Food ingredients and materials derived from novel food sources have been increasingly developed using innovative technologies and production processes. Therefore, understanding the diverse regulations and approval procedures in different countries is crucial before importing foreign ingredients; this is especially crucial for importing materials without a prior consumption history and exporting domestically produced food ingredients, for example, in Korea. This review discusses the procedures, from the temporary recognition of food ingredients to their registration in the Korean Food Code, as well as the regulatory frameworks and current status of novel food approval in the European Union (EU), Generally Recognized as Safe (GRAS) recognition by the United States (US) Food and Drug Administration (FDA), and novel food approval by the Food Standards Australia New Zealand (FSANZ).

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.