Sensory profiling and consumer evaluation of solar dried tomato powder in Ghana.

BACKGROUND: Tomato (Lycopersicon esculentum) is an important vegetable in Ghana, with high post-harvest losses due to absence of facilities to store, process and extend the shelf life of fresh tomatoes. Solar drying has been proven to be a more efficient and low-cost method of enhancing quality and adding value to tomato and other vegetables. However, there are concerns about sensory appeal of the dried products. In this study a natural mixed mode solar dryer was developed to dry pre-treated fresh Roma tomatoes. The sensory characteristics of the solar dried product was profiled and compared to the sensory properties of similar commercial dried tomato on the market using quantitative descriptive analysis methodology. Consumer testing using qualitative methods provided consumer feedback on the appeal of the solar dried tomatoes. RESULTS: Results showed that the solar dried tomato powder had a stronger tomato aroma intensity (scoring 127 on a 150 mm intensity scale) than the market products (0.7 on the same 150 mm intensity scale). When reconstituted, the solar dried tomato powder had a coarse appearance and texture, and a strong cooked tomato aroma compared to tomato paste. Consumers indicated that tomato powder was used as a thickener because it was highly viscous and when reconstituted imparted a desirable cooked tomato aroma and flavor. CONCLUSION: Considering the positions of the tomato products in the sensory space, although tomato powder had its own unique sensory properties, it also exhibited some sensory characteristics found in tomato paste. From this study, high-quality tomato powder has great potential for commercialization. © 2021 Society of Chemical Industry.

Pesticides residues in leafy green vegetables and irrigation waters in Accra, Ghana.

Pesticides are used in vegetable farming to control pests and diseases, reduce crop losses and improve yield. The study examined pesticide residues in irrigation waters and leafy green vegetables grown in some farming areas in Accra, Ghana. Three types of irrigation water sources (n = 23) and two exotic and four indigenous Ghanaian leafy vegetables (n = 34) from 10 farming areas in Accra, Ghana were collected and examined for 15 organochlorines, 13 organophosphorus and 9 synthetic pyrethroids pesticide residues using the modified QuEChERS procedure. Pesticide residues were detected on 50% (17/34) of the leafy vegetable and 52% (12/23) of the irrigation water samples analysed. Chlorpyrifos and deltamethrin were the most detected pesticide residues in the vegetables and irrigation water. About 26.5% of the vegetables contained pesticide residues exceeding the EU maximum residue limits, so vegetable farmers should be encouraged to comply with appropriate measures on pesticide use to enhance food safety.

The effect of thermal and ultrasonic treatment on amino acid composition, radical scavenging and reducing potential of hydrolysates obtained from simulated gastrointestinal digestion of cowpea proteins.

The effect of thermal and ultrasonic treatment of cowpea proteins (CP) on amino acid composition, radical scavenging and reducing potential of hydrolysates (CPH) obtained from in vitro simulated gastrointestinal digestion of CP was evaluated. Hydrolysis of native and treated CP with gastrointestinal pepsin and pancreatin yielded CPH that displayed antioxidant activities based on oxygen radical scavenging capacity (ORAC), ferric reducing antioxidant power (FRAP) and superoxide radical scavenging activity (SRSA). CPH derived from the treated CP yielded higher ORAC values than CPH from untreated proteins. However, lower significant FRAP and SRSA values were observed for these samples compared to untreated CPH (p < 0.05). Amino acid analysis indicated that CP processing decreased total sulphur-containing amino acids in the hydrolysates, particularly cysteine. The amount of cysteine appeared to be positively related to FRAP and SRSA values of CPH samples, but not ORAC. The results indicated that thermal and ultrasonic processing of CP can reduce the radical scavenging and reducing potential of the enzymatic hydrolysates possibly due to the decreased amounts of cysteine. Since the hydrolysates were generated with gastrointestinal enzymes, it is possible that the resulting compounds are produced to exert some health functions during normal consumption of cowpea.

Effect of Peanut Paste-based Ready-to-use School Meals With and Without Milk on Fluid Cognition in Northern Ghana: A Randomized Controlled Trial.

