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This study evaluated the refinement of sorghum, corn, and cowpea grains using the processing steps and equipment originally designed for wheat milling that consists of a conventional gradual reduction system. The need to mill these grains resulted from a desire to produce alternative ingredients for developing new fortified blended extruded foods used for food aid programming. Milling of white sorghum grain resulted in a crude protein content of 7.4% (wb) for both whole and coarse-milled flour. The crude protein content in whole fine-milled sorghum was 6.8% (wb), which was significantly lower than that of whole coarse flour at 9.3% (wb). A decrease in the ash content of sorghum flour correlates with the decortication process. However, degermed corn, fine and coarse, had significantly different crude protein content of 6.0 ± 0.2% (wb) and 7.7 ± 0.06% (wb), respectively. Degerming of corn improved the quality of corn flour (fine and coarse) by reducing the crude fat content from 3.3 ± 0.18% (wb) to 1.2 ± 0.02% (wb) and 0.6 ± 0.13% (wb), respectively. This helped increase the starch content from 60.1 ± 0.28% (wb) in raw corn to 74.7 ± 0.93% (wb) and 71.8 ± 0.00% (wb) in degermed fine and coarse corn flour, respectively. Cowpea milling did not produce differences in the milling stream outputs when the crude fat and crude protein were compared. Whole flour from the grains had higher milling yields than decorticated flour. This study demonstrated that a mill dedicated to wheat size reduction can be adapted to refine other grains to high quality. [ABSTRACT FROM AUTHOR]

This study developed a mathematical model following the fundamental principles of mass transfer for the simulation of the oil and moisture content change during the Deep-Fat Frying of plantain (ipekere) chip. The explicit Finite Difference Technique (FDT) was used to conduct a numerical solution to the consequential governing equation (partial differential equation) that was used to describe the mass transfer rate during the process. Computer codes that were computed in MATLAB were used for the implementation of FDT at diverse frying conditions. Samples of the plantain were cut into portions of 2 mm thickness, and these sliced portions were fried at separate frying oil temperatures (170, 180 and 190°C) between 0.5 and 4 minutes. The experimental data and the predicted outcomes were compared for the validation of the model, and the juxtaposition revealed a plausible agreement. The predicted values and the experimental values of oil and moisture transfer models produced correlation coefficients that range from 0.96 to 0.99 and 0.94 to 0.99, respectively. The predicted outcomes could be utilized for the control and design of the DFF.

Reaction to food allergens is on the increase and so is the attending cost on consumers, the food industry, and society at large. According to FDA, the "big-eight" allergens found in foods include wheat (gluten), peanuts, egg, shellfish, milk, tree nuts, fish, and soybeans. Sesame was added to the list in 2023, making the target allergen list nine instead of eight. These allergenic foods are major ingredients in many food products that can cause severe reactions in those allergic to them if found at a dose that can elicit a reaction. Defining the level of contamination that can elicit sensitivity is a work in progress. The first step in preventing an allergic reaction is reliable detection, then an effective quantification method. These are critical steps in keeping contaminated foods out of the supply chain of foods with allergen-free labels. The conventional methods of chemical assay, DNA-PCR, and enzyme protocols like enzyme-linked immunosorbent assay are effective in allergen detection but slow in providing a response. Most of these methods are incapable of quantifying the level of allergen contamination. There are emerging non-destructive methods that combine the power of sensors and machine learning to provide reliable detection and quantification. This review paper highlights some of the critical information on the types of prevalent food allergens, the mechanism of an allergic reaction in humans, the measure of allergenic sensitivity and eliciting doses, and the conventional and emerging AI-based methods of detection and quantification—the merits and downsides of each type. [ABSTRACT FROM AUTHOR]