Multivariate statistics is a tool for examining the relationship of multiple variables simultaneously. Principal component analysis (PCA) is an unsupervised multivariate analysis technique that simplifies the complexity of data by transforming them in a few dimensions showing their trends and correlations. Interests in XLSTAT as statistical software program of choice for routine multivariate statistics has been growing due in part to its compatibility with Microsoft Excel data format. As a case of study, multivariate analysis is used to study the effects of unfiltered beer-based marination on the volatile terpenes and thiols, and sensory attributes of grilled ruminant meats. PCA was conducted to determine the correlations between the abundances of volatile terpenes and thiols and sensory attribute scores in marinated grilled meats, as well as to analyze if there was any clustering based on the type of meat and marination treatments employed.•XLSTAT PCA output successfully reduced the number of variables into 2 components that explained 90.47% of the total variation of the data set.•PCA clustered marinated and unmarinated meats based on the presence and abundances of volatile terpenes, thiols and consumer sensory attribute scores.•PCA could be applied to explore relationships between volatile compounds and sensory attributes in different food systems.
Keywords: Grilled ruminant meat; Principal component analysis; SPME-GC/MS; Sensory analysis; Unfiltered beer-based marinades; Volatile metabolites.
© 2020 The Authors. Published by Elsevier B.V.