A supervised machine learning approach for the prediction of antioxidant activities of Amaranthus viridis seed.

This research aimed at modelling and predicting the antioxidant activities of Amaranthus viridis seed extract using four (4) data-driven models. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest Neighbour (k-NN), and Decision Tree (DT) were used as modelling algorithms for the construction of a non-linear empirical model to predict the antioxidant properties of Amaranthus viridis seed extract. Datasets for the modelling operation were obtained from a Box Behnken design while the hyperparameters of the ANN, SVM, k-NN and DT were determined using a 10-fold cross-validation technique. Among the Machine Learning algorithms, DT was observed to exhibit excellent performance and outperformed other Machine Learning algorithms in predicting the antioxidant activities of the seed extract, with a sensitivity of 0.867, precision of 0.928, area under the curve of 0.979, root mean square error of 0.184 and correlation coefficient of 0.9878. It was closely followed by ANN which was used to analyze and explain in detail the effect of the independent variables on the antioxidant activities of the seed extracts. This result affirmed the suitability of DT in predicting the antioxidant activities of Amaranthus viridis.

Understanding how different modification processes affect the physiochemical, functional, thermal, morphological structures and digestibility of cardaba banana starch.

In this study, starch was isolated from cardaba banana starch and was subjected to modification by heat-moisture treatment, citric acid, octenyl succinic anhydride, and sodium hexametaphosphate. Both the native and modified cardaba banana starches were examined for chemical, functional, pasting, thermal, morphological, structural, and antioxidant properties, as well as in vitro starch digestibility. Modification significantly influenced the properties of the cardaba banana starch. Cross-linking treatment improved the water, oil absorption, alkaline hydration capacity, swelling power, solubility and paste clarity of the starch. The final viscosity of the banana starch paste was increased alongside succinic anhydride modification which in turn enhanced the suitability of the starch in the production of high viscous products. Both FTIR spectra and X-ray diffractograms confirmed the starch had a C-type starch which was not affected by modification. Modification led to a decrease in relative crystallinity of the starch with succinylation having the maximum effect. The starch fractions; both SDS and RS significantly increased due to modification while the hydrolysis and glycemic index of the starch were significantly decreased by chemical modification. In conclusion, both physical and chemical modification of cardaba banana starch produced a starch that can serve as functional food or functional food ingredients.