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The photocatalytic decomposition of ethylene in a spiral-type reactor was investigated to develop a method for preserving the quality of agricultural products. Ethylene was injected or discharged from an apple into a 0.016 m3 closed container and was photocatalytically decomposed in the reactor at 25 °C. The results indicated that ethylene was decomposed and became undetectable (the amount of ethylene was lower than the detection limit of 40 ppb), which is useful for preserving the quality of agricultural products over long periods of time. For the demonstration experiment, we placed an apple, a persimmon, a cucumber, a cabos (Citrus sphaerocarpa), and a banana in a 0.117 m3 closed container at 25 °C. When the air inside the container was not photocatalytically treated, the concentration of ethylene increased up to 2.7 ppm. This lowered the quality of the persimmon, cucumber, and apple significantly. In contrast, when the air inside the container was photocatalytically treated, they remained fresh for at least nine days. Methane was simultaneously generated as a byproduct. It was hypothesized that methane was the product of the photocatalytic decomposition of some unknown chemical compound released from the analyzed agricultural products; however, we were unable to identify the chemical compound. Computer simulations using experimentally determined parameter values suggested that the decomposition of ethylene begins before or as soon as agricultural products are stored in closed rooms, such as truck containers. In conclusion, the photocatalytic decomposition of ethylene using a spiral-type reactor is effective for preserving the quality of agricultural products.

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg-Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.