Metabolomic Approach for the Authentication of Berry Fruit Juice by Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry Coupled to Chemometrics

Abstract

Berry fruit juice, which is represented by blueberry and cranberry juice, has become increasingly popular due to its reported nutritional and health benefits. However, in markets, adulteration of berry fruit juice with cheaper substitutes is frequent. In the present study, a metabolomic approach for authentication of berry fruit juices by liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was established. The global characterization of the berry fruit metabolome by information dependent acquisition directed LC-MS/MS coupled to a peak mining workflow by isotope pattern matching was reported. Targeted metabolomics analysis of known juice biomarkers, such as flavonoids, anthocyanins, etc. exhibited a good separation of berry fruit juices from adulterant juices. Moreover, untargeted metabolomics analysis was carried out and subjected to chemometrics analysis. Discrimination of blueberry juice, cranberry juice, and its adulterant apple juice and grape juice was obtained by principal component analysis-discriminant analysis. Eighteen characteristic markers discriminating berry fruit juice and its adulterants were selected by comparison of marker abundances in different juice samples. Identification of characteristic markers was accomplished by elemental formula prediction and online database searches based on accurate MS information. These results suggested that the combination of untargeted and targeted metabolomics approach has great potential for authentication of berry fruit juice.

The Role of Liquid Chromatography-Mass Spectrometry in Food Integrity and Authenticity

“…As can be seen in Table 2, and in line with previously commented targeted LC-MS and LC-MS/MS methodologies, polyphenols are ubiquitously used as biomarkers in targeted LC-HRMS approaches [15][16][17][18][19], whether considering a specific polyphenolic class or a wider selection. However, polyphenols are not always the best choice to solve the analytical problem even when plant-related food products are addressed, and therefore some other compounds can be employed.…”

“…Regarding the chromatographic separation, almost all the works described in the literature propose the use of C18 stationary phase columns [15][16][17][18][19]21]. In fact, Guijarro-Díez et al [15] tested and compared a C18 and a cyano column, both having the same size and particle diameter, for the chromatographic separation of five kaempferol derivatives and geniposide in the analysis of saffron samples, obtaining a better resolution and peak efficiency when using the C18 column.…”

“…As shown in Table 2, H-ESI has been established as the most common option for the ionization step whether positive mode [20], negative mode [15,16,18,19,21] or both [17] are employed. In order to ensure a good ionization of the targeted compounds (avoiding in-source fragmentation), Guijarro-Díez et al [15] studied both positive and negative ionization modes, as well as different mobile phase compositions, looking for the highest signal-to-noise (S/N) ratio in the determination of kaempferol derivatives in saffron samples.…”

“…Among all the range of mass analyzers, TOF and Orbitrap-based mass spectrometer technologies are usually employed in these type of targeted studies, especially hybrid instruments such as quadrupole-time-of-flight (Q-TOF) [15][16][17]21], quadrupole-Orbitrap (Q-Orbitrap) [19], and linear trap quadrupole-Orbitrap (LTQ-Orbitrap) [18] configurations. The main advantage of these types of instruments in front of single HRMS analyzers is the possibility to make data dependent MS/MS experiments.…”

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

“…Caldeira et al applied PLS-DA on GC×GC-MS data of breath volatiles to differentiate between asthmatic and non-asthmatic children [153]. Zhang et al applied OPLS-DA to verify the authenticity of fruit juices based on a set of 43 metabolites identified by LC-MS/MS [154]. A similar approach was applied by Yuswan et al, who applied OPLS-DA to LC-MS/MS proteomics data to discriminate between meat samples originating from different organisms [155].…”