The Physico-chemical Characteristics of Yeast Fermentation of two Mango (Mangifera indica Linn) Varieties.

Efficient methods of post harvest handling, preservation and value addition are critical for minimizing high losses in the post harvest chain of fruits.  The goal of this study was to address this problem by employing yeast fermentation technology to produce a more stable, value added product from mangoes. The design of the study involved determination of the fermentative capabilities of a selected yeast strain on the quality characteristics of mango wine obtained from two selected mango cultivars (improved and wild) with and without peels. The response variables monitored in the must and wine included total soluble solids (TSS), pH and total acidity (TA), microbial populations (aerophilic mesophiles, yeasts and Acetic acid Bacteria), and alcohol content. Volatile compounds development was also monitored using GC-FID procedures. Descriptive and hedonic sensory evaluations were carried out on the mango wine obtained from all treatments. The effects of mango peels in must fermentation characteristics compared well with those of must fermented without peels. However, the wines made using peeled mangoes were far more preferred by consumers than wine made using mangoes with peels. Five (5) major classes of aromatic volatiles were identified in all must and wine samples. Acetaldehyde and ethyl caprylate were present in all treatments, followed by isobutyraldehyde and 2, 3 Butanedione. Some volatiles identified appeared to be mango cultivar specific (Benzaldehyde and 1-methyl-2-pyrrolidone) while other volatiles appeared to be unique to the yeast strain employed (Ethyl butyrate). Keywords: mango, peels, yeast, must, wine, volatiles, alcoho

Fermentation Capacity of Yeasts Using Mango (Mangifera indica Linn.) as Substrate

The goal of this study was to address the problem of large post harvest losses of mangoes by employing yeast fermentation technology to produce a more stable, value-added product in this case fruit wine. The design of the study involved determination of the fermentative capabilities of four commercial yeast types on musts obtained from an improved (Keitt) mango cultivar that is popularly cultivated in some parts of Ghana for export. The characteristics of the mango musts that were monitored included total soluble solids (TSS), pH and total acidity (TA), microbial populations (aerophilic mesophiles, yeasts and Acetic acid Bacteria), alcohol content and colour over the course of the fermentations. Descriptive and hedonic sensory evaluation was carried out on the ferments obtained from all treatments. Results showed that two of the yeast types namely; Red Star Pasteur and Red Star Montrachet displayed superior fermentation characteristics and produced mango wines that were acceptable by both descriptive and affective sensory panels. Keywords: mango, yeast, fermentation, alcohol, wine, must, soluble solids, acidity

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

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