Targeted Metabolomics With a Chemometric Study of Oxygenated Heterocyclic Aglycones as a Tool for Preliminary Authenticity Assessment of Orange and Grapefruit Juices

Front Nutr. 2022 May 23:9:897982. doi: 10.3389/fnut.2022.897982. eCollection 2022.

Abstract

Profiles of citrus juice oxygenated heterocyclic aglycones (OHAs), which are notable marker secondary metabolites, were used to assess the authenticity of sweet orange and grapefruit juices in situations where mandarin and pomelo juices might be adulterants. Thirty-nine known OHAs, including 10 methoxyflavones, 13 coumarins, and 16 furanocoumarins, as well as 13 tentatively screened OHAs, were analyzed in orange, mandarin, grapefruit and pomelo juices using our newly developed high-resolution HPLC-UV and fluorescence detection method. Quantitative OHA profiles from 158 pure juice samples were obtained to establish a purity discriminant model using an omics strategy. Reduction of OHA variables showed that three important methoxyflavones, i.e. isosinensetin, tangeretin and sinensetin provided the best discrimination ability between sweet orange and mandarin juices. There are two subtypes of pomelos, Shatianyou Group and Wendan Group, of which juices should be separately compared to grapefruit juice. Five OHAs, namely meranzin, 3,5,6,7,8,3',4'-heptamethoxyflavone, osthole, 6',7'-epoxybergamottin, and bergamottin were found to discriminate Shatianyou Group of pomelo juice from grapefruit juice; whereas three OHAs, namely bergaptol, isomeranzin, and 6',7'-dihydroxybergamottin were able to discriminate Wendan Group of pomelo juice from grapefruit juice. The established partial least squares discriminant analysis (PLS-DA) models were capable of detecting as little as 10% mandarin juice in sweet orange juice and 10% pomelo juice in grapefruit juice, allowing for fast prescreening of excess addition with good reliability (root mean square error of prediction, RMSEP < 5%).

Keywords: adulteration; citrus; labeling; oxygenated heterocyclic compounds; partial least squares discriminant analysis; profiling; variable reduction.