Carotenoids, the most common fat-soluble plant pigments in nature, have attracted considerable attention because of their powerful antioxidant activity and potential to prevent of various diseases, such as cancer and arteriosclerosis. These compounds have numerous geometric isomers owing to E/Z-isomerization at arbitrary sites of the multiple conjugated double bonds. However, in nature, most carotenoids are present in the all-E-configuration. (all-E)-Carotenoids are characterized by low solubility in solvents and high crystallinity. Thus, these characteristics result in the decreased efficiency of carotenoid processing, such as extraction and micronization. On the other hand, Z-isomerization of carotenoids induces change in physicochemical properties. For example, the solubility of carotenoids is markedly improved and they change from a “crystalline state” to an “oily (amorphous) state”. Utilizing these changes in physicochemical properties of carotenoids, we successfully improved the efficiency of carotenoid processing. The objective of this paper is to review the effect of Z-isomerization of carotenoids on the physicochemical properties and its application to carotenoid processing, such as extraction, emulsification, and micronization. Furthermore, aspects of Z-isomerization methods of carotenoids and effects of Z-isomerization of carotenoids on the bioavailability and bioactivity are also included in this review.

Food can be processed by high hydrostatic pressure (HHP). HHP can inactivate microbes, while minimizing loss in food quality such as flavor, color, and nutrition. HHP food processing was first commercialized for producing fruit jams by a Japanese company in 1990, and the technology has been applied in the world to process foods such as meat products, juices, and dips as well as to shuck shellfish and crustaceans, whereas fundamental knowledge has been accumulated by intensive studies. This article reviews HHP-induced quality changes in foods based on our research topics on starch gelatinization, bacterial inactivation, and bacterial injury as fundamentals and pickles and compotes as applications.

Drying is the oldest method for preserving food, and rehydration is one of the simplest methods of cooking. The behavior of water transfer inside foods during the drying and rehydration processes varies depending on the manufacturing and cooking conditions, respectively, and the food quality is strongly affected by the behavior. Existing manufacturing and cooking processes, which have relied on great experience and build through trial and error, cannot quickly and accurately respond to the diverse demands of recent years, such as health consciousness and insatiable pursuit for palatability. Therefore, the theoretical study that enables rational food production has been strongly demanded. However, the phenomena and mechanisms of water transfer and their effects on the quality are not well understood. Focusing on the pasta with relatively simple composition, we have elucidated the phenomenon and its mechanism of water transfer occurring during drying and rehydration processes, and also investigated the effect of the water transfer on pasta quality. This review provides the overview of our findings.

We investigated the contributions of physicochemical features to a comprehensive evaluation of the Japanese sake known as ‘Junmai Ginjo’ by applying machine learning. We used 173 samples of the commercial Japanese sake. The sensory evaluation was conducted by 35 panelists. The panel conducted the evaluation of each sample using five statements for the comprehensive evaluation of the sample. General analysis, substance-related nucleic acid, volatile components and simplified analyses were measured as physicochemical analyses. We performed regression analyses using a multiple regression analysis (MRA), partial least squares regression (PLS) and machine learning employing a support vector machine (SVM), an artificial neural network (ANN), and random forest (RF). The results of these five analysis methods have demonstrated that machine learning (especially RF) provides comparable or higher prediction accuracy and better fitting than MRA. We also discuss the contribution of each physicochemical feature to the evaluation scores based on the regression coefficients obtained by MRA and the features’ importance obtained in RF. The analysis of the individual scores indicated that ethyl caproate and isoamyl acetate make large contributions to influence the sake evaluation.