Production of New Nano-Bacterial Cellulose with Lactobacillus rhamnosus by Using Whey Waste as Substrate with Optimization Taguchi Method, which has the potential to be used in many biomedical products

Abstract

Whey waste, which has a negative impact on the environment, is an important component with high organic content. The fact that it contains lactose, a fermentable sugar, is a suitable substrate for the formation of natural nano-cellulose. Bacterial nano-cellulose (BNC), a type of natural cellulose polymer synthesized by some microorganisms, has been reported to be a promising natural biomedical material due to its distinctive feature, including its unique fibril nanostructure, high water holding capacity, crystallinity, high chemical purity, fine wet mechanical property. In this study, new BNC production was realized for the first time by using Lactobacillus rhamnosus bacteria and whey as organic substrate. Optimum condition was determined by Taguchi method under the following condition; pH (5-6), organic source concentration (25-100 % g/L), active culture (10-30 % g/L), incubation period (8-12 day). Whereas Taguchi method was highest performed at at pH 5.5, organic source concentration 25 % g/L, active culture 30 % g/L, incubation period 8 days with 5.41 g BNC yield. Effects of organic source concentration found as decisive factor on Lactobacillus rhamnosus BNC yield with 95% confidence interval. Field emission scanning electron microscopy (FESEM), fourier transform infrared spectroscopy (FTIR), differential / thermogravimetric thermal analysis (DTG/TG) were utilised to evaluate the structure and characterization of BNC. BNC production by Lactobacillus rhamnosus, with its biocompatible and biodegradable properties, environmentally friendly and low-cost nanomaterials have been produced with the potential to be used in many biomedical applications such as wound dressing and drug coating material.

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

“…Karimipourfard et al (2024) [46] used the Taguchi method to improve a deep learning model for fast and accurate patient-specific internal dosimetry, achieving results comparable to the gold-standard Monte Carlo but with much faster calculation speed. Bayraktar et al (2024) [47] applied the Taguchi method to optimize the production of bacterial nano-cellulose from whey waste, a sustainable approach yielding biocompatible and biodegradable nanomaterials with potential for biomedical applications. These diverse applications demonstrate the Taguchi method's adaptability and potential to contribute to advancements across various engineering fields.…”