Comparative Study of the Characterisation and Extraction Techniques of Polyphenolic Compounds from Acacia seyal gum

Abstract Acacia seyal gum is an abundant source of natural polyphenolic compounds (NPPCs) and antioxidant activity with numerous benefits and is often used in cancer treatment. The type of extraction technique can significantly impact the yield and isolation of NPPCs from Acacia seyal gum (ASG). The traditional use of maceration extraction reportedly yields fewer NPPCs. Objectives This study investigates five extraction techniques for NPPCs and ASG antioxidant activity, namely: homogenisation, shaking, ultrasonication, magnetic stirring, and maceration. Materials and Methods The evaluation of the antioxidant activity (AoA) of the extracted NPPCs from ASG used five assays, namely: Total Flavonoids Content (TFC), Folin-Ciocalteu index (FCI), 2,2-Diphenyl-1-Picrylhydrazyl radical scavenging activity (DPPH), Ferric Reducing Antioxidant Power (FRAP), and Cupric Reducing Antioxidant Capacity (CUPRAC). Results To minimise the dataset dimensionality requires Principal Component Analysis. The ultrasonic and maceration techniques were the best techniques to extract NPPCs and examine the AoA of ASG, with a high correlation between the NPPCs and AoA. However, the maceration process was slow (12 h) compared to ultrasonication (1 h). Slow extraction can result in a decline of the NPPCs due to polyphenol oxidase-enzyme and impact productivity. Conclusions These findings provide an essential guide for the choice of extraction techniques for the effective extraction of NPPCs from ASG and other plant materials.

A comparative evaluation of different keyword extraction techniques

Retrieving keywords in a text is attracting researchers for a long time as it forms a base for many natural language applications like information retrieval, text summarization, document categorization etc. A text is a collection of words that represent the theme of the text naturally and to bring the naturalism under certain rules is itself a challenging task. In the present paper, the authors evaluate different spatial distribution based keyword extraction methods available in the literature on three standard scientific texts. The authors choose the first few high-frequency words for evaluation to reduce the complexity as all the methods are somehow based on frequency. The authors find that the methods are not providing good results particularly in the case of the first few retrieved words. Thus, the authors propose a new measure based on frequency, inverse document frequency, variance, and Tsallis entropy. Evaluation of different methods is done on the basis of precision, recall, and F-measure. Results show that the proposed method provides improved results.