African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

Reliable and user-friendly discrimination of coffee bean integrity and quantification of adulteration in the coffee bean processing value chain would be vital for ensuring consumer trust in quality control and traceability management. In this research, a portable short-wave NIR spectroscopy coupled with chemometric data analysis was employed under different pre-treatments to develop a rapid detection technique. Different pre-processing treatments (multiplicative scatter correction; MSC, standard normal variant; SNV, first derivative; FD) together with multivariate techniques; support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), and random forest (RF) were comparatively assessed using accuracy and correlation coefficient (R) for discrimination and quantification. The results showed that the FD-LDA model had 97.78% and 100 % in both the calibration set and prediction set. In comparison, the SPA-PLS model had R = 0.9711 and 0.9897 in both the calibration set and prediction set. The outcome of this study showed portable short-wave NIR spectroscopic techniques could be used for examining the integrity of coffee.

Drying is one of the traditional means of preserving food. However, various drying methods can influence the nutritional and bioactive constituents of the food product. This study is aimed at evaluating the effect of different drying methods on the proximate composition, physicochemical properties, and minor constituents of palm oil fruit mesocarp. Two varieties of fresh oil palm fruit (dura and tenera) were processed to separate the mesocarp from the other part of the fruit. The fresh fruit mesocarp was divided into five groups and subjected to different drying methods. Proximate and physicochemical characteristics of the oil palm fruit mesocarp were determined. The dried mesocarp had low moisture content (1.49-3.28%), high crude fat content (78.10-90.60%), carbohydrate (4.41-15.12%), crude protein (0.93-3.40%), and ash (0.53-1.15%). The free fatty acid (FFA) (1.06-3.54) and acid value (AV) (2.17 to 8.83 mgKOH/g) were lower because the samples were heated at 100°C for 30 min. The lower pH, FFA, AV, titratable acidity, moisture content, and high antioxidant activity of the oil palm fruit products could be an indication of shelf stability against microbial contamination and rancidity.

Coffee is the most consumed beverage and the second most valuable traded commodity in the world.