Abstract: This study explores three methods to enhance the quality of Ghanaian garlic before drying: basic ultrasound [sonication (US)], ultrasound with a soaking solution [osmosonication (US + OD)], and vacuum with ultrasound and soaking [vacuum-assisted osmosonication (V + US + OD)]. We looked at how these methods affect garlic’s antioxidants, phenolic and flavonoid content, enzyme activity, rehydration, drying time, energy use, and chemical makeup. The vacuum with ultrasound and soaking method was the best, improving most quality measures, shortening drying time, and saving energy. This method also preserved the garlic’s chemical integrity and had the highest allicin content. Overall, the results ranked the methods as V + US + OD > US + OD > US.…View details

Abstract: This study compared how well three different types of waste (sludge) produce methane gas at three very high temperatures (60°C, 65°C, and 70°C). We also looked at their performance at lower temperatures (37°C and 55°C). Using German guidelines, we measured the daily amount of methane produced. We found that cow manure at 65°C was the best, giving the most methane and breaking down waste the most effectively. On the other hand, the performance was the worst at 70°C for a different type of sludge. For large-scale waste processing systems, cow manure at 65°C is recommended.…View details

Abstract: Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.…View details

Abstract: Tiger nut milk (TNM) faces challenges with its stability (i.e. uneven distribution of suspended particles such as proteins, fats, and other solids), affecting consumer satisfaction, especially in regions where tiger nuts are grown. This study aimed to improve TNM stability and assess its impact on physical properties and consumer preferences. By adding 3 g/100 g sodium caseinate (“milk protein”) and 0.1 g/100 g xanthan gum (“thickening agent” or “food thickener”) to TNM, its ability to be stable (and not separate), nutritional quality, and the satisfaction of customers was increased significantly.…View details

Abstract: This study explores how farming practices that adapt to climate change impact food security in Ghana’s Central Region. It finds that most farmers are using low to moderately sustainable practices, which affects their food security. While many households experience hunger, those who adopt more sustainable farming practices tend to have better food security. The study suggests that improving the sustainability of farming can help protect against the effects of climate change and improve food availability for farm households.…View details