The changes in molecular masses of pectin in 0.5% pectin-pectinesterase (PE) mixtures (2 units/mL) incubated at various temperatures, pH values, and NaCl levels for 30 min were observed by a Toyopearl TSK HW-65 (F) gel permeation chromatography. The molecular mass of pectin was remarkably increased from 103 to 266 kDa when the incubation temperature of pectin-tomato PE was increased from 25 to 45 degrees C. A further increase in molecular mass was observed when a pectin-citrus PE mixture was incubated at 65 degrees C. The values of pH and NaCl levels were also crucial to the transacylation activity of PEs. Reaction at pH 7.5 with tomato PE and citrus PE remarkably expanded the molecular mass of pectin to 410 and 670 kDa, respectively. The NaCl level of 0.3-0.5 and 0.3 M was favorable for the transacylation reaction of tomato PE and citrus PE, respectively. Only high methoxylpectin was the suitable substrate for PE to conduct the transacylation reaction.
A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.