Gluten-free pasta was developed by substituting rice flour (RF) with lupin flour (LF). The factors tested were the substitution of RF by LF (10g/100g-30g/100g), egg (8g/100g-30g/100g) and guar gum (0.15g/100g-1g/100g) using a mixture-process design. Seven response variables were measured. Luminosity, ash content, protein content, chrome, hue, weight gain and loss of solids were explained by mixture-process models (p<0.05). LF was a significant factor due to its high protein and mineral content. A sensory analysis was performed to quantify consumer acceptance. The best formulation was obtained with 20g/100g LF, 30g/100g egg and 0.15g/100g guar gum. A proximate analysis was performed in order to compare it with a control sample (100% rice flour, 30% whole egg, 0.15% guar gum). This formulation showed an increase in ash (37.5 g/100g) and protein (63.15 g/100g), fat (112.12 g/100g) and fiber (126.66 g/100g). This study showed that partial substitution of RF by LF could be a reliable alternative for gluten-free products.

Changes in nutritional and quality characteristics of bread with lupin flour (LF; 0%–20% w/w), yeast extract (YE; 0%–2% w/w), and guar gum (GG; 0%–0.2% w/w) were studied. The rheological behavior of doughs and the physical and chemical properties of bread were analyzed. The addition of lupin flour increased protein content, water absorption, and pH of the dough. However, high levels of lupin flour caused a reduction in specific volume; with a 20% substitution (w/w), a reduction of 8.33% was observed. Also, the addition of 0.15% guar gum (w/w) increased the specific volume. Acceptability testing identified that high levels of YE reduced satisfaction in appearance, flavor, and overall liking due to the browning of the bread and residual taste. Based on these results, it is possible to produce loaf bread with high protein content, acceptable sensory parameters, and desirable physical and chemical characteristics. Practical applications Our research on the use of Lupinus mutabilis sweet on bread formulations has multiple practical applications. The higher protein content of lupin flour compared to wheat flour opens the possibility of creating bread recipes with higher nutritional value while maintaining other desirable characteristics. Our research also highlights the impact of flour high in dietary fiber such as lupin in the rheological properties of bread through the use of Mixolab. Future researchers and product developers can use our models as guidelines to better estimate the impact of lupin flour in their bread recipes. Finally, our study also provides methods that can be adapted to study the rheological properties of lupin in other food products such as pasta, cookies, biscuits, and other baked goods.

No abstract

The COVID-19 pandemic has created a worldwide healthcare crisis. Convolutional Neural Networks (CNNs) have recently been used with encouraging results to help detect COVID-19 from chest X-ray images. However, to generalize well to unseen data, CNNs require large labeled datasets. Due to the lack of publicly available COVID-19 datasets, most CNNs apply various data augmentation techniques during training. However, there has not been a thorough statistical analysis of how data augmentation operations affect classification performance for COVID-19 detection. In this study, a fractional factorial experimental design is used to examine the impact of basic augmentation methods on COVID-19 detection. The latter enables identifying which particular data augmentation techniques and interactions have a statistically significant impact on the classification performance, whether positively or negatively. Using the CoroNet architecture and two publicly available COVID-19 datasets, the most common basic augmentation methods in the literature are evaluated. The results of the experiments demonstrate that the methods of zoom, range, and height shift positively impact the model's accuracy in dataset 1. The performance of dataset 2 is unaffected by any of the data augmentation operations. Additionally, a new state-of-the-art performance is achieved on both datasets by training CoroNet with the ideal data augmentation values found using the experimental design. Specifically, in dataset 1, 97% accuracy, 93% precision, and 97.7% recall were attained, while in dataset 2, 97% accuracy, 97% precision, and 97.6% recall were achieved. These results indicate that analyzing the effects of data augmentations on a particular task and dataset is essential for the best performance. Doi: 10.28991/ESJ-2023-SPER-01 Full Text: PDF

Se estudió la sustitución parcial de harina de trigo por harina de lupino (Lupinus mutabilis Sweet) en la producción de pasta larga utilizando metodología de superficie de respuesta. Se utilizó un proceso secuencial de dos diseños experimentales. El primero fue un factorial 2 2 con cuatro puntos centrales. Las variables independientes fueron: cantidad de harina de lupino y huevo. Posteriormente se realizó un diseño central compuesto (DCC). Los resultados en el primer diseño indicaron modelos significativos para la humedad e índice de solubilidad en agua (ISA). El DCC mostró que la humedad, pérdida de sólidos e índice de absorción de agua (IAA) presentaron modelos significativos. Al aumentar la harina de lupino y huevo en la formulación incrementó el valor de IAA y de pérdida de sólidos. Finalmente, con la función de deseabilidad se determinó la mejor formulación para la obtención de una pasta más nutritiva y de buena calidad con 25% de harina de lupino y 18% de huevo.

No abstract

Ecuador has one of the highest soursop productions worldwide; however, as this fruit represents a promising market to the country, its organic waste is becoming a major problem. The aim of the study was to use the Mixolab to predict the quality of wheat flour partially substituted by soursop residues flour for bread making. The experiment was performed through a process mixture design; 21 premixes were made, and 10 significant variables were chosen. Using a desirability function, three criteria were optimized: 1) to maximize the use of soursop residues flour (SRF) (20% SRF substitution), 2) to form a loaf with strong gluten network characteristics (5% SRF substitution), and 3) to form a loaf with weak gluten network characteristics (13.2% SRF substitution). Results showed with a 95% confidence level that the new formulation with strong gluten network characteristics, 5% soursop residue flour and 95% wheat flour, was statistically equal to a control bread in moisture, pH and total ashes content. Additionally, an affective test was performed in order to identify the acceptability of the bread among potential consumers.

No abstract