Fluidized bed granulation is a common operation to improve the dispersibility and solubility of powdered food including instant soup, as well as powdered instant beverages. In the fluidized bed granulation process of food powder, fine particles are formed to large granule usually by spraying aqueous solution of polysaccharide as binder. The water added to the powder materials plays an essential role for the growth of granule, however, excessive moisture spoils the quality of granule products and elongate the processing time and successive drying period of the granule. This article summarizes a development process of new fluidized bed granulation technique using steam-water two-phase (SWTP) binder. This article firstly introduces a model to describe moisture content of fluidized bed as a function of time and binder spraying rate for the conventional granulation process using liquid binder. The model was consequently extended for the process using SWTP binder by considering condensation of steam to the powder materials and evaporation of binder droplets. The model developed in these studies was used to determine the binder spraying conditions for each binder to compare the granule growth at equivalent profile of moisture content. It was found that the SWTP binder decreased the amount of water used in the process to 40~84% comparing with conventional liquid binder.

We estimated the quality component values of the commercial Japanese sake Junmai Ginjo by using electronic (e)-nose and e-tongue data. Regression analysis methods were applied to predict the components. Characteristic features of Junmai Ginjo such as acidity, amino acid content, glucose and nine volatile components were used as objective variables. Explanatory variables were the 99 peak data obtained by an e-nose and seven sensor data obtained by an e-tongue. The prediction accuracy by the partial least squares regression method using e-nose and e-tongue data was 7.57 average error% (the ratio of the mean absolute error to the component value range). With the application of other regression analyses (multiple regression analysis, support-vector machine, random forest, gradient boosting), the prediction accuracy was improved for all components except the acidity and amino acid content. With the application of other regression analyses and the addition of the data of seven simplified analyses (Brix, pH, electrical conductivity, OD260, OD280, simplified alcohol content, simplified glucose content), the prediction accuracy was improved for all components. (average error%: 5.04) The analysis conditions (i.e., the regression analysis and the dataset of explanatory variables) for the best score differed depending on the component. Thus, when predicting components by a regression analysis, it is necessary to prepare a plurality of analysis conditions and challenges.

3D printers have become popular in the world, and are expected to be used not only in industrial sectors but also in food sectors these days. Especially, making soft meals such as nursing foods with a 3D printer is useful to improve the satisfaction of eating and to reduce the burden of the caregiver. Therefore, we tried 3D printing of soft foods by using a combination of protein and gelling agent. And the effects of proteins and gelling agents were investigated. The results showed that 3D printing of soft foods can be performed by using a combination of protein and gelling agent and then extruding in a temperature range where the gelling agent does not completely gel and has fluidity after thermal denaturation of protein. Protein contributed to maintaining fluidity and retaining the shape of the discharged line, and gelling agent contributed to retaining the shape of the discharged line and adhering the layers. Furthermore, instead of protein, thermosetting gelling agent and thermoplastic gelling agent having a high gelation temperature also can be used.