuses on the product quality aspects. This review paper aims to comprehend the RW drying system more mechanistically to understand better the principles, diffusion models explaining the transfer processes, and emerging novel hybrid drying approaches.

Assessing Compositional and Quality Parameters of Unconcentrated and Refractive Window Concentrated Milk Based on Color Components

In this study, a multiple linear regression equation was developed to measure and predict quality parameters of unconcentrated and concentrated milk based on color components. The viscosity, density, pH, moisture, and fat content could be measured using image processing technology. The multiple linear regression model had a good fitting on experimental data considering the limited errors (0.00–1.12%), standard deviation (0.000–0.043), and root mean square error (0.0007–0.3721). Therefore, these models can be used to predict the quality parameters of milk, including fat percentage, pH, viscosity, density, and moisture content, based on color components of unconcentrated and concentrated milk. The maximum and minimum of color change were 12.28 and 5.96, respectively. The values of browning index were also well-predicted and were within the standard limits. The non-destructive and quick procedure that proposed in this study showed a percentage of accuracy in assessing and predicting the quality parameters milk based on color components. Overall, the color correlates with different compositional and physical characteristics, and provide a possible internet of things (IoT)-based approach to accompany the conventional approaches in the future after further evaluation at large scale for various types of milks subjected to various processes.

Cold plasma for mitigating agrochemical and pesticide residue in food and water: Similarities with ozone and ultraviolet technologies

Pesticide and agrochemical residues in food and water are among hazardous chemicals that are associated with adverse health effects. Consequently, technologies for pesticide abatement in food and water remain in focus. Cold plasma is an emerging decontamination technology, that is being increasingly explored for the abatement of agrochemical and pesticide residue in food and water. In some cases, rapid and complete degradation of pesticide residues has come to light. Such promising results encourage exploring scale-up and commercialization. To achieve this, unraveling mechanisms involved in plasma decontamination and the nature of degradation products is needed. The present review identifies the mechanisms involved in plasma- assisted removal of pesticide residues from food and water, draws parallels with mechanism of ozone and ultraviolet technologies, investigates the chemistry of the intermediates and degradates, and identifies some future research needs. The review recognizes that mechanisms involved in plasma processes have overlapping similarities to those identified for ozone and ultraviolet light, involving oxidation by hydroxyl radical and photo-oxidation. The toxicity of intermediates and degradates in plasma processing have not received much attention. The safety aspects of end products form plasma led degradation of pesticides should be considered for practical exploitation. Identification of intermediates and degradation products, recognition of most potent plasma species, understanding the influence of co-existing entities, the energy efficiency of plasma reactors, and the process economics deserve research focus.

Development and Optimization of Djulis Sourdough Bread Using Taguchi Grey Relational Analysis

Bakery products made from naturally fermented sourdough show a diversified flavor and nutritional profile. Djulis (Chenopodium formosanum), known as red quinoa or Taiwan djulis, originally cultivated by Taiwanese indigenous people in mountain areas in eastern and southern Taiwan, has a high nutritional value and characteristic properties. In the present study, a new bakery product (djulis sourdough bread) was developed and a combination of the Taguchi method coupled with grey theory was utilized to optimize the baking parameters (product formulation). Five main factors, i.e., djulis sourdough (A), hulled djulis (B), oil type (C), a mixture of bread flour (wet gluten content of 29.0%) and a high-gluten flour (wet gluten content of 35.5%) (D), and honey (E), (each at four levels) were chosen for the Taguchi experiment design (L₁₆(4)⁵). Dependent parameters were the data from texture profile analysis (brittleness, springiness, cohesiveness, gumminess, and chewiness), color analysis (L*, a*, and b*), and sensory evaluation (appearance, aroma, bitterness, sourness, chewiness, and overall acceptance) of the final product. Taguchi grey relational analysis successfully determined the optimal conditions based on combined parameters (5 factors), which highlighted the advantages of this innovative optimization technique. The result shows that the optimal formula for producing a djulis sourdough bread with the best texture, color, and sensory qualities was A3B1C1D2E2, i.e., 20% djulis sourdough, 0% addition of hulled djulis, 8% unsalted butter, 80% wheat flour + 20% high-gluten flour, and 10% honey, respectively. Such a novel application could be a reference for improving the quality of bakery products in the industry. Moreover, it seems that the new bakery product developed in this study has good potential to be commercially produced after further nutritional and economic analysis.