DEVELOPMENT AND CHARACTERIZATION OF ORO-DISPERSIBLE TABLETS OF METFORMIN HYDROCHLORIDE USING CAJANUS CAJAN STARCH AS A NATURAL SUPERDISINTEGRANT

Objective: The aim of the research work was to explore the use of Cajanus cajan (Pigeon pea) polysaccharide as a superdisintegrant. The novel superdisintegrant has been evaluated for its action by incorporating it into orodispersible tablets of Metformin Hydrochloride. Methods: Cajanus cajan starch was extracted from its seeds and superdisintegrant was developed by microwave modification of the extract. Various characterization tests such as gelatinization temperature, water absorption index, pH, and viscosity were used to identify the microwave-modified polysaccharide. The orodispersible tablets were made using a direct compression process employing varying concentrations of modified Cajanus cajan starch. Prepared tablets were tested for several pre and post-compression parameters and compared with a well-established synthetic superdisintegrant, sodium starch glycolate. The stability studies were conducted on an optimized formulation. Results: Fourier transform infrared spectroscopy study showed that the drug had no interactions with the microwave-modified Cajanus cajan starch. SEM confirmed that Cajanus cajan starch granules exhibited intact granular structure in oval shapes and smooth surfaces. After microwave modification, the Cajanus cajan starch component lost its granular structure, which further led to the generation of surface pores and internal channels, causing overall swelling responsible for superdisintegrant activity. The optimized formulation (ODF5) containing 15 % modified Cajanus cajan starch performed better in terms of wetting time (22.21 s), disintegration time (53.3 s), and in vitro drug release (92%), as compared to formulation prepared by synthetic superdisintegrant (ODF1). Conclusion: The present investigation concluded that modified Cajanus cajan starch has good potential as a superdisintegrant for formulating oro-dispersible tablets. Furthermore, modified Cajanus cajan starch is inexpensive, non-toxic and compatible in comparison with available synthetic superdisintegrants

Object Detection in Indian Food Platters using Transfer Learning with YOLOv4

Object detection is a well-known problem in computer vision. Despite this, its usage and pervasiveness in the traditional Indian food dishes has been limited. Particularly, recognizing Indian food dishes present in a single photo is challenging due to three reasons: 1. Lack of annotated Indian food datasets 2. Non-distinct boundaries between the dishes 3. High intra-class variation. We solve these issues by providing a comprehensively labelled Indian food dataset- IndianFood10, which contains 10 food classes that appear frequently in a staple Indian meal and using transfer learning with YOLOv4 object detector model. Our model is able to achieve an overall mAP score of 91.8% and f1-score of 0.90 for our 10 class dataset. We also provide an extension of our 10 class dataset- IndianFood20, which contains 10 more traditional Indian food classes.Comment: 6 pages, 7 figures, 38th IEEE International Conference on Data Engineering, 2022, DECOR Worksho