Prediction and Process Optimization of Bioscouring of Organic Cotton Fabrics through Specific Mixed Enzymatic System using Artificial Neural Network (ANN)

Abstract

The objective is to develop an improved enzymatic cotton scouring process on the basis of a fast enzyme reaction to efficiently remove pectin and wax compounds from an organic cotton fabric by using a specific mixed enzymatic system. An attempt has been made to study the pectinolytic activity on the organic cotton fabric using four selective enzymes—alkaline pectinase, protease, lipase, and cellulase—and three process parameters—enzyme concentration, temperature, and reaction time. These process parameters are selected based on an artificial neural network (ANN) using the MATLAB 7.0 software, and the output of the experiment was described in terms of fabric physical properties—weight loss, water absorbency, wetting area, whiteness index, yellowness index, and brightness index. The bioscoured organic cotton fabric was tested for wax content and pectin degradation rate and the results were optimized with minimum error. The test results were analyzed to predict the optimum process parameters to achieve the required bioscouring fabric properties and the required level of pectin degradation rate, and were compared with the results of the actual trials. The performance of the specific mixed enzymatic system during bioscouring was assessed using ruthenium red dye test and FTIR (Fourier transform infrared spectroscopy) to confirm the degradation of pectin in the bioscoured organic cotton fabrics.