Abstract: Hibiscus-based drinks like Bissap are popular in Ghana, but they spoil quickly during processing and storage, limiting their commercial use. This study explores the biochemical changes in bissap-carrot blends during storage and their impact on sensory properties, with the aim of enhancing the commercial viability of bissap-based beverages. Four different formulations were tested: 100% Bissap extract; Bissap + Carrot juice extract; Bissap + Carrot + preservative, Bissap + Blanched Carrot + preservative. These blends were stored in plastic bottles at room temperature for 21 days, and their chemical and sensory properties were measured throughout the storage period. Key metrics such as pH, antioxidant activity, colour stability, and anthocyanin content were monitored, along with sensory attributes like flavour, aroma, and appearance. Results indicated that the addition of carrot juice reduced the natural sourness of bissap and improved pH stability and preserved antioxidant properties, while fresh carrots helped retain the colour pigments of the bissap over time. These findings provide insights for beverage manufacturers looking to extend the shelf life of bissap-based products while maintaining quality and sensory appeal.…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