Enzymatic hydrolysis and fermentation of ultradispersed wood particles after ultrasonic pretreatment

Background: A study of the correlation between the particle size of lignocellulosic substrates and ultrasound pretreatment on the efficiency of further enzymatic hydrolysis and fermentation to ethanol. Results: Themaximumconcentrations of glucose and, to a lesser extent, di- and trisaccharideswere obtained in a series of experiments with 48-h enzymatic hydrolysis of pine rawmaterials ground at 380–400 rpm for 30min. The highest glucose yield was observed at the end of the hydrolysis with a cellulase dosage of 10 mg of protein (204 ± 21 units CMCase per g of sawdust). The greatest enzymatic hydrolysis efficiency was observed in a sample that combined two-stage grinding at 400 rpm with ultrasonic treatment for 5–10 min at a power of 10 W per kg of sawdust. The glucose yield in this case (35.5 g glucose l−1) increased twofold compared to ground substrate without further preparation. Conclusions: Using a mechanical two-stage grinding of lignocellulosic raw materials with ultrasonication increases the efficiency of subsequent enzymatic hydrolysis and fermentation.

Spectrum-effect relationships between high performance liquid chromatography fingerprints and bioactivities of charred areca nut

Purpose: To investigate the spectrum-effect relationships between high performance liquid chromatography (HPLC) fingerprints and duodenum contractility of charred areca nut (CAN) on rats. Methods: An HPLC method was used to establish the fingerprint of charred areca nut (CAN). The promoting effect on contractility of intestinal smooth was carried out to evaluate the duodenum contractility of CAN in vitro. In addition, the spectrum-effect relationships between HPLC fingerprints and bioactivities of CAN were investigated using multiple linear regression analysis (backward method). Results: Fourteen common peaks were detected and peak 3 (5-Hydroxymethyl-2-furfural, 5-HMF) was selected as the reference peak to calculate the relative retention time of 13 other common peaks. In addition, the equation of spectrum-effect relationships {Y = 3.818 - 1.126X1 + 0.817X2 - 0.045X4 - 0.504X5 + 0.728X6 - 0.056X8 + 1.122X9 - 0.247X13 - 0.978X14 (p < 0.05, R2 = 1)} was established in the present study by the multiple linear regression analysis (backward method). According to the equation, the absolute value of the coefficient before X1, X2, X4, X5, X6, X8, X9, X13, X14 was the coefficient between the component and the parameter. Conclusion: The model presented in this study successfully unraveled the spectrum-effect relationship of CAN, which provides a promising strategy for screening effective constituents of areca nut.