29 research outputs found
Composition and rheological properties of flour and dough from genetically modified wheat (Triticum aestivum L.) Hi-Line 111
The main objective of this work was to evaluate the composition, nutritional, physical and rheological properties of wheat flour and dough from genetically modified wheat (Triticum aestivum L.) Hi-Line 111 (GMW) compared to conventional wheat (non-GMW). Analyses were conducted to measure the proximate chemical composition with references to 18 components including total solid, protein, lipids, crude fiber, ash, carbohydrate, minerals, amino acids, and fatty acids. In addition, physical and rheological properties such as water absorption, arrival time, dough development time, stability value, dough weakening value, extensibility of dough, resistance to extension, and ratio of resistance/extensibility were evaluated. The results showed that there were no significant differences between GMW and non-GMW in terms of chemical composition. Results revealed the presence of saturated and unsaturated fatty acids wherein there were no significant differences between GMW and its counterpart in the levels of fatty acids. In addition, there were no significant differences on the levels of amino acids. In addition, there were no significant differences between the GMW and non-GMW in the physical and rheological properties. From these results, it can be concluded that GMW Hi-Line 111 is confirmed to have nearly the composition and rheological properties as non-GMW
Composition and rheological properties of flour and dough from genetically modified wheat ( Triticum aestivum
Evaluation of Two Fitting Methods Applied for Thin-Layer Drying of Cape Gooseberry Fruits
Use of Opuntia ficus-indica (L.) Mill extracts from Brazilian Caatinga as an alternative of natural moisturizer in cosmetic formulations
Effect of heat treatment on rheological properties of mixed nectars based on cashew apple, mango and acerola pulps
Bioactive Phytochemicals from Nigella sativa Oil Processing By-products
Nigella sativa (black cumin) is an annual flowering plant grown in the Middle East, Asia, and Mediterranean regions. Black cumin seeds are well known in history with their therapeutic effects in various cultures and also are used for culinary applications. Oil extraction is possible with different methods, but nowadays, the cold press method is gaining importance with the modern customers’ healthy product preferences and its by-products rich in bioactive phytochemicals. The main compounds of seedcakes are carbohydrates, proteins, minerals, fibers, flavonoids, phenolics, and vitamins. Even though the seedcakes are used only as fodder for animals, recent studies have shown the seedcakes’ potential as a value-added raw material or additives for functional food, supplements, cosmeceuticals, and pharmaceuticals with their antioxidant, antimicrobial, and nutritional effects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG
Towards energy-efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO
Publisher Copyright: © 2023 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand-side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance-Level ElectricityUK-DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.Peer reviewe