BACKGROUND: Few studies have investigated the role of school feeding in low- and middle-income countries as a means of improving childhood cognition. Peanut/milk ready-to-use food (PM-RUF) or cowpea offers an affordable, scalable option that might improve cognition. OBJECTIVES: To determine whether micronutrient-fortified PM-RUF or peanut/cowpea ready-to-use food (PC-RUF) would improve fluid cognition as assessed by 4 tests from the National Institutes of Health Toolbox Cognitive Battery when compared with a micronutrient-fortified millet porridge (FP) after a year of school feeding. METHODS: An individually randomly assigned, investigator-blinded, controlled clinical trial was conducted at 6 schools in Mion District in rural northern Ghana. Eight hundred seventy-one school children aged 5-12 y were randomly assigned and allocated to receive PM-RUF (n = 282), PC-RUF (n = 292), or FP (n = 297), each providing ∼400 kcal/d. The primary outcomes were 4 fluid cognition test scores: Dimensional Change Card Sort test, Flanker Inhibitory Control and Attention test, Pattern Comparison Processing Speed test, and a modified List Sorting Working Memory test. Secondary outcomes included a composite median ranking of the 4 primary outcomes and anthropometry changes. RESULTS: Among the 871 participants (median age, 8.8 y; 47% female), 795 (91%) completed endline cognitive testing. Median attendance rates exceeded 87% in all groups. PM-RUF group demonstrated better fluid cognition on the Dimensional Change Card Sort test [odds ratio (OR): 1.5; 95% CI: 1.1, 2.0; P = 0.016] and Pattern Comparison Processing Speed test (OR: 1.4; 95% CI: 1.0, 1.9; P = 0.026) than FP, whereas there were no significant differences on Flanker Inhibitory Control and Attention or List Sorting Working Memory tests. PC-RUF group demonstrated no improvement over FP on any cognitive tests. PM-RUF group had superior fluid cognition composite median rankings (OR: 1.5; 95% CI: 1.1, 2.0; P = 0.007). CONCLUSIONS: Among rural Ghanaian children aged 5-12 y, PM-RUF compared with FP resulted in superior fluid cognition. This trial was registered at clinicaltrials.gov as NCT04349007.

Chicken Feather Protein Dispersant for Effective Crude Oil Dispersion in the Marine Environment.

Various studies report that aside from the adverse impact of the crude oil on the marine environment, there is the likelihood that chemical dispersants used on the surface of water as oil-treating agents themselves possess a degree of toxicity, which have additional effects on the environment. To eliminate the subject of toxicity, there exist several materials in nature that have the ability to form good emulsions, and such products include protein molecules. In this study, chicken feathers which are known to contain ≥90% protein were used to formulate a novel dispersant to disperse crude oil in seawater (35 ppt). Protein from chicken feathers was extracted and synthesized into the chicken feather protein (CFP) dispersant using deionized water as a solvent. Emulsions formed from CFP-synthesized dispersants were stable over a considerably long period of time, whereas the droplet sizes of the emulsion formed were on the average very small in diameter, making droplet coalescence very slow. The CFP dispersants exhibited moderate surface and interfacial activity at normal seawater salinity. Using the US EPA's baffled flask test, at 800 and 1000 mg/ml CFP surfactant-to-oil ratios, dispersion effectiveness values of 56.92 and 68.64 vol % were obtained, respectively, which show that CFP has a great potential in crude oil dispersion. Moreover, the acute toxicity test performed on Nile tilapia showed that CFP was practically nontoxic with an LC50 value of more than 100 mg/L after 96 h of exposure. The results obtained showed that the CFP dispersant is environmentally friendly.

Drying Characteristics and Microbiological Quality Assessment of Solar-Dried Tomato.

Tomato (Lycopersicon esculentum) is an important vegetable used in cooking most local foods in Ghana. At the peak season of harvesting, high loses are incurred because of the absence of tomato processing facilities to store, process, and extend the shelf life of fresh tomatoes. Solar drying has been proven to be a more efficient and low-cost method of enhancing quality and adding value to tomato and other vegetables. However, there are concerns about the functionality and quality of the dried products by consumers due to the methods of drying used. In this study, a passive mixed-mode solar dryer suitable for drying tomato was adapted and used to investigate the dehydration characteristics and microbiological quality of the dried tomato. The efficiency of a passive solar dryer was evaluated and used in the processing of fresh tomato to powder. The processing involved the pretreatment of 6 mm slices of fresh Roma variety of tomato by dipping in potassium metabisulfite solution and ascorbic acid solution. The moisture content, moisture ratio, and dehydration rate of solar-dried tomato were assessed. The 24 h dryer efficiency of 24.2% facilitated the drying process of tomato (final moisture content of 12-14%). Aerobic mesophile counts were lower in solar-dried tomato pretreated with potassium metabisulfite (3.90 CFU/g) compared with sun-dried samples (4.85 CFU/g). Solar-dried tomato powder is safer for consumption compared with open sun-dried tomato samples.

Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques.

Obesity is a major global health challenge and a risk factor for the leading causes of death, including heart disease, stroke, diabetes, and several types of cancer. Attempts to manage and regulate obesity have led to the implementation of various dietary regulatory initiatives to provide information on the calorie contents of meals. Although knowledge of the calorie content is useful for meal planning, it is not sufficient as other factors, including health status (diabetes, hypertension, etc.) and level of physical activity, are essential in the decision process for obesity management. In this work, we present an artificial intelligence- (AI-) based application that is driven by a genetic algorithm (GA) as a potential tool for tracking a user's energy balance and predicting possible calorie intake required to meet daily calorie needs for obesity management. The algorithm takes the users' input information on desired foods which are selected from a database and extracted records of users on cholesterol level, diabetes status, and level of physical activity, to predict possible meals required to meet the users need. The micro- and macronutrients of food content are used for the computation and prediction of the potential foods required to meet the daily calorie needs. The functionality and performance of the model were tested using a sample of 30 volunteers from the University of Ghana. Results revealed that the model was able to predict both glycemic and non-glycemic foods based on the condition of the user as well as the macro- and micronutrients requirements. Moreover, the system is able to adequately track the progress of the user's weight loss over time, daily nutritional needs, daily calorie intake, and predictions of meals that must be taken to avoid compromising their health. The proposed system can serve as a useful resource for individuals, dieticians, and other health management personnel for managing obesity, patients, and for training students in fields of dietetics and consumer science.

Design and Development of Diabetes Management System Using Machine Learning.

This paper describes the design and implementation of a software system to improve the management of diabetes using a machine learning approach and to demonstrate and evaluate its effectiveness in controlling diabetes. The proposed approach for this management system handles the various factors that affect the health of people with diabetes by combining multiple artificial intelligence algorithms. The proposed framework factors the diabetes management problem into subgoals: building a Tensorflow neural network model for food classification; thus, it allows users to upload an image to determine if a meal is recommended for consumption; implementing K-Nearest Neighbour (KNN) algorithm to recommend meals; using cognitive sciences to build a diabetes question and answer chatbot; tracking user activity, user geolocation, and generating pdfs of logged blood sugar readings. The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. The model learned features of the images fed from local Ghanaian dishes with specific nutritional value and essence in managing diabetics and provided accurate image classification with given labels and corresponding accuracy. The model achieved specified goals by predicting with high accuracy, labels of new images. The food recognition and classification model achieved over 95% accuracy levels for specific calorie intakes. The performance of the meal recommender model and question and answer chatbot was tested with a designed cross-platform user-friendly interface using Cordova and Ionic Frameworks for software development for both mobile and web applications. The system recommended meals to meet the calorific needs of users successfully using KNN (with k = 5) and answered questions asked in a human-like way. The implemented system would solve the problem of managing activity, dieting recommendations, and medication notification of diabetics.

Consumer knowledge, preference, and perceived quality of dried tomato products in Ghana.

Postharvest losses (PHL) are incurred in the tomato value chain in Ghana and solar drying of tomato is a promising technology for reducing the loss. However, there are concerns on the usage, functionality and sensory appeal of the dried products to consumers, compounded with the lack of information and research on dried tomato processing in Ghana. A survey was carried out by administering semistructured questionnaires to 395 randomly selected and willing respondents in the Accra Metropolis. Information was obtained on the socioeconomic profile, consumption pattern, knowledge, and acceptance of tomato processing technologies and assessment of quality attributes important to consumers. Most consumers (74%) preferred tomato powder that is conveniently packaged to retain the characteristic intense taste and the flavor using Friedman's rank mean procedure. The study indicated that consumers were more concerned about good manufacturing practices during the production of solar-dried tomato (48.8%) rather than the quality attributes (8.6%). These findings indicate the need for safe solar drying procedures in order to increase consumer acceptability of solar-dried tomato products in Ghana.

Soybean-Enriched Snacks Based on African Rice.

Snacks were produced by extruding blends of partially-defatted soybean flour with flours from milled or parboiled African-grown rice. The interplay between composition and processing in producing snacks with a satisfactory sensory profile was addressed by e-sensing, and by molecular and rheological approaches. Soybean proteins play a main role in defining the properties of the protein network in the products. At the same content in soybean flour, use of parboiled rice flour increases the snack's hardness. Electronic nose and electronic tongue discriminated samples containing a higher amount of soybean flour from those with a lower soybean flour content.