Application of data science to the aroma analytical results of beverages commercially available

Abstract

Applying the statistical methods on the food analysis data plays important role in the field of food safety, food development, research, and quality control, etc. In this paper, an application example by data science in food analysis using the aroma component data of commercial canned coffee obtained by analysis was introduced. The aroma components of 6 types of fragrance-free products and 6 types of fragrance-added products of canned coffee were analyzed using headspace GC/MS. Analytical data of aroma components contained in coffee was statistically analyzed by R program. Four hundred ninety aroma compounds were extracted by the estimation of GC/MS library search (NIST MS library). Principal component analysis, cluster analysis, False Discovery Rate (FDR) test, etc. were applied to the estimated compounds, and they were classified into two groups, fragrance-added and fragrancefree coffee. Statistical methods are one of the important tools in food analysis as demonstrated by this article, and analytical ability is especially useful for food research and food development.