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In this study, the retention of vitamin C in fresh broccoli stored at different temperatures (i.e. chiller, room, cooking, and roasting or baking; 5-120°C) was investigated. The thermal stability of vitamin C in broccoli was analysed at 5, 20, 45, 60, 70, 80, 110, and 120°C. The vitamin C content was measured by the indophenol titration method. Vitamin C was affected negatively at all stored temperatures. The degradation of vitamin C was modelled by first-order reaction kinetics and the reaction rate constants were observed as 9.03×10-8 and 5.65×10-3 s-1 when stored at 5°C and 120°C, respectively. The activation energy was estimated as 74.2 kJ/mol within the temperature range used in this study. The lowest decay of vitamin C was observed during the chilling condition. The data on retention of vitamin C in broccoli could be used to determine their stability, when stored as raw, and when heated at different temperatures.

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Background In previous times, infectious diseases affected the quality of human life during home confinement. This study investigated the influence of home confinement during the COVID-19 pandemic outbreak on lifestyle, mental wellbeing, nutritional status, and sleeping pattern. Method: An online multicategory questionnaire was distributed to collect Demographical information and combined the following tools: Food Frequency Questionnaire (FFQ), International Physical Activity Questionnaire (IPAQ), WHO-5 wellbeing score, and Pittsburgh Sleep Quality Index (PSQI). A snowball none-discriminate sampling procedure was followed to collect data from people attending or working at higher institution had covering the period between Mar/00/2020 and Apr/24/2020. A total of 1723 completed responses (917 Males 37.4 ± 13.4 yrs. old and (806 Females 32.2 ± 11.5 yrs. old) were collected. Results Female participants had significantly lower mental health scores as compared to males (53.9% vs 46.1%). on Mental wellbeing score was better among participants with medium and high Physical Activity Levels (p < 0.00). Additionally, mental wellbeing score was significantly improved by dietary quality and sleeping score (p < 0.001). However, physical activity was by far the major determinants of mental health score. Conclusion Factors such as PA, Diet, sleeping patterns were associated with mental wellbeing during COVID-19 confinement among Arab participants.

Rice (Oryza sativa) is one of the most important cereal grains that is popularly consumed globally as a staple food. A number of rice dishes are prepared and consumed in Oman. Representative samples of four different types of cooked rice dishes (White rice, Brown rice, Kabuli rice, and Biryani rice) and two traditional dishes (Arsiya and Harees) were collected from local restaurants. The aim of this study was to evaluate the proximate composition, glycemic index (GI) and glycemic load (GL) of these dishes. The results indicated significant (P<0.05) differences in the proximate composition, GI, and GL values of differently cooked rice dishes as well as for Arsiya and Harees. With the exception of white rice which showed the highest glycemic index value (77.3), all other three rice dishes as well as Arsiya and Harees were within the medium GI category (59.5 to 62.9). Similarly, the white rice showed the highest glycemic load value (20.9), whereas the other three rice dishes were within the medium GL category (10.7 to 16.7). The Arsiya and Harees were within low GL category (5.4 to 6.2). The method of cooking appears to affect the proximate composition, starch gelatinization, release of glucose and glycemic index of these dishes. We are reporting for the first time the GI and GL values of these Omani dishes. The results will help in developing appropriate dietary management strategies in meal planning by using the concept of GI and GL for both the normal and diabetic subjects to reduce their risk of chronic diseases.

Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results: The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%,13.7%,13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions: Overall, ML algorithms seem to be an effective method in early detection and prediction of food insecurity. Future research would benefit from utilizing the proposed model in developing more complex and accurate models aiming to enhance granularity, with the ability to share data, to incorporate wide range of variables, and to make use of automation for effective prevention and intervention programs at the regional and individual levels.