The antioxidant activities based on the free radical scavenging, reducing power, and bleaching inhibition were investigated for the three commonly used honeys in Malaysia, namely, tualang, gelam, and acacia honey. The antioxidant capacity of the honey samples was correlated with their biochemical constituents such as total phenol, total flavonoid content, and total water-soluble vitamins (vitamin B1, B2, B3, B9, B12, and vitamin C). The total flavonoid content of honey samples was strongly correlated with the three antioxidative processes (0.9910). In contrast, the total water-soluble vitamins was found to be well correlated with the free radical scavenging activity (). Vitamin B3 was likely to be in the highest concentration, which covered for 6980% of the total vitamin content. A number of five phenolic acids, three flavonoids, and two organic acids had also been detected from the honey samples using UPLC-MS/MS, without sugar-removal procedure.

Aqueous and ethanol extracts of different traditional Malaysian plants (Polygonum minus, Andrographis paniculata, Curcuma xanthorrhiza, Momordica charantia and Strobilanthes crispus) were evaluated for their antioxidant properties, total phenolic content and cytotoxic activity. Antioxidant activity was evaluated by using 1,1-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) assays. The results showed that ethanol extracts contain high antioxidant activities compared to aqueous extracts. The findings exhibited a strong correlation between antioxidant activity and the total phenol contents. In addition, all the plant extracts showed non-toxic effects against a normal human lung fibroblast cell line (Hs888Lu). Although traditionally aqueous extracts are used, we determined that ethanol extracts usually achieved better activity in the assays.

Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.