Starch-based biodegradable films amended with nano-starch and tannic acid-coated nano-starch exhibit enhanced mechanical and functional attributes with antimicrobial activity.

Starch-based biofilms are biodegradable, but their application is limited by lower mechanical strength and absence of antimicrobial properties. In this context, the present study attempted to unleash the potential of nanotechnology for synthesizing nano-starch (NS) and tannic acid-coated nano-starch (T-NS) for augmenting the tensile strength and antimicrobial properties of starch-based biofilms. Moreover, this study reports one of the first such attempts to improve the commercial viability of starch extracted from the corms of Amorphophallus paeoniifolius. In this study, NS and T-NS samples were first synthesized by the physical and chemical modification of the native starch (S) molecules. The NS and T-NS samples showed significantly smaller granule size, lower moisture content, and swelling power. Further, amendments with NS and T-NS samples (25 % and 50 %) to the native starch molecules were performed to obtain biofilm samples. The NSB (NS amended) and T-NSB (T-NS amended) biofilms showed comparatively higher tensile strength than SB films (100 % starch-based). The T-NSB showed greater antimicrobial activity against gram-positive and gram-negative bacteria. All the biofilms showed almost complete biodegradation in soil (in 10 days). Therefore, it can be concluded that additives like NS and T-NS can improve starch-based biofilms' mechanical strength and antimicrobial properties with considerable biodegradability.

Optimization of starch extraction from Amorphophallus paeoniifolius corms using response surface methodology (RSM) and artificial neural network (ANN) for improving yield with tenable chemical attributes.

The development of the extraction process for improving the starch yield from unconventional plants is emerging as a topic of interest. In this respect, the present work aimed to optimize the starch extraction from the corms of elephant foot yam (Amorphophallus paeoniifolius) with the help of response surface methodology (RSM) and artificial neural network (ANN). The RSM model performed better than the ANN in predicting the starch yield with higher precision. In this connection, this study for the first time reports the significant improvement of starch yield from A. paeoniifolius (51.76 g/100 g of the corm dry weight). The extracted starch samples based on yield - high (APHS), medium (APMS), and low (APLS) exhibited a variable granule size (7.17-14.14 µm) along with low ash content, moisture content, protein, and free amino acid indicating purity and desirability. The FTIR analysis also confirmed the chemical composition and purity of the starch samples. Moreover, the XRD analysis showed the prevalence of C-type starch (2θ = 14.303°). Based on other physicochemical, biochemical, functional, and pasting properties, the three starch samples showed more or less similar characteristics thereby indicating the sustentation of beneficial attributes of starch molecules irrespective of the variation in extraction parameters.