Speaker: Ding Jianfen , Senior Statistical Research Engineer, JMP US R&D Headquarters
Topic: Application of JMP® multivariate analysis in chemical, daily necessities and food companies
summary:
Any company's products will enter the consumer market. Compared with other companies, chemical, daily necessities and food companies have a more direct and intimate relationship with consumers. In order to make their products gain a foothold in the market and win the favor of consumers, these companies will conduct a large number of consumer preference studies, including many well-known global companies. These studies usually generate a large amount of sensory data. These sensory data with color, fragrance and taste are not only large in quantity but also of different types. In order to better explore and analyze this type of data, JMP has been increasing the research and development of effective multivariate data analysis methods for this type of data year by year. In this discussion, we will introduce how to apply analysis of variance (ANOVA), K-means clustering, principal component analysis (PCA), partial least squares (PLS), multifactor analysis (MFA), multiple correspondence analysis (MCA) and text analyzer to analyze sensory and consumer preference data, emphasizing how these methods work, how each method should be interpreted, and their inherent interrelationships. By describing the best approaches to sensory and consumer preference questions, we hope that this discussion will provide analysts with an understanding of multivariate approaches to this type of data so that every analyst will be able to effectively use the multivariate tools provided by JMP when faced with sensory and consumer research data in the future.
