Production and partial characterization of alkaline polygalacturonase secreted by thermophilic Bacillus sp. SMIA-2 under submerged culture using pectin and corn steep liquor

Polygalacturonase production by the thermophilic Bacillus sp. SMIA-2 cultivated in liquid cultures containing 0.5% (w/v) apple pectin and supplemented with 0.3% (w/v) corn steep liquor, reached its maximum after 36 hours with levels of 39 U.mL-1. The increase in apple pectin and corn steep liquor concentrations in the medium from 0.5 and 0.3%, respectively, to 0.65%, markedly affected the production of polygalacturonase, whose activity increased four times, reaching a maximum of 150.3 U.mL-1. Studies on polygalacturonase characterization revealed that the optimum temperature of this enzyme was between 60-70 °C. Thermostability profile indicated that the enzyme retained about 82 and 63% of its activity at 60 and 70 °C, respectively, after 2 hours of incubation. The optimum pH of the enzyme was found to be 10.0. After incubation of crude enzyme solution at room temperature for 2 hours at pH 8.0, a decrease of about 29% on its original activity was observed. At pH 10.0, the decrease was 25%.

Automated sugar analysis

Abstract Sugarcane monosaccharides are reducing sugars, and classical analytical methodologies (Lane-Eynon, Benedict, complexometric-EDTA, Luff-Schoorl, Musson-Walker, Somogyi-Nelson) are based on reducing copper ions in alkaline solutions. In Brazil, certain factories use Lane-Eynon, others use the equipment referred to as “REDUTEC”, and additional factories analyze reducing sugars based on a mathematic model. The objective of this paper is to understand the relationship between variations in millivolts, mass and tenors of reducing sugars during the analysis process. Another objective is to generate an automatic model for this process. The work herein uses the equipment referred to as “REDUTEC”, a digital balance, a peristaltic pump, a digital camcorder, math programs and graphics programs. We conclude that the millivolts, mass and tenors of reducing sugars exhibit a good mathematical correlation, and the mathematical model generated was benchmarked to low-concentration reducing sugars (<0.3%). Using the model created herein, reducing sugars analyses can be automated using the new equipment.